JP7445728B2 - rating system - Google Patents

Description

Embodiments relate to evaluation systems.

Wearable devices that are attached to the body have been developed.

Japanese Patent Application Publication No. 2016-103275 Patent No. 4759633

Embodiments provide an evaluation system that can more effectively utilize user information.

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

Embodiments can provide an evaluation system that can use user information more effectively.

FIGS. 1(a) to 1(c) are schematic diagrams illustrating the evaluation system according to the first embodiment. 3 is a flowchart showing an evaluation method of the evaluation system according to the first embodiment. FIG. 2 is a schematic diagram illustrating evaluation results in the evaluation system according to the first embodiment. FIG. 2 is a schematic diagram illustrating an evaluation system according to a second embodiment. FIG. 2 is a schematic diagram illustrating an evaluation system according to a second embodiment. It is a flow chart which shows the evaluation method of the evaluation system concerning a 2nd embodiment. It is a flow chart showing operation in an evaluation system concerning an embodiment. FIG. 1 is a schematic diagram illustrating an evaluation system according to an embodiment. FIGS. 9(a) to 9(c) are schematic diagrams illustrating information in the evaluation system according to the third embodiment. FIGS. 10(a) to 10(c) are schematic diagrams illustrating evaluation result information according to the third embodiment. FIG. 1 is a schematic diagram illustrating insurance according to an embodiment.

Embodiments of the present invention will be described below with reference to the drawings. In the specification of this application and each figure, the same elements as those described above with respect to the existing figures are given the same reference numerals, and detailed explanations are omitted as appropriate.

(First embodiment)
The evaluation system according to the first embodiment will be explained 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.
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 biological information etc. obtained from a plurality of user systems. For example, the evaluation system 110 acquires biometric information of the user while driving. The evaluation system 110 evaluates the user's condition while driving by comparing the biometric information with biometric information previously obtained from a plurality of user systems. Specifically, the evaluation system 110 evaluates the user when the state of the user who is driving at a certain timing is the state of the user who is driving at another timing or the state that is close to the state where the user is driving. Evaluate how much it differs from the state of

This embodiment is not limited to the case where the user is driving. Embodiments are applicable when a user performs a series of tasks that include one or more operations.

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

For example, each of the detectors SN (for example, 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 acquired biological information and behavior information of the user in a memory (not shown) in the first user system 10.

The first user system 10 can communicate with the arithmetic 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 regarding the user's behavior 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 (mobile body). Vehicle 28 is, for example, a car. The second user system 20 includes a detector SN (i-th detector SNi, in this example, seventh detector SN7). The seventh detector SN7 is provided, for example, on the seat 27 of the vehicle 28. For example, the seventh detector SN7 detects biological information of the user 81 sitting on the seat 27. The seventh detector SN7 may image the body (for example, the head such as the face) of the user 81. Information based on the imaging results may be at least part of the information regarding the user 81. In this case, the seventh detector SN7 may be provided in the windshield portion of the vehicle 28.

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 the embodiment, 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 plurality of detectors SN provided in the first user system 10.

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

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

The second user system 20 may include a communication unit 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 via the communication unit 21 in a wired or wireless manner. The control unit transmits the biometric information of the user 81 acquired by the seventh detector SN7 to the calculation system 30 via the communication unit 21. Server 22 may include a computer. Server 22 may include memory. Server 22 includes, for example, an electric circuit.

The first user system 10 and the second user system 20 acquire biometric information and the like of the user 81. The calculation system 30 acquires biometric information and the like 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 calculation system 30 includes, for example, a communication section 31, a server 32, and a control section 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 biometric information of the user 81 or behavioral information of the user 81. 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 the 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. For example, the control unit 33 may cause the RAM 34 provided in the calculation system 30 to set the calculation program.

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

FIGS. 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, a bottom 71 and a mattress 60. The first detector SN1 is provided between the bottom 71 and the mattress 60, for example. 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 signal based on the force detected by the first detector SN1 includes at least part of biological information and behavioral information. The first detector SN1 detects biological information and information regarding the behavior of the user 81 based on at least one of the magnitude of the signal (force) and the temporal change in the magnitude of the signal (force). For example, vibrations depending on the state of the user 81 are applied to the first detector SN1. The vibrations correspond to the body movements of the user 81, for example. Vibrations are detected at the first detector SN1. The vibrations 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 position (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 and the temporal change in force detected by the first detector SN1.

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

As shown in FIG. 1(b), the third detector SN3 is provided on the pillow 72 or the like. Vibrations depending on the state of the user 81 are applied to the third detector SN3. The vibrations correspond to the body movements of the user 81, for example. The third detector SN3 detects, for example, heart rate, pulse, respiration, body temperature, electromyogram (including electrocardiogram), electroencephalogram, blood pressure, and blood oxygen concentration (for example, percutaneous arterial oxygen saturation) of the user 81. It may also be possible to obtain biometric information including at least one of the following:

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 the user's 81 pulse, body temperature, and brain waves, for example. The fourth detector SN4 may be able to acquire behavioral information of the user 81 including at least one of sleep, wakefulness, standing position, sitting position, lying position, walking, and exercise.

As shown in FIG. 1(c), the fifth detector SN5 is, for example, a wearable device shaped like glasses. The user 81 uses the fifth detector SN5, for example, by wearing it in the same way as glasses. 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 able to acquire behavioral information including at least one of sleep, wakefulness, standing position, sitting position, lying position, walking, and exercise.

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

For example, the biological information of the user 81 includes heart rate, pulse, respiration, body temperature, weight, pulse, electromyogram (including electrocardiogram), brain waves, and blood oxygen concentration (for example, percutaneous arterial oxygen saturation). (including).
For example, the behavior information of the user 81 can be classified into sleep, wakefulness, falling asleep, getting out of bed, getting up, sitting position (for example, ready to get out of bed), standing position, sitting position, lying position, walking, and exercise.
For example, the state of the user 81 is sleeping, sitting, standing, walking, running, eating, after eating, while excreting, and after excreting. The state can be classified into the state during bathing, the state after bathing, the state while smoking, and the state after smoking.

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

FIG. 2 is a flowchart showing the evaluation method of the evaluation system according to the first embodiment.
When evaluating the user 81 while driving the vehicle 28, for example, it is evaluated whether there is a high possibility of risk such as an accident. For example, the user 81 may be unwell, excited, or distracted while driving. In such situations, it is assessed whether there is a high possibility of risk such as an accident.

The evaluation system 110 acquires biological information of the user 81 or information regarding 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 the insurance premium based on the evaluation result of the user 81.

Each of the plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) has, for example, a portion that comes into contact with the user 81 (and includes clothing, etc.). Biological information may be acquired 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 pulses. Other detectors SN (for example, the sixth detector SN6) may not be able to acquire pulses.

For example, the user 81 may not use a plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) at the same time. For example, one detector SN is worn in the morning and removed at night. Another detector SN may be worn at night. The accuracy of biological information acquired by a plurality of detectors (first to sixth detectors SN1 to SN6, etc.) may differ from each other.

For example, when the detector SN7 acquires biological information of the user 81 while driving the vehicle 28 and evaluates the user 81, a plurality of detectors SN (the first to sixth detectors SN1 to SN6 etc.). For example, reference data (or reference distribution) that is the basis for calculating insurance premiums is created using biometric information acquired by a plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.). This allows, for example, to calculate accurate insurance premiums.

For example, during one period (evaluation period, timing) when the user 81 is driving, the biometric 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 biological information of the user 81 during a driving period (non-evaluation period) that is different from this evaluation period. get. For example, the plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) acquire biological information when the user 81 is driving during the non-evaluation period. For example, the evaluation system 110 extracts biological information of a non-evaluation period from the biological information acquired by a 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 biological 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, for example, artificial intelligence (AI).

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, evaluation is performed based on the pulse of the user 81. In embodiments, evaluation may be performed based on other biological information (eg, blood pressure, etc.). Before the evaluation system 110 performs the evaluation, a plurality of detectors SN (for example, first to third detectors SN1 to SN3) acquire the pulse rate (biological information) of the user 81 and information regarding the behavior of the user 81. be done. The acquired information is already held in the server 32 of the evaluation system 110. For example, the user 81 uses the second detector SN2 substantially all the time. For example, the user 81 uses the first detector SN1 and the third detector SN3 while sleeping.

(Step S1)
When starting the evaluation of the user 81, the calculation system 30 transmits 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 unit of the second user system 20 receives this command CMD via the communication unit 21. The control unit of the second user system 20 activates the seventh detector SN7.

(Step S2)
The second user system 20 acquires biological information of the user 81, information regarding the behavior of the user 81, and other information using 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 S2 to the calculation system 30 via the communication unit 21. For example, the second user system 20 may transmit the detection results to the calculation system 30 after detecting biological information and the like for a certain period of time. For example, the second user system 20 may transmit the detection result to the calculation system 30 at any time while detecting biological information or the like. For example, data DATA1 including this information is transmitted from the second user system 20 to the computing system 30.

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

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

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

The server 32 holds biological information etc. acquired from a plurality of detectors SN (first to third detectors SN1 to SN3). The server 32 holds this biometric information together with the user's 81 status. The state of the user 81 may be, for example, whether or not he is sleeping, sitting (sitting), standing (standing), walking or not, and running. Whether or not you are eating or not, whether you are after eating or not, whether you are defecating or not, whether you are after defecation, whether you are taking a bath or not, whether you are after bathing, whether you are smoking or not, or whether you are after smoking. It is classified according to

For example, the state St0 of the user 81 corresponds to a state in which 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 user 81 standing.

An example in which the control unit 33 extracts data of the user 81 in the "same state" will be described. When the user 81 performs a certain action, the control unit 33 determines which of the above categories stored in the server 32 the state of the user 81 corresponds to. The classifications stored in server 32 are finite. When the state of the user 81 does not correspond to the classification stored in the server 32, the control unit 33 selects a state close to the state of the user 81 among the classifications stored in the server 32 as "same state". choose. For example, the classification of the state of the user 81 stored in the server 32 does not include "driving state." 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 when 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, it is specified whether the user 81 is in either state St0 or St1 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 other detectors SN, for example, the user 81 You may input your status from a communication terminal or the like.

For example, the control unit 33 determines that the "driving state" corresponds to the state St1 of the user 81. Then, the control unit 33 extracts information regarding the pulse that corresponds to the state St1 of the user 81 from the information regarding the pulse held in the server 32. The control unit 33 introduces the extracted information regarding the pulse into the RAM 34.

(Step S7)
The control unit 33 classifies "pulse information" corresponding to the state of the user 81 for each of the plurality of detectors SN. For example, the control unit 33 classifies which of the first to third detectors SN1 to SN3 has acquired the "pulse information" that corresponds to the state of the user 81. 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 calculates the pulse distribution X[ _ei,t , _σi,t ] (i is the number of the detector SN, t (or a predetermined time). The average pulse distribution is "ei,t". The variance of the pulse distribution is "σi,t".

(Step S8)
The control unit 33 generates a pulse reference distribution (or reference data) in consideration of the reliability of the first to third detectors SN1 to SN3. The control unit 33 generates the reference distribution C _S1t using weighting functions F _k,t (k is the number of the detector) 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 a certain function. The reference distribution C _S1t is expressed by the following first equation.

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

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

An example will be described below in which, after the control unit 33 generates the reference distribution C _S1,t1 for a certain period t1, the control unit 33 acquires biological information for another period t2 and updates the reference distribution.

At this time, the control unit 33 calculates the reference distribution C _S1,t2 in the same steps as 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 number of the period). The reference distribution C _S1,t1+t2 is expressed by the following fourth equation.

The weighting function G _ti is, for example, a function for weighting between multiple periods. This weighting function G _ti may be a natural number, for example. The weighting function G _ti may be a certain 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 the range of ±1σ or ±2σ of the standard distribution C _Sl,t1+t2, for example. It is determined whether it is within the ±3σ range, whether it is within the ±3σ range, or whether it is not within the ±3σ range. Based on the results, the control unit 33 evaluates the risk of the user 81 driving the vehicle 28.

(3 Calculation of insurance premiums for automobile insurance)
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, the value Iv1 of the insurance premium for automobile insurance is determined corresponding to the value of the evaluation value Ev1 (1 to 9 in this example). The evaluation value Ev1 is a classification corresponding to the risk of the user 81.

As shown in FIG. 3, the control unit 33 classifies and evaluates the users 81 according to their risks. The control unit 33 calculates the automobile insurance premium of the user 81 according to the classification.

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

In the evaluation system 110 according to the present embodiment, the calculation system 30 compares the biometric information of the user 81 who is driving and the biometric information of the user 81 when the user 81 was in the same state as the user 81. do. The evaluation system 110 then evaluates the user 81 by evaluating the risk of the user 81 driving. Then, based on the result of the evaluation, 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 car insurance premium.

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

(Other examples)
In the above example, user 81 driving 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 to earn a living.

For example, if the control unit 33 determines that the work is performed while standing, the control unit 33 extracts biometric information and the like corresponding to the state St2 of the user 81 in step S6 described above. Based on the results, the control unit 33 evaluates the user 81. This allows the user 81's risk (for example, possible health impairment) to be evaluated while performing the job. For example, if the work is work, the future health risks of the user 81 can be predicted. The results of these evaluations may include, for example, conditions (for example, rank) for at least one of life insurance and medical insurance premiums.

In this manner, according to the embodiment, future risks of the user 81 can be evaluated more accurately. For example, by presenting insurance conditions using this result, more accurate insurance conditions can be determined.

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

Based on this prediction, the user 81 can reduce future risks by making appropriate changes. The calculation system 30 (and the evaluation system 110, etc.) can be used as a training system for the user 81.

In embodiments, work 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 is different from the evaluation system 110 of the first embodiment in that an output system 40 is further added, and the other configurations are different from the evaluation system 110 of the first embodiment. The same is true. In the first embodiment, the control unit 33 calculates the insurance premium 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 that calculates insurance premiums as shown in FIG. According to the program, the output system 40 calculates 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 may be made by any wired or wireless method. The information terminal 41, the server 22, and the control unit 43 may include a computer. The information terminal 41, the server 22, and the control unit 43 may include a memory. The information terminal 41, the server 22, and the control unit 43 include, for example, an electric circuit. The information terminal 41 may include, for example, a smartphone.

The information terminal 41 communicates with the communication unit 31 of the calculation system 30, for example. Communication includes sending and receiving data. For example, the communication unit 31 of the calculation system 30 sends the evaluation result of the user 81 to the information terminal 41 of the output system 40 . The information terminal 41 calculates the insurance premium for the user 81 based on the evaluation result. Evaluation results and insurance premiums are stored in the server 42 for each user 81. The control unit 43 controls the operations of the information terminal 41 and the server 42, for example. The control unit 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 by, for example, an insurance provider (eg, an insurance company). The insurance provider may determine the conditions 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 a table described below.

(1. Structure of evaluation system)
For example, when one type of biological information (for example, pulse) is acquired by multiple detectors SN, the acquisition accuracy of the multiple detectors SN is taken into consideration. The server 32 of the calculation system 30 maintains a table regarding the reliability of each of the plurality of detectors SN for each plurality of 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, the server 32 maintains a table. In this table, for example, reliability CE for a plurality of detectors SN is determined for each of a plurality of pieces of biological information (for example, pulse rate). For example, if the value of reliability CE is large, the reliability of the evaluation result (information) by the detector SN is high. FIG. 5 shows a table regarding pulse rates.

In this example, the reliability CE is 10 in the first detector SN1. The reliability CE of the second detector SN2 is 3. The reliability CE of the third detector SN3 is 7. The reliability CE of the fifth detector SN5 is 5. For other detectors SN, the reliability CE is 0. Any detector SNi has a reliability CEi.

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

A specific evaluation method will be explained using FIGS. 5 and 6.

FIG. 6 is a flowchart 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 table related to reliability CE 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 the process of proceeding 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, but pulse information is obtained from the second detector SN2 and third detector SN3, the total reliability CE is 10 (=3+7). be. At this time, the control unit 33 performs the process of proceeding to step SS2.

In step SS2, the control unit 33 estimates a reference distribution (or reference data).

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

An example will be described below based on the premise described in the evaluation method of the first embodiment. However, in the following example, the calculation system 30 cannot acquire 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 calculation system 30 is able to acquire information regarding the pulse rate of the user 81 corresponding to the state St0 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. Using the correlation, the control unit 33 determines the pulse rate obtained by the first detector SN1 from the pulse information obtained from the second detector SN2 and the third detector SN3, which corresponds to the state St1 of the user 81. Estimate the pulse information that will occur.

The control unit 33 generates a reference distribution (or reference data) 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. generate.

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

In the third embodiment, the calculation system 30 (for example, the control unit 33) evaluates the user 81 based on information (at least one of biological information and behavioral information) acquired from a plurality of detectors SN. . For example, when obtaining evaluation results, multiple pieces of information (data) obtained from multiple detectors SN can be matched. 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 conditions 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 the operation of the evaluation system according to the embodiment.
As shown in FIG. 7, for example, in step S9, the control unit 33 of the calculation system 30 (third system) evaluates the user 81. If the evaluation result of the user 81 is higher than the predetermined rank, the process ends. On the other hand, if the user's 81 evaluation is below the predetermined rank, the control unit 33 issues an automatic driving switching instruction. For example, a command CMD for instructing automatic operation switching is transmitted to the second user system 20 (second system). The second user system 20 starts automatic driving based on a command CMD for instructing automatic driving switching. For example, automatic operation 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 the automatic driving program is provided from the calculation 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 the evaluation system according to the embodiment.
FIG. 8 shows an example of the hardware configuration of the calculation system 30. The calculation system 30 includes, for example, a communication section 31 (communication interface), a server 32 (for example, an interface with an external memory, etc.), a control section 33 (for example, a CPU), a RAM 34a, a ROM 34b, and the like. The calculation system 30 may further include, for example, an input section 35a and an output section 35b.

The processing (instructions) 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 this program to process the various information described above.

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

For example, information recorded on a recording medium can be read by a computer (or an embedded system). In the recording medium, the recording format (storage format) is arbitrary. 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. In a computer, a program may be acquired (or read) through a network.

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

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

A computer according to an embodiment includes one or more devices (eg, a personal computer, etc.). A computer according to an embodiment may include a plurality of devices connected via a network.

(Fourth embodiment)
In the fourth embodiment, an evaluation system 110 (see FIG. 1(a)) includes a calculation system 30 (for example, a calculation device, see FIG. 1(a)).

The evaluation system 110 (for example, the calculation system 30) obtains, for example, first information regarding the user 81 and second information regarding the user 81. This first information includes at least one of biological information and behavioral information of the user 81 in a first state in which the user 81 is performing one task (first task). On the other hand, the second information includes at least one of biological information and behavior information of the user 81 in a second state in which the user 81 is not performing the work (first work). The evaluation system 110 can output evaluation result information regarding the first work of the user 81 based on the first information and second information. In this embodiment, the user 81 is the person to be evaluated.

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

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

For example, the condition of the user 81 is evaluated by the first detector SN1 and the 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 during and before and after sleeping. The first detector SN1 detects, for example, the heartbeat, pulse, breathing, sleep, and wakefulness of the user 81.

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

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, breathing, sleep, and wakefulness.

In the following, to simplify the explanation, a case will be described in which pulse information is used among various data obtained by these detectors SN. In the following, information (for example, data) obtained by the detector SN will be modeled and explained.

FIGS. 9(a) to 9(c) are schematic diagrams illustrating information in the evaluation system according to the fourth embodiment.
The horizontal axis of these figures is time tm. The vertical axes in each of FIGS. 9(a) to 9(c) represent 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, for example, the pulse rate per minute corresponds to the strength of these signals.

These signals become at least part of the information regarding the user 81. Alternatively, information obtained by processing these signals becomes part of the information regarding the user 81. These figures illustrate 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 becomes strong while the user 81 is sleeping and before and after sleeping (that is, at night, for example). For example, during the daytime, the first detector SN1 does not perform detection, so the first signal SG1 cannot be obtained. While the user 81 is sleeping, the pulse rate changes depending on the sleeping state of the user 81. According to this, the intensity of the first signal SG1 changes.

As shown in FIG. 9(b), 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 at night. 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 riding the vehicle 28. 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). The period excluding the first state ST1 becomes the second state ST2.

For example, in the first period (period including the first time) on the fifth day D5, the user 81 is performing the first task OP1 (driving the vehicle 28). The other days (in this example, the first day D1 to the fourth day D4, the sixth day D6, and the eighth day D8) 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 The second signal SG2 in the first period (period including the first time) of the eighth day D8) is compared. In this case, a difference occurs in the second signal SG2 corresponding to the difference in the presence or absence of the first work OP1. For example, the intensity (pulse rate) of the second signal SG2 when the first work OP1 (driving) is being performed, and the intensity (pulse rate) of the second signal SG2 when the first work OP1 (driving) is not being performed. There is a difference between. For example, the former is stronger than the latter.

In this way, the biometric information of the user 81 changes depending on whether or not the first task OP1 is performed. This change in biometric information is related to the possibility (eg, degree of risk) of occurring in the first task OP1 when the user 81 performs the first task OP1 (driving). The user 81 can be evaluated regarding the first task OP1 by evaluating changes in the biometric information of the user 81 based on whether or not the first task OP1 is performed.

In the fourth embodiment, first information including at least one of biological information and behavior information of the user 81 in the first state ST1 in which the user 81 is performing the first task OP1, and second information including at least one of biological information and behavioral information of the user 81 in the first state ST1 in which the user 81 is performing the first task OP1, and The evaluation regarding the first task OP1 of the user 81 is performed based on the relative relationship (for example, difference) between the second information including at least one of biological information and behavioral information of the user 81 in state ST2. be exposed.

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

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

In the above example, one detector (second detector SN2) can detect the first information (the state when the first work OP1 is being performed) and the second information (the state when the first work OP1 is being performed). The states of , , , and , are detected.

In the fourth embodiment, the first information and the second information may be obtained by different detectors. For example, the first information (the state when performing the first work OP1) may be obtained by 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, an evaluation regarding the first work OP1 of the user 81 may be performed.

The first information and the second information may be obtained by mutually different detectors SN. At this time, the relationship between the derived data (information) is determined 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 detectors SN may be appropriately corrected.

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

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

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's pulse, and the second information is also the user's 81's pulse. The first information is the user's 81's breathing, and the second information is also the user's 81's breathing. The first information is the user's 81 body temperature, and the second information is also the user's 81 body temperature. The first information is the weight of the user 81, and the second information is also the weight of the user 81. The first information is the electromyogram (including electrocardiogram) of the user 81, and the second information is also the electromyogram (including the electrocardiogram) of the user 81. The first information is the user's 81 brain waves, and the second information is also the user's 81 brain waves. 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 user's 81 blood oxygen concentration, and the second information is also the user's 81 blood oxygen concentration. The first information is the exhaled gas of the user 81, and the second information is also the exhaled gas of the user 81.

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 user's 81's pulse, respiration, body temperature, weight, electromyogram (including electrocardiogram), brain waves, blood pressure, blood oxygen concentration ( For example, it may include percutaneous arterial blood oxygen saturation) and/or exhaled gas. For example, different types of biometric information may be related to each other. For example, changes in pulse rate may be linked to changes in heartbeat. In such a case, for example, the first detector SN1 may detect a pulse, and the seventh detector SN7 may detect a heartbeat.

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

As described above, in one embodiment, the evaluation result of the user 81 includes 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 and the second information. The 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 task OP1 includes driving the vehicle 28, the evaluation result information is information regarding the risk of driving for the user 81, for example. For example, the pulse rate while driving may be excessively higher than the pulse rate 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 likely to cause an accident while driving. The estimated result becomes evaluation result information.

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

An example of the user's 81 evaluation will be described below.
FIGS. 10(a) to 10(c) are schematic diagrams illustrating evaluation result information according to the fourth embodiment.
FIG. 10A illustrates one index xv1 of the user's 81 evaluation results. The index xv1 includes, 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). It is a value corresponding to the relative relationship (for example, difference) between and. 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 task OP1 (for example, driving) is high.

For example, index xv1 relates to pulse. For example, the index xv1 may relate to temporal changes in pulse rate.

A plurality of ranges are determined depending on the value of the index xv1. In this example, ranges are defined in intervals of five. In one range, the difference in index xv1 is 5. An evaluation value Ev1 is determined for each of the plurality of ranges. In this example, when the value of the index xv1 is large, the evaluation value Ev1 is large.

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 evaluation value Ev1 becomes at least a part of evaluation result information.

In this example, multiple ranges regarding the index xv1 are divided at equal intervals. In embodiments, the sections may be non-uniformly spaced.

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

As shown in FIG. 10(c), a plurality of ranges are determined 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 temporal changes in blood pressure. In this example as well, the evaluation value Ev2 is determined for each of the plurality of ranges. In this example, when the value of the index xv2 is large, the evaluation value Ev2 is large.

For example, evaluation may be performed using at least one of such evaluation value Ev1 and 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 values Ev2 is larger than the range of evaluation values Ev1. For example, the evaluation result of the index xv2 may more appropriately express the change in biological information in the targeted first task 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. This allows more appropriate evaluation to be performed when a plurality of evaluation values are used.

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

In this way, in the fourth embodiment, the evaluation result information includes one of the plurality of classified ranks. The plurality of classified ranks are, for example, the above-mentioned evaluation value Ev1 or evaluation value Ev2. The classified plurality of ranks may be, for example, a processing result (for example, a sum, a product, a weighted sum, etc.) of a plurality of evaluation values (for example, evaluation value Ev1, evaluation value Ev2, etc.). For example, one of the plurality of ranks is output by any method.

The fourth embodiment described above may be implemented by the evaluation system 111 (see FIG. 4).
In one example of output system 40 (eg, an evaluation results output system), output system 40 is used by, for example, an insurance provider (eg, an insurance company). The insurance provider may determine the insurance conditions 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 a part of the insurance conditions of the automobile insurance may be determined based on the evaluation result information of the user 81 regarding the first work OP1.

For example, the evaluation value Ev1 regarding the index xv1 as illustrated in FIGS. 10(a) to 10(c) is used. For example, insurance conditions and the like may be set according to the evaluation value Ev1. The insurance conditions include, for example, at least one of an insurance premium and an insurance money.

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, when the evaluation value Ev1 is "1", the insurance premium value Iv1 is 11,000 yen. For example, when the evaluation value Ev1 is "9", the insurance premium value Iv1 is 55,000 yen.

In this way, the user 81 is evaluated regarding the user 81 performing the first work OP1 (for example, driving). Then, 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 work OP1 (for example, driving) is based on the information of the user 81 (for example, biological information). Therefore, the user's 81 evaluation result is more accurate. The insurance premium value Iv1 can be determined more accurately based on the actual situation of the user 81.

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

In the above example, the first task OP1 includes driving the vehicle 28. In the embodiment, the first work OP1 is arbitrary.

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

For example, during a period when the user 81 is working, the biological information (for example, blood pressure, etc.) of the user 81 may be significantly different from when the user 81 is not working. In this case, for example, it is presumed that the user 81 is subject to a large physical and mental burden due to work. For example, if an excessive load is applied to the user 81, the health of the user 81 may be impaired.

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

In this way, according to the fourth embodiment, the future risks of the user 81 can be evaluated more accurately. For example, by presenting insurance conditions using this result, more accurate insurance conditions can be determined.

Furthermore, in the fourth embodiment, the state when the user 81 performs the first work OP1 in the future can be output. For example, it is possible to predict and output not only the state of the user 81 when actually performing the first work OP1, but also the states (symptoms) that are likely to occur when the user 81 performs the first work OP1 in the future. 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 lower risk. For example, the computing system 30 (and the evaluation system 110, etc.) can be used as a training system for the 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, one of a plurality of classified ranks regarding the user 81 is included based on the information (biological information and behavioral information) of the user 81 during a period of one day or more. 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 health checkup is performed is detected. The information obtained through the health checkup detects the health condition of the user 81 at that time. However, it is difficult to evaluate the possibility (for example, risk) of the user 81's condition when performing the first work OP1 based on the health condition detected in the medical examination.

For example, in a health checkup, standard ranges are determined for evaluation items (eg, blood pressure, etc.). For example, a normal range of blood pressure is determined, and when the range is exceeded, it is determined that a person is in a disease state, for example. That is, information obtained from the user 81 (for example, biological information) is compared with a separately determined standard value (for example, a normal range). Based on the comparison results, the health condition of the user 81 is determined.

On the other hand, biometric information of a user 81 (for example, a patient or a care recipient) is acquired in a hospital or nursing care facility. For example, pulse rate or the like is detected as biological information. The detected pulse is then compared with a separately determined standard value (for example, a normal range). Based on the results, for example, an alarm is generated and appropriate action is taken. However, the future possibilities of the user 81 (for example, the risks when performing the first task OP1) are not evaluated based on the results of biometric information acquisition at a hospital or nursing care facility.

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

In the fourth embodiment, the period for acquiring information about the user 81 may be, for example, two days or more. For example, if the period is one day, one change in day/night conditions occurs. Therefore, the variation in the obtained information may be large. If the period for acquiring information about the user 81 is two days or more, the day/night condition occurs multiple times. For example, information obtained under multiple conditions is averaged. Thereby, the user 81 can be evaluated with higher accuracy.

The period for acquiring information about the user 81 may be, for example, three days or more, or five days or more. This allows evaluation results with higher accuracy to 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 information corresponding to the past 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 set as the second state ST2. For example, if the first information is the pulse rate while performing the first work OP1, data on the pulse rate while sleeping 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 premium and insurance money), deposit conditions (for example, interest rate, etc.), and the provision of various services. It may also include at least one of the conditions for receiving the service (for example, the content of the service, etc.).

(Fifth embodiment)
Also in the fifth embodiment, for example, a calculation system 30 (for example, a calculation device, see FIG. 1(a) or FIG. 4) is provided. The calculation system 30 obtains first information including biological information of the user 81 and second information including behavioral information of the user 81. This information is detected by, for example, the first detection system 10 or the second detection system 20. For example, the second information may be input by the user 81 or the like.

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

For example, biological information (for example, pulse rate, etc.) differs between when the person is sleeping and when the person is exercising. Based on the degree of this difference, etc., it is possible to evaluate (estimate) the possibility of the future state of the user 81 (for example, the possibility of injury or illness). The evaluation results are output as evaluation result information.

For example, the start time and end time of sleep of the user 81 are included in one of the behavioral information (second information). On the other hand, biological information during sleep (such as changes in pulse rate) or biological information during the day (such as changes in pulse rate) is acquired as the biological information (first information) of the user 81. Based on such first information and second information, the possibility of the future state of the user 81 (for example, the possibility of injury or illness) can be evaluated (estimated). The evaluation results are output as evaluation result information.

Also in the fifth embodiment, it is possible to provide an evaluation system that can use user information 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 makes the evaluation result of the user 81 clear. The evaluation result information may include, for example, conditions (for example, rank) for at least one of life insurance and medical insurance premiums.

(Sixth embodiment)
Also in the sixth embodiment, for example, a calculation system 30 (see FIG. 1(a) or FIG. 4) is provided. The calculation 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.

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

For example, biometric information (eg, blood pressure, etc.) of the user 81 (eg, patient or care recipient) is detected at a health checkup or in a hospital (including a nursing care facility). The detected biological information is then compared with a separately determined standard value (for example, a normal range). Based on the results, the health of the user 81 is diagnosed, for example. Alternatively, an alarm is generated and appropriate action is taken. However, the future possibilities (for example, risks, etc.) of the user 81 are not evaluated as a plurality of classified ranks based on the results of biometric information acquired at a medical examination or a hospital. Further, future possibilities (for example, risks, etc.) are not output as one of the plurality of classified ranks.

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

In the sixth embodiment, the period for acquiring biometric information of the user 81 is, for example, one day or more. This period may be two days or more. For example, if the period is one day, one change in day/night conditions occurs. Therefore, the variation in the obtained information may be large. If the period for acquiring information about the user 81 is two days or more, the day/night condition occurs multiple times. For example, information obtained under multiple conditions is averaged. Thereby, the user 81 can be evaluated with higher accuracy. The period for acquiring information about the user 81 may be, for example, three days or more, or five days or more. This allows evaluation results with higher accuracy to be obtained.

Also in the sixth embodiment, an evaluation system that can utilize user information more effectively can be provided.

The processes 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 this embodiment, insurance is a mutual aid system in which participants fairly share a fixed insurance premium commensurate with the predicted probability of an accident occurring against risks that may occur in the future. It is a system of mutual help born from the spirit. Insurance can be classified based on the subject of risk: insurance for people (for example, insurance against death, injury, or illness), insurance for things (for example, insurance for damage to property), and insurance for money (for example, insurance for loss of income). ), and insurance related to matters (for example, insurance against liability).

As shown in FIG. 11, examples of insurance related to people and money include life insurance and accident insurance. Examples of insurance include fire insurance, earthquake insurance, automobile insurance, compulsory automobile liability insurance, traffic accident insurance, general accident insurance, overseas travel accident insurance, medical security insurance, nursing care insurance, and pension insurance. The present invention may also be applied to these types of insurance with appropriate design changes.

According to the embodiment, an evaluation system that can utilize biological information more effectively can be provided.

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

Combinations of any two or more elements of each specific example to the extent technically possible are also included within the scope of the present invention as long as they encompass the gist of the present invention.

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

In addition, it is understood that various changes and modifications can be made by those skilled in the art within the scope of the idea of the present invention, and these changes and modifications also fall within the scope of the present invention. .

10... First user system, 11... Communication terminal, 20... Second user system, 21... Communication department, 22... Server, 27... Seat, 28... Vehicle, 30... Arithmetic system, 31... Communication department, 32... Server, 33...Control unit, 34...RAM, 34a...RAM, 34b...ROM, 35a...Input unit, 35b...Output unit, 40...Output system, 41...Information terminal, 42...Server, 43...Control unit, 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-S8...Step, SG1...First signal, SG2...Second signal, SG7...Seventh signal, SN, SNi...Detector, SN1-SN7... 1st to 7th detectors, ST1, ST2...state, t1 to t5, tk...period, tm...time, xv1, xv2...indicator

Claims (3)

a first detector;
a second detector different from the first detector;
Based on the first biological information acquired by the first detector during the first period in which the user is in the first state in which the user is performing the first task, the user is in the first state and in the first period. a control unit that evaluates second biological information acquired by the second detector during a second period different from the first period;
Equipped with
A system for switching an apparatus used in the first work from manual operation to automatic operation when the control unit evaluates that the user is in an abnormal state by evaluating the second biological information. The system according to claim 1, wherein the first biometric information and the second biometric information are the same type of biometric information. The system according to claim 1 or 2, wherein the control unit calculates an insurance premium regarding the first work of the user based on the evaluation result of the second biological information.

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