JP2019146901A - Service providing device and service providing program - Google Patents

Abstract

To provide a service providing device and a service providing program capable of providing detailed services suitable for the state of a subject.SOLUTION: The service providing device estimates a plurality of state elements for a user state on the basis of biological information of a user H. Next, a standard weight coefficient corresponding to an operation mode is acquired for the state elements, and the standard weight coefficient is adjusted on the basis of the personal information of the user H and the travel route information and the vehicle information of a vehicle C. The service content of the service to be provided to the user H is estimated from the state elements and the adjusted weight coefficient. Here, a state element in which 1 to 10 are set as the weight coefficient is selected as a state element for estimating the service content. Then, on the basis of the estimated service content, the device is operated and provided to the user H. Thus, detailed services suitable for the user state can be provided.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a service providing apparatus and a service providing program for providing various services to a target person.

  In Patent Literature 1, user information is acquired by the sensor 10, the user's emotion (wakefulness, surprise, joy, comfort, comfort, sleepiness, irritability, etc.) is estimated based on the acquired physical quantity, and the emotion is presented Techniques to do this are disclosed. As a presentation method, in addition to displaying emotions on the display device 17 (FIGS. 3 and 6, etc.), the shift 41 of the vehicle 40, the interior lighting 42, the engine speed 43, the brake 44, the steering 45, the navigation device 46, etc. There is something to control (FIG. 10).

JP 2017-73107 A

  However, according to Patent Document 1, since the presentation of emotion is performed based on one of the emotional elements such as awakening, surprise, joy, comfort, comfort, sleepiness, and irritation, the user (subject) It was difficult to provide detailed control (control) suitable for the above conditions.

  SUMMARY An advantage of some aspects of the invention is that it provides a service providing apparatus and a service providing program capable of providing a detailed service suitable for a subject's condition.

  In order to achieve this object, the service providing apparatus of the present invention includes a detection unit that detects biological information of the subject, and the state of the subject based on the biological information detected by the detection unit. Estimating means for estimating separately, and weighting means for selecting an element according to a service to be provided from a plurality of elements estimated by the estimating means, and weighting the selected element according to the service And control means for controlling each device in accordance with the weighting result by the weighting means, and providing the service to the subject by the operation of each device controlled by the control means.

  In addition, the service providing program of the present invention includes a detection function for detecting the subject's biological information, and an estimation function for estimating the subject's state in a plurality of elements based on the biological information detected by the detection function. A weighting function that selects an element according to a service to be provided from a plurality of elements estimated by the estimation function, and weights the selected element according to the service, and the weighting function. A control function for controlling each device according to a weighting result is realized by a computer, and the service is provided to the target person by the operation of each device controlled by the control function.

  According to the service providing apparatus and the service providing program of the present invention, first, based on the detected biological information of the subject, the state of the subject is estimated by dividing it into a plurality of elements. Next, an element corresponding to the service to be provided is selected from the estimated plurality of elements, and the selected element is weighted according to the service. Each device is controlled according to the weighting result, and the service is provided to the target person by operating each device. In this way, since an element corresponding to a service to be provided is selected from a plurality of elements and the selected element is weighted according to the service to provide the service to the target person, There is an effect that a detailed service suitable for the state can be provided.

  Note that the weighting means (weighting function) relates to “selection of elements according to service” by setting the weighting to “0” for elements that are not selected and setting the weighting other than “0” for the elements that are selected. You may make it do. That is, the weighting means (weighting function) may perform the selection and weighting at a time.

It is a functional block diagram for demonstrating the function of a service provision apparatus. (A) is a figure showing adjustment of the weighting factor by the weighting factor estimation part according to vehicle information in safe driving mode, (b) is a figure by the weighting factor estimation part according to vehicle information in safe driving mode. It is a figure showing adjustment of a weighting coefficient. It is a block diagram which shows the electrical structure of the motor vehicle by which a service provision apparatus is mounted. (A) is a figure which represented standard weighting coefficient data typically, (b) is a figure for demonstrating a user state. (A) is a diagram schematically showing a user status table, (b) is a diagram schematically showing a weighting factor table, and (c) is a diagram schematically showing a service table. It is. It is a flowchart which shows the main process of a service provision apparatus.

  Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. The service providing apparatus 1 is mounted on the automobile C, and each apparatus of the automobile C is determined according to the user state estimated from the biological information of the user (target person) H who is the driver of the automobile C, vehicle information of the automobile C, and the like. By operating 18 to 23 (see FIG. 3), the device provides a “service” such as operation assistance or operation assistance for the vehicle C.

  First, the function of the service providing apparatus 1 will be described with reference to FIG. FIG. 1 is a functional block diagram for explaining the functions of the service providing apparatus 1. As shown in FIG. 1, the service providing apparatus 1 mainly includes a state estimation unit 30, a weight coefficient estimation unit 31, and a service estimation unit 32. Furthermore, the biometric sensor 16, the cloud server 25, the vehicle sensor 17 and the vehicle device 33 mounted on the car C are connected to the service providing apparatus 1, respectively.

  The state estimation unit 30 is a calculation unit that estimates the user state based on the biological information of the user H detected by the biological sensor 16. Here, the biosensor 16 that detects the biometric information of the user H will be described. The biometric sensor 16 is a sensor that detects biometric information such as the facial expression, line of sight, eye movement, voice, posture, brain wave, cerebral blood flow, blood pressure, heart rate, body temperature, blood oxygen content, and sweating amount of the user H. Of such biological information, information detected from the biological sensor 16 as a facial expression, eye line, eye movement, voice, and posture will be described.

  As the “expression” of the biometric information, a true face, a smile, a Buddha top face, a crying face, a sleeping face, etc. are distinguished from the face image of the user H, and a change in expression (for example, a change from a true face to a smile) is also detected. Further, the face color of the user H (flushing, pale, etc.) is also detected. As the “line of sight” of the biological information, the position of the line of sight of the user H, the amount of movement of the line of sight per unit time (for example, 1 second), the differential value of the movement amount, and the like are detected. Is the stop time and stop count of the eye movement of the user H, the movement pattern and the blink, and the pupil opening degree, the amount of change in the pupil opening per unit time (for example, 1 second), and the derivative of the amount of change. The value is detected.

  As the “voice” of the biometric information, the loudness, the pitch of the voice of the user H, the change of the voice color, and the like are detected, and the content of the conversation between the user H and the passenger and the content of the user H's monologue are also detected. . As the “posture” of the biometric information, the state of the posture of the user H such as the spine being extended, the back of the spine, or leaning toward the body side, or the change in the posture (for example, the spine is changed from the state where the spine is extended) ) Is detected.

  In this way, the biological information of the user H detected by the biological sensor 16 is output to the state estimation unit 30 and also transmitted to the cloud server 25 existing on the network. The biosensor 16 may be composed of a plurality of devices (camera, microphone, electroencephalograph, cerebral blood flow meter, blood pressure monitor, heart rate monitor, thermometer, blood oximeter, sweat meter, etc.) or a single device. You may comprise by an apparatus.

  The state estimation unit 30 estimates the user state based on the biological information detected from the biological sensor 16. In the present embodiment, the user state is composed of seven elements (hereinafter referred to as “seven state elements”) such as physical condition, perception, memory, cognition, emotion, sensitivity, and intention. Here, each state element of the user state estimated by the state estimation unit 30 will be described.

  The “physical condition” of the state element is an element that represents the physical condition of the user H (good, somewhat bad, nausea, dullness, etc.). The physical condition is comprehensively determined from the detected biological information mainly from facial expressions, eyes, voice, posture, blood pressure, heart rate, body temperature, blood oxygen content and sweating. The “perception” of the state element can grasp how much the situation (for example, the number and form of objects in the vicinity of the user H) is placed by the user H's vision, hearing, etc. (that is, eyes, ears, etc.). (High, medium, low, etc.) Perception is comprehensively determined from the detected biological information mainly from the line of sight (position of the line of sight), eye movement (opening of the pupil), brain waves and cerebral blood flow.

  The “memory” of the state element is an element indicating what kind of memory the user H has for the position where the car C travels (there is a good memory, a bad memory, etc.), and the detected biological information Of these, it is judged comprehensively mainly from EEG, cerebral blood flow, heart rate and sweating. The “recognition” of the state element is the level of the meaning of the situation where the user H perceived by vision, hearing, etc. is placed (for example, “red light” of the traffic light means “progress prohibition”) in the brain. It is an element indicating whether it can be recognized (high, medium, low, etc.). Recognition is comprehensively determined from the detected biological information mainly from the line of sight (position of the line of sight), eye movement (opening of the pupil), brain waves and cerebral blood flow.

  The state element “emotion” is an element representing the feeling (joy, anger, sadness, etc.) of the user H, and mainly includes facial expression, voice, posture, heart rate, eye movement, brain wave among the detected biological information. It is judged comprehensively from cerebral blood flow. The “sensitivity” of the state element is an element that expresses the impression (good feeling, bad feeling, etc.) that the user H has intuitively, and mainly includes facial expressions, voices, brain waves, and brain blood among the detected biological information. It is judged comprehensively from the current. The “intent” of the state element is an element representing what the user H wants to do (want to go straight, turn right, etc.). Of the detected biological information, the judgment is made comprehensively mainly from facial expressions, voices (particularly, the conversation contents of the user H), brain waves and cerebral blood flow.

  Thus, based on the user state estimated by the state estimation unit 30, a service provided to the user H is estimated by the service estimation unit 32 described later. Further, the user state is also transmitted to the cloud server 25 and used for estimation of the weighting coefficient from the next time. Note that the state elements estimated by the state estimation unit 30 are not limited to seven such as physical condition, perception, memory, cognition, emotion, sensitivity, and intention, and other state elements other than the seven are added. It may be estimated.

  In addition, the biological information for determining each state element of the user state is not limited to the combination described above, and the state element may be determined based on a combination of more biological information. It may be determined by omitting some biological information from the combination. Further, the value representing each state element is not limited to the above-described values (good, somewhat bad, nausea, dullness, etc.), and the state element may be appropriately represented by other values.

  The weight coefficient estimation unit 31 is a calculation unit that estimates a weight coefficient for the user state estimated by the state estimation unit 30. The weighting coefficient is a coefficient for setting the importance (that is, weighting) for each state element of the user state in order to provide a service corresponding to an operation mode (safe driving mode, driving skill maintenance mode, etc.) described later from the user state. It is.

  In the present embodiment, the weighting factor is an integer value from 0 to 10. The maximum value of 10 is a value indicating that the state element is regarded as most important for the service, while the minimum value of 0 is a value indicating that the state element is not used for the service. The importance of the user state with respect to the service increases in ascending order of 1 to 9. That is, a state element in which “0” is set as the weighting coefficient indicates that it is not selected for service estimation.

  Instead of setting a weighting factor of 0 for a non-selected state element, a state element necessary for service estimation is selected in advance, and a weighting factor of 1 to 10 is set for the state element. Anyway.

  First, the weighting factor estimating unit 31 obtains a standard weighting factor (see FIG. 4A, hereinafter referred to as “standard weighting factor”), which is set in advance for each operation mode of the service providing apparatus 1. To do. The standard weighting factor is a weighting factor set by prior research and is a weighting factor that can provide a service that requires the operation mode in a certain environment and in a certain user state. That is, the service estimated by the service estimation unit 32 (to be described later) based on a certain user state and a standard weighting factor under a certain environment is a service itself that is desired to be provided in the operation mode.

  Since the standard weighting factor is a weighting factor in a certain environment and in a certain user state, in order to provide the user H with a service that requires the operation mode, a standard weighting factor is used according to the state of the user H or the car C. The coefficient needs to be adjusted. Therefore, the weight coefficient estimation unit 31 determines the standard weight coefficient according to the user state, the vehicle information acquired from the vehicle sensor 17, the personal information corresponding to the user H acquired from the cloud server 25, and the travel path of the car C. Adjust based on the travel route information. Here, vehicle information, personal information, and travel route information used for adjusting the weighting coefficient will be described.

  The vehicle sensor 17 is a sensor that acquires vehicle information of each part of the automobile C. The vehicle information detected from the vehicle sensor 17 includes the speed, acceleration, position information, angular velocity, angular acceleration, room temperature, outdoor sound, and the like of the automobile C. The vehicle sensor 17 may be constituted by a plurality of sensors such as a speedometer, an accelerometer, a GPS positioning device, and a thermometer, or may be constituted by a single sensor.

  The personal information is information for each user H stored in the cloud server 25. The sex and age group of the user H (20s, 30s,...), Blood type, height, weight, and the chronicity of the user H. As a result of the medical condition, medical history, personality tendency (adult, impatient, etc.) and personality test (YG personality test, etc.), driving history, traffic accident history, location visited by the user H, impression and memory of the location, and the like are included. Moreover, the biometric information detected by the biometric sensor 16 described above, the user status estimated by the status estimation unit 30, and the weighting factor information related to the weighting factor estimated by the weighting factor estimation unit 31 described later are also personal information. Accumulated in the cloud server 25.

  The travel route information is information related to the travel route of the automobile C stored in the cloud server 25, and is a speed limit, whether the vehicle is a dedicated road, the number of lanes of the travel route, the curvature of the curve, the gradient, the type of road surface (gravel road , Paved roads, etc.) and road surface conditions (immediately after completion of repair work, rough roads, etc.), past accident histories on the road, accident histories on the road for each user H, and the like.

  In the weighting factor estimating unit 31, the standard weighting factor is adjusted according to the vehicle information, the personal information of the user H, and the travel route information. Here, the required service differs depending on the operation mode. Therefore, the amount of adjustment to the standard weighting coefficient varies depending on the operation mode even for the same user state, vehicle information, user H personal information, and travel route information. With reference to FIG. 2, the adjustment of the standard weighting factor will be described using vehicle information as an example.

  FIG. 2A is a diagram illustrating adjustment of the weighting factor by the weighting factor estimating unit 31 according to the vehicle information in the safe driving mode, and FIG. 2B is a diagram corresponding to the vehicle information in the driving skill maintaining mode. FIG. 6 is a diagram illustrating adjustment of a weighting factor by the weighting factor estimating unit 31. The arrows shown in FIG. 2 indicate which vehicle information is used to adjust the weighting factor for the state element. Note that both FIG. 2A and FIG. 2B show adjustment of weighting factors in the same user state and the same vehicle information.

  As shown in FIG. 2A, in the safe driving mode, the weighting coefficient of physical condition is adjusted according to the indoor temperature and the outdoor temperature in the vehicle information. Similarly, perceptual weighting factors are adjusted according to speed and acceleration, cognitive weighting factors are adjusted according to acceleration, and memory and emotional weighting factors are adjusted according to position information.

  On the other hand, as shown in FIG. 2 (b), in the driving skill maintenance mode, the weighting coefficient of physical condition is adjusted according to the room temperature and the outdoor temperature, and the weighting coefficient of perception is adjusted according to the speed and acceleration, The memory and emotion weighting factors are adjusted according to the position information. The cognitive weighting factor is adjusted according to speed and acceleration, the emotional weighting factor is adjusted according to position information, and the intentional weighting factor is adjusted according to acceleration and position information.

  That is, the weight factors of physical condition, perception and emotion are adjusted by the same vehicle information in both operation modes, while the weight factors of recognition are adjusted according to the acceleration in the safe driving mode, while the driving skill maintenance mode Then, it is adjusted according to acceleration and speed. Therefore, the vehicle information related to the adjustment of the weighting factor varies depending on the operation mode.

  Further, since the required services differ depending on the operation mode, the adjustment amount of the weighting factor is different even for the same user state and vehicle information. For example, the weighting factor for physical condition is adjusted according to the indoor temperature and the outdoor temperature in both the safe driving mode and the driving skill maintenance mode. In the safe driving mode, the weighting factor increases by “2” from “5” to “7”. On the other hand, in the driving skill maintenance mode, the weight coefficient is adjusted by decreasing “1” from “5” to “4”.

  In addition, when the room temperature and the outdoor temperature are appropriate, the adjustment amount of the weighting factor for physical condition is set small (for example, “0” or “+1”), and the room temperature and the outdoor temperature are extremely high (for example, 38 ° C.). Alternatively, when the temperature is low (for example, −10 ° C.), there is a possibility that the physical condition of the user H may vary depending on the indoor temperature and the outdoor temperature. Therefore, the adjustment amount of the weighting factor for the physical condition is set large (for example, “+4”). Or “+5”).

  As described above, depending on the operation mode, even in the same user state, the types of vehicle information related to the adjustment of the weighting factor are different, and even in the same user state and the same vehicle information, the adjustment is performed. Since the amount of adjustment of the weighting factor is different, it is possible to adjust the weighting factor in detail according to the operation mode.

  2A and 2B show an example of adjustment of the weighting factor based on the vehicle information, but the weighting factor is similarly adjusted according to the personal information of the user H and the travel route information. Then, the final weighting factor is estimated by integrating the weighting factor adjustment result based on the vehicle information, the weighting factor adjustment result based on the personal information, and the weighting factor adjustment result based on the travel route information. .

  When integrating the adjustment results of the weighting factors, the average value of the adjustment results of the respective weighting factors may be used as a final weighting factor, and further weighting factors are set for vehicle information, personal information, and travel route information. The result of weight calculation between the weight coefficient and the adjustment result of each weight coefficient may be the final weight coefficient. Further, the present invention is not limited to the configuration in which the weight coefficient is adjusted for each of the vehicle information, the personal information, and the road information, and the adjustment result of the weight coefficient is integrated, and all elements of the vehicle information, the personal information, and the road information are included. The weighting factor may be adjusted based on the above.

  Furthermore, when there is information that cannot be acquired among vehicle information, personal information, and travel route information, based on the latest information that can be acquired most recently in the information that cannot be acquired, or preset default values of the information that cannot be acquired The weighting factor may be adjusted. If vehicle information cannot be acquired, the weighting factor may be adjusted based on the personal information and the travel route information. If personal information cannot be acquired, the weighting factor may be adjusted based on the vehicle information and the travel route information. If the road information cannot be acquired, the weighting factor may be adjusted based on the vehicle information and the personal information. If vehicle information and personal information cannot be obtained, the weighting factor may be adjusted based on the travel route information. If vehicle information and travel route information cannot be obtained, the weighting factor may be adjusted based on the personal information. If the personal information and the travel route information cannot be acquired, the weighting factor may be adjusted based on the vehicle information.

  Returning to FIG. The weighting factor estimated by the weighting factor estimation unit 31 is transmitted to the cloud server 25 in addition to being output to the service estimation unit 32 described later. The weighting coefficient is analyzed in the cloud server 25, and the result is accumulated in the personal information of the user H and used for adjustment of the weighting coefficient from the next time.

  The service estimation unit 32 is a calculation unit that estimates a service based on the user state estimated by the state estimation unit 30 and the weighting factor estimated by the weighting factor estimation unit 31. A vehicle device 33 that operates each part of the vehicle is mounted on the automobile C. The vehicle device 33 includes an LCD 18, a speaker 19, an air conditioner 20, a lighting device 21, a traveling device 22, and a steering device 23, which will be described later with reference to FIG. included.

  As the types of services estimated by the service estimation unit 32, “LCD” for setting the display contents of the LCD 18 and the brightness of the backlight, and “music” for setting the music tune output from the speaker 19 and its volume, etc. Switching between cold air / warm air of the air conditioner 20, “air conditioning” for setting the air volume, “lighting” for setting the brightness and color of the lighting device 21, and the accelerator device and / or brake of the traveling device 22 “Accelerator / brake” that changes the characteristics of the device and “steering” that changes the steering characteristics of the steering device 23 are included. For each of these service types, a service content representing the operation content is estimated.

  As a service content estimation method, for example, when the perception and cognition in the user state is estimated to be “low” and the weighting coefficient of perception and cognition is “10”, the display on the LCD 18 does not hinder the driving of the user H. As described above, the backlight “dark” of the LCD 18 is set as the service content of the “LCD”. Further, in order to promote the improvement of the arousal level of the user H and improve the perception and recognition ability, “light music” that plays light music from the speaker 19 is set as the service content of “music”.

  Note that the types of services are not limited to LCD, music, air conditioning, lighting, accelerator / brake, and steering. For example, a fragrance generating device that generates “fragrance” is installed, and motion sickness is generated from the fragrance generating device. An aroma for prevention may be provided, or a vibration generating device that generates “vibration” may be provided, and when the user H's drowsiness is detected, the vibration generating device may provide vibration to the user H.

  In addition, the service content is not limited to what the user H can recognize, such as “light music”. It is good also as a structure which encourages operation assistance etc. For example, a configuration may be adopted in which low frequency or high frequency outside the audible range is output from the speaker 19 to alert the user H immediately before the car C approaches the curve, or the user 19 can hear the user H's semicircular canal from the speaker 19. It is also possible to output a very high frequency outside the area to prevent motion sickness. Moreover, it is good also as a structure which blinks the illuminating device 21 according to rhythms, such as a user's H heartbeat and respiration, and the reverse phase of a pulse wave, and raises the arousal level of the user H. Moreover, it is good also as a structure which gives "music", "lighting", etc. with respect to the specific site | parts (back head, abdomen, etc.) which the user H cannot perceive a sound and light directly. Thereby, it can be made to feel that it is easy to operate the motor vehicle C unconsciously with respect to the user H.

  And based on the service content for every kind of service estimated in the service estimation part 32, the vehicle apparatus 33 of the motor vehicle C is operated. Thus, since the vehicle apparatus 33 is operated based on the user state and the weighting factor, it is possible to provide the user H with a detailed service suitable for the operation mode and the user state.

  Furthermore, the biological information from the user H who has detected the operation of the vehicle device 33 is input to the biological sensor 16, and the operation of the vehicle device 33 is repeatedly performed from the state estimation unit 30. That is, whether the service is optimal / inappropriate for the operation of the vehicle device 33 by the service is evaluated based on the biometric information of the user H, and the weight coefficient is estimated by the weight coefficient estimation unit 31 according to the result. The vehicle device 33 is operated. As a result, as the service providing device 1 is used, a service more suitable for the user H can be provided.

  Next, the electrical configuration of the service providing apparatus 1 will be described with reference to FIGS. FIG. 3 is a block diagram showing an electrical configuration of the automobile C on which the service providing apparatus 1 is mounted. The service providing apparatus 1 is mounted on the car C, and the service providing apparatus 1 includes a CPU 10, a flash ROM 11, and a RAM 12, which are connected to the input / output port 14 via the bus line 13. .

  The input / output port 14 further includes a communication device 15 that transmits / receives data to / from the cloud server 25, a biosensor 16, a vehicle sensor 17, and a vehicle device 33 mounted on the automobile C, that is, for the user H. An LCD 18 for displaying information, a speaker 19 for outputting audio signals, an air conditioner 20 for adjusting the temperature in the car C by blowing out hot air or cold air, and an illumination device 21 for adjusting the brightness in the car C A traveling device 22 that travels the vehicle C according to the operation of the accelerator device and the brake device (both not shown), and a steering device 23 that steers the vehicle C according to the operation of the handle device (not shown), respectively. It is connected.

  The CPU 10 is an arithmetic device that controls each unit connected by the bus line 13. The flash ROM 11 is a rewritable nonvolatile storage device that stores programs executed by the CPU 10, fixed value data, and the like, and stores a control program 11a and standard weight coefficient data 11b. When the control program 11a is executed by the CPU 10, the main process of FIG. 6 is executed. The standard weight coefficient data 11b is a data table in which standard weight coefficients for the user state are stored for each operation mode of the service providing apparatus 1. The standard weight coefficient data 11b will be described with reference to FIG.

  The standard weighting coefficient data 11b stores a standard weighting coefficient for a user state state element set in advance for each operation mode of the service providing apparatus 1. The operation mode is mainly recognized by the aging and the like in the “safe driving mode” that contributes to safe driving by assisting the cognitive ability of the user H with the devices 18 to 23 and reducing the burden on the recognition of the user H. A “driving skill maintenance mode” is provided for assisting the driving operation of the car C of the user H whose ability and physical ability have declined by the devices 18 to 23, and a plurality of operation modes are also provided.

  The standard weighting factor for the safe driving mode is “5”, “10”, “1”, “10”, “5” for the physical condition, perception, memory, cognition, emotion, sensitivity, and intention of the state element, respectively. , “2” and “4” are set. For safe driving, “10” is set because perception and cognition are most important, and “physical condition”, “emotion”, and “intention” are moderately important, so “4” and “5” Is set. On the other hand, since “memory” and “sensitivity” are substantially irrelevant to safe driving, “2” and “1” are set.

  On the other hand, the standard weighting factors in the driving skill maintenance mode are “5”, “5”, “1”, “1” for the physical condition, perception, memory, cognition, emotion, sensitivity, and intention of the state element, respectively. “10”, “5”, “1” and “10” are set. In driving support, “cognition” and “intention” are most important, so “10” is set, and “physical condition”, “perception”, and “emotion” are moderately important. "Or" 5 "is set. On the other hand, “memory” and “sensitivity” are substantially irrelevant to driving operation support, and may be adversely affected, so “2” or “1” is set.

  Returning to FIG. The RAM 12 is a memory for the CPU 10 to store various work data, flags, and the like in a rewritable manner when the control program 11a is executed. The user state table 12a, the personal information memory 12b, the travel route information memory 12c, the vehicle An information memory 12d, a weight coefficient table 12e, and a service table 12f are provided.

  The user state table 12a is a data table in which state elements of the estimated user state are stored. Here, the user status table 12a will be described with reference to FIGS. 4B and 5A. FIG. 4B is a diagram for explaining the user state, and FIG. 5A is a diagram schematically showing the user state table 12a.

  As shown in FIG. 4B, the user state further has a plurality of states for each state element. In this embodiment, each state for each state element is symbolized and managed. Specifically, the state is symbolized by a combination of an alphabetic prefix and an integer corresponding to each state element. For example, the physical condition is symbolized by a combination of the prefix “C” and an integer. For example, “C1” is defined as a “very good” state, “C2” is defined as a “good” state, and the like. Similarly, perception is symbolized by a combination of the prefix “P” and an integer, “P1” is defined as a “high” state, “P2” is defined as a “medium” state, and the like.

  As shown in FIG. 5A, the user state table 12a is provided with a state memory 12a1 for storing a state symbol for each state element. A symbol representing the estimated state for each state element is stored in a corresponding memory area of the state memory 12a1.

  Returning to FIG. The personal information memory 12 b is a memory that stores the personal information of the user H acquired from the cloud server 25. As described above, from the cloud server 25, the sex, age group, height, weight, medical condition and history of chronic disease, personality tendency and personality test results, driving history, traffic accident history, location where user H has visited, Impressions and memories of places, and further, biometric information, user status, and weighting factors are transmitted. In the personal information memory 12b, these personal information transmitted from the cloud server 25 is stored so as to be distinguishable from each other.

  The travel route information memory 12 c is a memory that stores travel route information of the automobile C acquired from the cloud server 25. As described above, from the cloud server 25, the speed limit, whether it is an automobile-only road, the number of lanes, the curvature of the curve, the slope, the type of road surface, the state of the road surface, the history of accidents on the road, and the history of each user H A history of accidents on the road is transmitted. The travel route information memory 12c stores the travel route information transmitted from the cloud server 25 so that they can be distinguished from each other.

  The vehicle information memory 12 d is a memory that stores vehicle information of the automobile C acquired from the vehicle sensor 17. As described above, the speed, acceleration, position information, angular velocity, angular acceleration, room temperature, outdoor sound, and the like of the automobile C are acquired from the vehicle sensor 17. The vehicle information memory 12d stores these vehicle information in a distinguishable manner.

  The weighting factor table 12e is a memory that stores a standard weighting factor acquired from the standard weighting factor data 11b and a weighting factor adjusted from the standard weighting factor. The weighting coefficient table 12e will be described with reference to FIG. FIG. 5B is a diagram schematically showing the weighting coefficient table 12e. As shown in FIG. 5B, the weighting coefficient table 12e is provided with a weighting coefficient memory 12e1 that stores a weighting coefficient for each state element in the user state. The standard weighting factor acquired from the standard weighting factor data 11b and the weighting factor estimated by the above-described weighting factor estimation unit 31 are stored in the corresponding memory area of the weighting factor memory 12e1.

  Returning to FIG. The service table 12f is a data table that stores service contents provided to the user H for each type of service. The service table 12f will be described with reference to FIG. FIG. 5C is a diagram schematically showing the service table 12f. As shown in FIG. 5C, a service content memory 12f1 for storing the service content to be provided to the user H is provided for each type of service (LCD, music, air conditioning, accelerator / brake, steering, lighting). . The service content for each estimated service type is stored in the corresponding memory area of the service content memory 12f1.

  Next, a main process executed by the CPU 10 of the service providing apparatus 1 will be described with reference to FIG. FIG. 6 is a flowchart of the main process of the service providing apparatus 1. The main process is performed immediately after the service providing apparatus 1 is turned on.

  In the main process, first, biometric information of the user H (facial expression, line of sight, eye movement, voice, posture, brain wave, cerebral blood flow, blood pressure, heart rate, body temperature, blood oxygen, sweating, etc.) is acquired from the biosensor 16 (S1). ), The user state is estimated from the acquired biological information of the user H, and the resulting state element is stored in the state memory 12a1 of the user state table 12a (S2). The process of S2 corresponds to the process in the state estimation unit 30 described above with reference to FIG. In addition, since the estimation method of the user state in the process of S2 is the same as the estimation method of the user state in the state estimation unit 30, the description thereof is omitted.

  After the process of S2, the estimated user state is transmitted to the cloud server 25 (S3). The user state transmitted to the cloud server 25 is analyzed by the cloud server 25 and accumulated in personal information for the user H in the cloud server 25. After the process of S3, the vehicle information of each part of the automobile C is acquired from the vehicle sensor 17 and stored in the vehicle information memory 12d (S4), the personal information of the user H is acquired from the cloud server 25, and stored in the personal information memory 12b. Save (S5).

  After the process of S5, the operation mode of the automobile C is confirmed (S6). Here, the operation mode of the automobile C is set in advance by the user H via an input device (not shown) of the service providing apparatus 1. Note that the operation mode is not limited to being set via the input device, and may be set according to the “intent” of the state element estimated in the process of S2, for example. Thereby, even when the user H is driving the automobile C, it is possible to switch to a desired operation mode at a desired timing.

  In the process of S6, when “safe driving mode” is set as the operation mode (S6: “safe driving mode”), the weighting factor of the safe driving mode is obtained from the standard weighting factor data 11b and the weighting factor table 12e. If the driving skill maintenance mode is set as the operation mode (S6: “driving skill maintenance mode”), the weighting factor of the driving skill maintenance mode is obtained from the standard weighting factor data 11b and weighted. Save to the coefficient table 12e (S8). If an operation mode other than the safe driving mode and the driving skill maintenance mode is set (S6: “Other”), the weighting coefficient of the corresponding operation mode is obtained from the standard weighting coefficient data 11b, and the weighting coefficient is obtained. Save to the table 12e (S9).

  After the processes of S7 to S9, the user status stored in the user status table 12a, the personal information of the user H stored in the personal information memory 12b, and the vehicle of the vehicle C stored in the vehicle information memory 12d Based on the information, each weight coefficient in the weight coefficient table 12e is adjusted (S10). The processes in S6 to S10 described above correspond to the process in the weight coefficient estimation unit 31 described above with reference to FIG. Note that the adjustment method of the weighting factor in the process of S10 is the same as the adjustment method of the weighting factor in the weighting factor estimating unit 31, and therefore the description thereof is omitted.

  After the processing of S10, the service content for each service type is estimated from the user status stored in the user status table 12a and the weighting factor stored in the weighting factor table 12e, and the service content in the service table 12f Save in the memory 12f1 (S11). The process of S11 corresponds to the process in the service estimation unit 32 described above with reference to FIG. The service content estimation method in the process of S11 is the same as the user state estimation method in the service estimation unit 32, and thus the description thereof is omitted.

  After the processing of S11, the vehicle device 33, that is, the LCD 18, the speaker 19, the air conditioner 20, the lighting device 21, the traveling device 22, and the steering device 23 are operated based on the value of the service content memory 12f1 of the service table 12f ( S12). Therefore, since the vehicle apparatus 33 is operated according to the service content estimated based on the user state of the user state table 12a and the weighting factor of the weighting factor table 12e, the operation mode and the user state are determined for the user H. Detailed service suitable for

  After the process of S12, the biological information is acquired again from the biological sensor 16 (S13), and it is determined from the acquired biological information whether the user H feels optimal (good) (S14). That is, it is determined how the user H feels about the service performed in the process of S12. As a method of determining that the user H is optimal in the process of S14, the user state is estimated from the biological information acquired in the process of S13, and whether or not the estimated user state is optimal is determined, as in the process of S2. Is done. Specifically, when the state element “sensitivity” of the user state is determined to be “feeling good”, the user H is determined to be optimal.

  It should be noted that the user H may be determined to be optimal depending on the state elements of other user states, the state element “sensitivity” is determined to be “feeling good”, and the state element “emotion” is “joy”. When it is determined that the user H is optimal, the user H may be determined to be optimal from a plurality of state elements of the user status.

  As a result of the process of S14, when the user H feels optimal (S14: Yes), the service by the process of S12 is determined to be optimal for the user H at the present time, and thus represents “optimum service”. Along with the information, the biometric information acquired in the process of S13 and the weighting coefficient of the weighting coefficient table 12e are transmitted to the cloud server 25 (S15), and the processes in S12 and subsequent steps are repeated. Thereby, since the service content which the user H feels optimal is repeated, the service which the user H feels optimal can be provided continuously.

  In addition, the weighting factor that causes the service that the user H feels optimal is transmitted to the cloud server 25 together with the biological information. In the cloud server 25, the received biometric information, information indicating “optimum service”, and a weighting factor are stored in association with each other. Then, in the estimation of the weighting factor by the processing of S6 to S10 after the next time, the weighting factor acquired from the cloud server 25 is preferentially applied to the adjustment of the weighting factor from the “optimum service”. That is, the weighting coefficient that has been determined to be optimal for the user H before is used preferentially for adjustment of the weighting coefficient. As a result, the service adjusted with the weighting factor can be a service more in accordance with the tendency or preference of the user H.

  On the other hand, as a result of the process of S14, when the user H feels that it is not optimal (S14: No), it is determined that the service by the process of S12 is inappropriate for the user H at the present time. The biometric information acquired in the process of S13 and the weighting coefficient of the weighting coefficient table 12e are transmitted to the cloud server 25 together with the information indicating that the service is “Non-Service” (S16), and the weighting coefficient by the processing of S6 to S10 is transmitted. In order to perform the estimation again, the processes after S1 are repeated.

  Here, the weighting coefficient that has become a factor of the service that the user H feels inappropriate is also transmitted to the cloud server 25 together with the biological information. In the cloud server 25, the received biometric information, information indicating “inappropriate service”, and a weighting factor are stored in association with each other. That is, after the process of S16, when the weighting coefficient is estimated again by the processes of S6 to S10, the weighting coefficient that is the “optimum service” is preferentially applied to the adjustment of the weighting coefficient. The weighting factor of the service that is felt as “near service” is not used for the adjustment of the weighting factor. Therefore, the possibility that the user H feels optimal for the service based on the weighting coefficient estimated after the next time can be increased.

  As described above, the biometric information and the weighting coefficient are transmitted to the cloud server 25 together with information representing the optimum service / unsuitable service of the user H with respect to the service performed by the devices 18 to 23, and the analysis result is the next and subsequent times. It is used to estimate the weighting factor. That is, so-called “feedback control” is performed in which the weight coefficient adjustment method is changed by evaluating the service via the user H. As a result, the weighting factor of the service that the user H feels optimal is preferentially used for the adjustment of the weighting factor from the next time onward, so that the service that the user H feels optimal can be provided with high accuracy.

  Also, by executing a large number of such feedback controls, a large number of weighting factors of services that the user H feels optimal are accumulated in the cloud server 25. That is, since the choice of the weighting factor of the service that the user H feels optimal is increased in the cloud server 25, the accuracy of adjustment of the weighting factor is improved correspondingly, and the service that the user H feels optimal is more accurate. Can be provided.

  Although the present invention has been described based on the embodiments, the present invention is not limited to the above-described embodiments, and various improvements and modifications can be easily made without departing from the spirit of the present invention. Can be inferred.

  In the above embodiment, the service providing device 1 is mounted on the automobile C. However, the present invention is not necessarily limited to this, and the service providing apparatus 1 may be mounted on a motorcycle other than the automobile C, an electric wheelchair, a construction machine, or an agricultural machine. The service providing apparatus 1 may be applied to an apparatus installed in an office. In such a case, the user state of the user H in the office is estimated, and the brightness and color of the lighting device in the office, the brightness of the backlight of the information terminal such as a PC, and the output from the speaker in the office according to the user state What is necessary is just to set it as the structure which controls indoor BGM etc. to be performed.

  In the above embodiment, the user H is the driver of the car C. However, instead of this, the passenger of the car C may be the user H, and the service may be provided based on the biological information and personal information of the passenger.

  In the embodiment described above, the cloud server 25 is configured to store the personal information and travel route information of the user H. However, the present invention is not necessarily limited to this, and the personal information and the travel route information of the user H are accumulated in a server on the network other than the cloud server 25 (for example, a data server configured by a single device). Also good. Further, the personal information of the user H may be stored in the flash ROM 11 of the service providing apparatus 1.

  In the above embodiment, the standard weight coefficient data 11b is stored in the flash ROM 11 of the service providing apparatus 1. However, the present invention is not necessarily limited to this, and the standard weight coefficient data 11b may be stored in the cloud server 25, and the necessary standard weight coefficient may be acquired from the cloud server 25 in the processes of S6 to S9.

1 Service Providing Device 11a Control Program (Service Providing Program)
16 Biological sensor (detection means, part of detection function)
17 Vehicle sensor (vehicle information acquisition means)
18 LCD (display device)
19 Speaker (sound generator)
20 air conditioner 21 lighting device 22 travel device (accelerator device, brake device)
23 Steering device 25 Cloud server (external device)
C Automobile (vehicle)
H user (target person, driver)
S1, S13 Detection means, part of detection function S2 estimation means, estimation function S5 external information acquisition means S10 weighting means, weighting function S12 control means, control functions S12, S13, S14, S16 Repeat control means S15, S16 External accumulation means

Claims (11)

Detection means for detecting the biological information of the subject;
Based on the biological information detected by the detecting means, the estimating means for estimating the subject's state divided into a plurality of elements;
A weighting unit that selects an element according to a service to be provided from a plurality of elements estimated by the estimation unit, and performs weighting according to the service with respect to the selected element;
Control means for controlling each device according to the weighting result by the weighting means,
A service providing apparatus for providing the service to the target person by the operation of each apparatus controlled by the control means. The detection means redetects the biological information of the subject who is receiving the service,
2. The service providing apparatus according to claim 1, wherein the weighting unit inputs and accumulates the re-detected biometric information, and corrects the weighting according to the service based on the accumulated information. .   3. The service according to claim 2, further comprising: a repeat control unit that repeats control of each of the devices being executed by the control unit when the biometric information redetected by the detection unit is good. Providing device. The service providing device is mounted on a vehicle,
Vehicle information acquisition means for acquiring vehicle information,
4. The service providing apparatus according to claim 1, wherein the weighting unit adjusts the weighting based on the vehicle information acquired by the vehicle information acquisition unit. 5. External information acquisition means for acquiring external information stored in the external device,
5. The service providing apparatus according to claim 1, wherein the weighting unit adjusts the weighting based on the external information acquired by the external information acquisition unit.   The service providing apparatus according to claim 5, wherein the external information acquired by the external information acquisition unit is information corresponding to the target person. The service providing device is mounted on a vehicle,
7. The service providing apparatus according to claim 5, wherein the external information acquired by the external information acquisition unit is information corresponding to a travel route on which the vehicle is traveling. The detection means redetects the biological information of the subject who is receiving the service,
8. The service providing apparatus according to claim 5, further comprising an external storage unit that outputs and stores the re-detected biological information to the external apparatus. The service providing device is mounted on a vehicle,
The control means provides a service to the target person by controlling at least one of a display device, a sounding device, an air conditioner, a lighting device, an accelerator device, a brake device, and a steering device as each device. The service providing apparatus according to any one of claims 1 to 8, wherein The service providing device is mounted on a vehicle,
The service providing apparatus according to claim 1, wherein the target person of the service is a driver or a passenger. A detection function for detecting biological information of the subject person;
Based on the biological information detected by the detection function, an estimation function for estimating the state of the subject divided into a plurality of elements,
A weighting function that selects an element according to a service to be provided from a plurality of elements estimated by the estimation function and weights the selected element according to the service;
A control function for controlling each device according to a weighting result by the weighting function is realized by a computer, and the service is provided to the target person by an operation of each device controlled by the control function. Service provision program.

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