Service provision system and human model production method

Abstract

To provide a service that matches a user by personalizing a service to be provided.SOLUTION: During a dialogue using a dialogue model for general dialogue (NO in S235-S246, S247, No in S249), the dialogue model is switched to a task processing model in accordance with the content of the dialogue (NO in S247, S248), to allow a user to utilize the knowledge dedicated to a specific field of the task processing model. Through dialogues with individual users using dialogue models, the dialogue models are personalized to dialogue models suitable for the individual users (YES in S245, S246).SELECTED DRAWING: Figure 20

Description

[Technical field]

[0001] The present invention relates to a support system that utilizes artificial intelligence to support a user in repeating practices for self-improvement. [Background technology]

[0002] In order to perform machine learning using artificial intelligence, there have been methods that create learning data from multiple information sources (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-232997 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, machine learning requires a huge amount of training data, and in order to create this vast amount of training data, it is necessary to collect a large amount of necessary information from sources. In order to put machine learning to practical use, it is essential to reduce the effort and cost required for collecting this information.

[0005] Furthermore, for example, a general model created by collecting large amounts of information from an unspecified number of people and performing machine learning using the huge amount of learning data tends to become an average model for the entire unspecified number of people, and if that general model is used to provide a service to each user, each of whom has individual differences, there is a risk that the service will not be a good match for users who deviate from the average.

[0006] The present invention has been devised in view of the above-mentioned circumstances, and has as its purpose the problem of a service using a general model resulting from machine learning not being a good match for a user.

[0007] Next, an example of the correspondence between various means for solving the problems and the embodiments will be shown below in parentheses.

[0008] The present invention is a support system (e.g., Figs. 9 and 10, etc.) that uses artificial intelligence to support a user's repeated practice (e.g., rote learning, muscle training, etc.) for self-improvement, A data collection means (e.g., S182, S211, etc.) for collecting data of a plurality of users who perform the practice; A storage means (e.g., an artificial intelligence DB 17) for storing a general model (e.g., a rote learning model Ti=T0·i, a muscle training model Fi=F0+(i-1) / a, etc.) generated by performing machine learning (e.g., reinforcement learning, regression, etc.) based on the data of multiple users collected by the data collection means; A personalized model generating means (e.g., S191 and S204, S223 and S228, etc.) that generates a personalized model personalized for an individual user based on the general model stored in the storage means and the individual data for one user collected by the data collecting means; A practice content specification means (e.g., S204, S228, etc.) that specifies practice content to be executed when the individual user repeatedly performs a practice by using the personalized model generated by the personalized model generation means; a service providing means (e.g., S206, S229, etc.) for providing a service for the individual user to carry out the practice content identified by the practice content identifying means (e.g., providing study items based on the most efficient review plan, providing a muscle training menu based on the most efficient muscle training plan, etc.), The general model is a model that is commonly applicable to multiple users and is generated to make the repeated practice more efficient (e.g., the rote learning model Ti=T0·i in FIG. 15, the muscle training model Fi=F0+(i−1) / a, etc.), The personalized model is a model created specifically for an individual user to streamline the repeated practice of the individual user (eg, S204, S228, etc.).

[0009] This configuration can solve the problem of a service using a general model resulting from machine learning not matching the user.

[0010] Preferably, the general model is a model generated by using factors that differ for each user (e.g., individual ability differences such as memorization ability, initial load for muscle training, load increase coefficient, preferences, etc.) as constants (e.g., T0, F0, a, etc.) (e.g., S189, S214, FIG. 15, etc.), The personalized model generation means A value derivation means (e.g., S191, S223, etc.) for deriving actual values ​​(e.g., TK, FK, az, CT, etc.) of the individual user that fit into the constant part in the general model based on information sent from the individual user; and a substitution means (eg, S204, 228, etc.) for substituting the actual value derived by the value derivation means into a constant part in the general model to generate the personalized model.

[0011] Preferably, the data collection means collects data of a plurality of users sent from a professional company (eg, professional company 71 in FIG. 9) that provides the practice to users. [Brief description of the drawings]

[0012] [Figure 1] This is a system diagram showing the overall configuration of an IoT device intermediation system and a service provision system using machine learning. [Diagram 2] FIG. 1A is a block diagram showing a control circuit of an IoT device, and FIG. 1B is a block diagram showing a control circuit of a mobile communication device. [Diagram 3] FIG. 1 is a block diagram showing a control circuit for a PC or a server. [Figure 4]4 is a flowchart showing the main routine program of the mobile communication device and the artificial intelligence server. [Diagram 5] 1 is a flowchart showing a subroutine program for IoT processing and a flowchart for an IoT device (sensor) and an IoT server. [Figure 6] 1 is a flowchart showing a subroutine program for IoT processing and a flowchart for an IoT device (actuator) and an IoT server. [Figure 7] 1 is a flowchart showing a subroutine program for IoT processing and a flowchart for an IoT device (actuator) and an IoT server. [Figure 8] 1 is a flowchart showing a subroutine program for IoT processing and a flowchart for a wireless sensor network and an IoT server. [Figure 9] FIG. 1 is an explanatory diagram showing an overview of a service providing system that uses machine learning. [Figure 10] FIG. 1 is a functional block diagram showing a service providing system using machine learning. [Figure 11] 13 is a flowchart showing a subroutine program for external display processing and display processing, and a flowchart for a digital menu board. [Figure 12] 13A is a flowchart showing a subroutine program for processing expert data, and FIG. 13B is a flowchart showing a subroutine program for processing maintenance data and a flowchart for a PC of a maintenance professional. [Figure 13] 1A is a flowchart showing a subroutine program for around data terminal processing and around data server processing, and FIG. 1B is a flowchart showing a subroutine program for personal service terminal processing and personal service server processing. [Figure 14] 13 is a flowchart showing a subroutine program of a user learning terminal process and a user learning server process. [Figure 15]FIG. 1 is an explanatory diagram illustrating a general model creation method. [Figure 16] 13 is a flowchart showing a subroutine program of a learning service terminal process and a learning service server process. [Figure 17] 13 is a flowchart showing a subroutine program for processing muscle training data and a flowchart of a PC of a muscle training professional. [Figure 18] 1A is a flowchart showing a subroutine program for muscle training terminal processing and muscle training server processing, and FIG. 1B is a flowchart showing a subroutine program for muscle training service terminal processing and muscle training service server processing. [Figure 19] FIG. 1A is a diagram showing a specific example of a dialogue model stored in an artificial intelligence DB, and FIG. 1B is a diagram showing a specific example of personalized data for dialogue stored in a user DB. [Figure 20] 6 is a flowchart showing a subroutine program of an interactive terminal process and an interactive server process. [Figure 21] FIG. 2A is a diagram showing a specific example of a task processing model stored in an artificial intelligence DB, and FIG. 2B is a diagram showing a specific example of personalized data for task processing stored in a user DB. [Figure 22] 4 is a flowchart showing a subroutine program of a task terminal process and a task server process. [Figure 23] 4 is a flowchart showing a subroutine program of a task terminal process and a task server process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] [IoT device intermediation system] First, the IoT device intermediation system will be described with reference to Fig. 1 to Fig. 8. IoT is an abbreviation for Internet of Things, in which things are networked by the Internet Protocol and send signals to the Internet by themselves. The IoT device intermediation system is a system in which a user's mobile communication device 3 mediates communication with a group 2 of IoT devices (various sensors and actuators for IoT, etc.) that do not have the function to connect to the Internet and communicate, and connects the group 2 of IoT devices to the Internet 1 via the mobile communication device 3 to enable communication.

[0014] Referring to the overall system of FIG. 1, a mobile communication device 3, such as a wearable computer of a user, a robot 6 in the user's home, a personal computer (hereinafter referred to as "PC") 7 in the user's home, an IoT server 8, an artificial intelligence server 9, PCs 10 of various specialists, and an SNS server 11 are connected to the Internet 1 and are configured to be able to communicate with each other. The mobile communication device 3 has a function for communicating with a group of IoT devices 2, a wireless sensor network 4, and a group of displays 5 such as digital menu boards. Assumed communication methods include, for example, Wi-Fi (registered trademark), Bluetooth (registered trademark), Wi-Fi Direct (registered trademark), Zigbee (registered trademark), Z-wave (registered trademark), and Ant+ (registered trademark). In addition, the OS supports iOS, Android (registered trademark), Linux (registered trademark), TIZEN (registered trademark), and other real-time OS. IEEE802.15.4 is adopted as the wireless standard for transmission and reception. In addition, IPv6 (Internet Protocol Version 6) is adopted as the Internet Protocol.

[0015] A wireless sensor network 4 is a wireless network that allows multiple sensor-equipped wireless terminals to be distributed in space and to collect environmental and physical conditions in cooperation with each other. For example, a sensor device is created using energy harvesting, M2M, or batteries, and pressure or gauge sensors are used to constantly monitor, for example, deterioration of metal fatigue, and to report any changes. It is mainly installed in structures such as bridges and tunnels. In general, it includes multiple sensor nodes and a gateway sensor node. These nodes are usually composed of one or more sensors, a wireless chip, a microprocessor, and a power source (battery, etc.). The hardware configuration of the node's control circuit is the same as that shown in Figure 2(a). A wireless sensor network usually has an ad hoc function and a routing function (routing algorithm) for sending data from each node to a central node. In other words, it has the function of autonomously reconstructing another communication path when a communication failure occurs between nodes. There is also an element of distributed processing because the nodes work together as a group. In addition, it has the function of operating for a long period of time without receiving power from an external source, and therefore has a power saving function or a self-powering function. In this embodiment, the wireless sensor network 4 is a type of the IoT device group 2.

[0016] The IoT server 8 can write and read data to and from the IoT device DB 15, which is a database of IoT devices (hereinafter referred to as "DB"). The artificial intelligence server 9 can write and read data to and from the artificial intelligence DB 17, the user DB 12, and the learning DB 60. The SNS server can write and read data to and from the SNS DB 13.

[0017] Next, the hardware configuration of the control circuit of the IoT device 2 will be described with reference to FIG. 2(a). The IoT device group 2 is sensors and actuators installed all over the earth, and includes a CPU (Central Processing Unit) 18 for controlling the whole, a ROM (Read Only Member) 20 storing programs for executing various functions, a RAM (Random Access Memory) 19 which is a work area for the CPU 18, and a wireless communication interface unit 22 using, for example, Wi-Fi, Bluetooth, Wi-Fi Direct, Zigbee, Zwave, Ant+, etc. If the IoT device 2 is a sensor, it includes a sensor unit 14. On the other hand, if the IoT device 2 is an actuator, an actuator unit 21 is provided instead of or in addition to the sensor unit 14. The CPU 18, the RAM 19, the ROM 20, the wireless communication interface unit 22, the sensor unit 14, and the actuator unit 21 are connected so as to be able to exchange signals.

[0018] Next, referring to Fig. 2(b), a hardware configuration of a control circuit of a wearable computer, which is an example of the mobile communication device 3, will be described. This wearable computer 3 is typified by smart glasses or the like, and a CPU 23, a RAM 24, a ROM 25, and an EEPROM (Electronically Erasable and Programmable Read Only Memory) 26 are connected via a bus 27. The functions of the CPU 23, the RAM 24, and the ROM 25 are basically the same as those described above for the IoT device 2. The EEPROM 26 stores application programs downloaded via the Internet 1.

[0019] Various devices are connected to the bus 27 via an interface unit 28. For example, a digital video camera input unit 29 that captures an image of the user's surroundings (such as an image in the user's line of sight), a display unit 30 that overlays information on the lens of the smart glasses, a wireless communication processing unit 31 that wirelessly communicates with a base station and communicates data with a server or the like via the Internet 1, an input operation unit 32 that causes the CPU 23 of the wearable computer 3 to execute a desired function by the user, a voice output unit 33 and a voice input unit 34 that allow the user to make a call by voice, a location information acquisition unit 35 that acquires a current location based on GPS information from a satellite, radio waves from a base station, and radio waves from a wireless LAN access point, a wireless communication interface unit 36 ​​that communicates with the IoT device group 2 using Wi-Fi, Bluetooth, Wi-Fi Direct, Zigbee, Zwave, Ant+, etc., a gaze position detection unit 37 that detects the user's gaze position and identifies the position of the user's gaze in the image captured by the digital video camera input unit 29, various sensors 38, and the like are connected to the interface unit 28.

[0020] The input operation unit 32 not only accepts manual operations by the user, but also accepts instructions by the user's gestures, specific winks, etc. Note that voice instructions by the user are accepted by a voice input unit 34. The various sensors 38 are sensors for temperature, humidity, illuminance, ultraviolet light, etc., attached to the wearable computer 3. Note that the hardware configuration of the robot's control circuit would be the same as that shown in Fig. 2(a) except that the various sensors 38 are deleted and a movement drive circuit is added.

[0021] Next, the hardware configuration of the control circuits of the PC and various servers 7 to 11 will be described with reference to Fig. 3. As described above, a CPU 40, a RAM 41, and a ROM 42 are connected by a bus 43. An interface unit 44 to which the bus 43 is connected is connected to a communication unit 45 with the Internet 1, etc., a display unit 46 that displays images and information to an operator, and an input operation unit 47 that receives operations from the operator.

[0022] Next, a flow chart of the main program of the control process executed by the CPU 23 of the mobile communication device 3 and the CPU 40 of the artificial intelligence server 9 will be described with reference to Fig. 4. The mobile communication device 3 performs IoT processing at S1. This is processing for connecting the IoT device group 2 and the Internet 1 via the mobile communication device 3, transmitting detection data of the IoT device (sensor) 2 to the IoT server 8, and transmitting a command signal (actuator control signal) from the IoT server 8 to the IoT device (actuator) 2.

[0023] Next, the mobile communication device 3 performs external display processing at S2, while the artificial intelligence server 9 performs display processing at S3. This is processing for displaying videos, still images, etc. uploaded by the user to the Internet on a display 5 such as a digital menu board. Next, the mobile communication device 3 performs around data terminal processing at S4, while the artificial intelligence server 9 performs around data server processing at S5. This is processing for collecting video and audio around the user by the mobile communication device 3 such as a wearable computer in the artificial intelligence server 9, and using the collected video and audio data as information for machine learning.

[0024] Next, the mobile communication device 3 performs personal service terminal processing at S7, and the artificial intelligence server 9 performs personal service server processing at S8. This is processing for providing the user with personalized services by utilizing the results of machine learning by the artificial intelligence. Next, the mobile communication device 3 performs dialogue terminal processing at S9, and the artificial intelligence server 9 performs dialogue server processing at S10. This is processing for the user and the artificial intelligence to have various dialogues. Next, the mobile communication device 3 performs task terminal processing at S11, and the artificial intelligence server 9 performs task server processing at S12. This is processing for the artificial intelligence that has completed machine learning to respond to the user's work-related questions and inquiries.

[0025] Next, a flowchart of the subroutine program of the IoT processing shown in S1 above will be described with reference to Figures 5 to 8. First, with reference to Figure 5, the processing performed by the IoT device (sensor) 2, the mobile communication device 3, and the IoT server 8 will be described.

[0026] The CPU 18 of the IoT device (sensor) 2 performs a process of storing the detection data by the sensor unit 14 in the RAM 19 in S20, and determines whether or not a transmission command signal has been received in S21. If not, the process returns to S20 and repeatedly goes through the loop of S20→21→S20.

[0027] Meanwhile, in S22, the CPU 23 of the mobile communication device 3 transmits location information based on GPS information, radio waves from a base station, wireless radio waves from a wireless LAN access point, etc. to the IoT server 8. The IoT server 8 receives the location information in S24 and searches the IoT device DB 15 in S25, and determines in S26 whether or not an IoT device 2 to receive data from is present in the vicinity of the mobile communication device 3 that transmitted the location information. Ideally, the IoT server 8 periodically collects detection data of the IoT device (sensor) 2, and searches the IoT device DB 15 to identify an IoT device (sensor) 2 for which the periodic reception is due. If an IoT device 2 to receive data from is present, a YES determination is made in S26, and control proceeds to S27, where a command signal for reception is transmitted to the mobile communication device 3.

[0028] On the other hand, if there is no IoT device 2 to receive the command signal, NO is determined in S26 and control proceeds to S38 to determine whether there is an IoT device (actuator) 2 to which a command signal (actuator control signal) is to be transmitted in the vicinity. If there is, control proceeds to S39 to transmit a command signal for transmission and transmission information (information for controlling the actuator unit 21) to the mobile communication device 3. The mobile communication device 3 that has received a command signal in S27 or S39 determines YES in S23 and control proceeds to S28 to determine whether the received command signal is for reception. If the command signal is for transmission transmitted in S38, control proceeds to S47, but if the command signal is for reception transmitted in S27, control proceeds to S29 to transmit a transmission command signal to a nearby IoT device (sensor) 2.

[0029] The mobile communication device 3 receives the data in S21, and transmits the detection data and device ID stored in the RAM to the mobile communication device 3 in S30, and returns to S20. The mobile communication device 3 receives the data in S31, and transmits the received data to the IoT server 8 in S32. The mobile communication device 3 receives the data in S33, and updates the stored data of the IoT device corresponding to the received device ID, among the stored data in the IoT device DB 15, to the received data in S34.

[0030] Next, the IoT server 8 transmits points as an example of a benefit to the mobile communication device 3 in S35, and then control proceeds to S61. The mobile communication device 3 receives the points in S36, updates the points stored in the EEPROM 26 (S37), and then control proceeds to S60. The points stored in the EEPROM 26 can be used to receive various services from the company that operates the IoT server 8. For example, the points can be used to pay highway tolls, or to purchase JR or private railway tickets or commuter passes. The points may also be redeemable for cash. This can provide an incentive for users to broker IoT devices.

[0031] On the other hand, if the result of S38 is NO, control proceeds to S40, where it is determined whether or not there are any of the various sensors 38 of the mobile communication device 3 that are desired to receive data. In other words, it is determined whether or not there is any of the various sensors 38 provided in the mobile communication device 3 located at the location information received in S24 that is desired to transmit detection data at that geographical location. If there is no such sensor, control proceeds to S24, but if there is, control proceeds to S62.

[0032] Next, the process for the IoT device (actuator) 2 will be described with reference to FIG. 6. The IoT device (actuator) 2 performs operation control process based on the transmission information transmitted from the IoT server 8 and stored in the RAM 19 (S45). Next, in S46, it is determined whether or not a reception command signal has been received from the mobile communication device 3. If not, control returns to S45, and the loop of S45 → S46 → S45 is repeated. In this state, the mobile communication device 3 that has received the transmission command signal and the transmission information transmits the reception command signal and the transmission information to the IoT device (actuator) 2 in S47. The IoT device (actuator) 2 that has received it determines YES in S46, and control proceeds to S48, where the received transmission information (information for controlling the actuator unit 21) is stored in the RAM 19, and a reception completion signal and a device ID are transmitted to the mobile communication device 3 in S49.

[0033] The mobile communication device 3 receives the completion signal in S50 and transmits the completion signal to the IoT server 8 in S51. The IoT server 8 receives the completion signal in S52 and searches the IoT device DB based on the device ID in S53, and updates the data corresponding to the device ID. For example, the data is updated to "XX:XX △× transmission information transmitted". Then, the points are transmitted to the mobile communication device 3 in S54, and control proceeds to S61. The mobile communication device 3 receives the completion signal in S55 and updates the points stored in the EEPROM 26 (S56). Then, control proceeds to S60.

[0034] In S60, it is determined whether or not a command signal for mobile reception and a data identification signal have been received from the IoT server 8. If not, control proceeds to S84. On the other hand, if the IoT server 8 determines that there are various sensors 38 of the mobile communication device 3 that the user wishes to receive at the location received in S24, it determines YES in S40 and transmits a command signal for mobile reception and a data identification signal in S62. The mobile communication device 3 that receives the signal determines YES in S60 and proceeds to S63, where it determines whether the data specified by the data identification signal (temperature, humidity, illuminance, ultraviolet rays, etc.) requires a required detection time. In the case of illuminance and ultraviolet rays, they can be detected instantly and do not require a required detection time, so it determines NO in S63 and proceeds to S64, where the specified data is detected by the corresponding sensor 38, and the detected data is transmitted to the IoT server 8 (S65).

[0035] The IoT server 8 receives the data in S66, searches the IoT device DB 15 in S67, and updates the data with the new detection data received. Then, the points are transmitted to the mobile communication device 3 in S68. The mobile communication device 3 receives the data in S69, and adds and updates the points stored in the EEPROM 26 (S70). After that, the control proceeds to S84.

[0036] On the other hand, if the data specified by the data specification signal, such as temperature or humidity, requires a time to be detected, then a YES determination is made in S63, control proceeds to S71, and the display unit 30 notifies the user of the time required for detection (for example, 30 seconds, etc.). If the user sees this and cooperates with the transmission of the sensor detection data, the user stays at the current position for 30 seconds and waits for the detection to be completed, but if the user does not intend to cooperate, the user moves without staying. After the notification in S71, the specified data is subjected to detection processing by the corresponding sensor 38 in S72, and it is determined whether the detection is completed or not in S73. If the detection is not yet completed, it is determined whether the device remains within the detection area in S74, and if the device remains within the detection area, a loop is repeatedly executed by returning to S72. If the user's mobile communication device 3 moves out of the detection area before the detection is completed, control proceeds to S84, but if the user's mobile communication device 3 remains within the detection area until the detection is completed, control proceeds to S75, the detection data is transmitted to the IoT server 8, and then control proceeds to S69.

[0037] The IoT server 8 receives the data in S66, searches the IoT device DB 15 in S67, and updates the data with the new detection data received. Then, in S68, the IoT server 8 transmits the points to the mobile communication device 3. The mobile communication device 3 receives the data in S69, and adds and updates the points stored in the EEPROM 26 (S70). After that, the control proceeds to S84.

[0038] 5 to 7, the determination means (e.g., S26, S38, S49, etc.) for determining whether or not there is an IoT device that needs to exchange information between the IoT device group 2 and the IoT server 8 is provided on the IoT server 8 side, but this determination means may be provided on the IoT device group 2 side. This can reduce the control burden on the IoT server 8 side.

[0039] Next, the processing for the wireless sensor network 4 will be described with reference to Fig. 8. In the wireless sensor network 4, in S80, detection data of each sensor is collected and stored in association with each sensor ID. Next, in S81, it is determined whether or not there is any detection data that requires urgent transmission. If there is no data that requires urgent transmission, control proceeds to S82, where it is determined whether or not it is time for regular transmission (e.g., every 24 hours). If it is not time for regular transmission, control returns to S80, and the loop of S80 → S81 → S82 is repeated.

[0040] During this loop, if an obvious abnormality that can be judged by the wireless sensor network 4 is found in each detection data and it is judged in S81 that there is data that should be urgently transmitted, control proceeds to S83. Control also proceeds to S83 if it is judged in S82 that it is time for regular transmission.

[0041] In S83, a process of transmitting a transmission signal is performed. Next, in S86, it is determined whether or not a response signal has been received from the mobile communication device 3. If not, the process returns to S80, and the loop of S80 to S83 → S86 → S80 is repeated.

[0042] In this state, when a user carrying the mobile communication device 3 passes near the wireless sensor network 4, the transmission signal in S83 is received, a YES determination is made in S84, and control proceeds to S85. The mobile communication device 3 transmits a response signal to the wireless sensor network 4 in S85, and the wireless sensor network 4 receiving the response signal determines a YES determination in S86, and control proceeds to S87. The wireless sensor network 4 transmits the detection data collected in S80 to the mobile communication device 3 for each sensor ID (S87). The mobile communication device 3 receives the detection data in S89, and transmits the received detection data and the sensor ID together with a transmission signal to the IoT server 8 in S90.

[0043] Then, the IoT server 8 judges YES in S61, and the control proceeds to S91, where if there is emergency detection data in the received detection data, it notifies the abnormality together with the sensor ID, and then in S92, searches the wireless sensor DB 16 based on the sensor ID of the detection data and updates the detection data to the new one received. Next, in S93, the points are transmitted to the mobile communication device 3, and the control returns to S24. The mobile communication device 3, which receives the points in S94, adds and updates the points stored in the EEPROM 26 (S95), and then returns, and the control proceeds to S2.

[0044] In the control shown in FIG. 8, a determination means (e.g., S82) for determining whether or not there is an IoT device that needs to exchange information between the wireless sensor network 4 and the IoT server 8 is provided on the wireless sensor network 4 side, but this determination means may also be provided on the IoT server 8 side.

[0045] In the IoT device intermediation system described above, the user's mobile communication device (for example, a wearable computer such as smart glasses, a smartphone, a vehicle such as a car equipped with communication function, etc.) acts as an intermediary to connect the IoT device group 2 and the Internet 1, so that the IoT device group 2 does not necessarily need an Internet connection function, which makes it possible to keep costs low. Moreover, by a user performing the above intermediation using a mobile communication device, the user is given a privilege (for example, points, etc.), which gives the user an incentive to the above intermediation, and makes it possible to promote the user's intermediation behavior.

[0046] Furthermore, since a determination means (e.g., S26, S38, S49, etc.) is provided on the side of an intermediary computer (e.g., IoT server 8 or network cloud) through which the wearable computer 3 acts as an intermediary for the IoT device group 2 to determine whether or not there is an IoT device (including various sensors 38 of the mobile communication device 3) with which information needs to be exchanged, the IoT device group 2 does not need to make the above determination, reducing the processing burden on the IoT device group 2 side and further reducing costs. Moreover, if the IoT server 8 having the determination means identifies the IoT device group 2 with which information needs to be exchanged (e.g., S26, S38, S49, etc.) and notifies the wearable computer 3 of the position information of the identified IoT device 2 to notify the user, there is an advantage that the user can be encouraged to act as an intermediary.

[0047] In addition, when using the various sensors 38 provided on the wearable computer 3 to collect detection data from the various sensors 38 at desired locations onto the Internet, if a certain amount of time is required for detection, the wearable computer 3 notifies the user of the detection time required (e.g., S71), thereby encouraging the user to cooperate in collecting detection data that requires a certain amount of time.

[0048] [Service provision system using machine learning] Next, a service providing system using machine learning will be described with reference to FIGS. A major issue in putting machine learning to practical use is how to collect large amounts of training data inexpensively. This system provides users with services that use the results of machine learning, thereby building a mechanism for many users to proactively provide their own training data to the system. In particular, as shown in Figure 9, the training data (data from everyday life, etc.) provided by each user to the system is also used for personalization, and a general model obtained as a result of machine learning is optimized (personalized) for each user, and a personalized service is provided to each user. This prevents the inconvenience of each user relying on others to provide the training data and only enjoying the service.

[0049] First, the system will be outlined with reference to FIG. Each user 70 transmits around data (such as the image the user 70 is viewing, the position of their gaze, audio, and location data such as GPS) collected by a mobile communication device 3 such as a wearable computer to the machine learning 72, where it is used as learning data for the machine learning 72, and the data is also used for personalization 74.

[0050] For example, the state of each user 70 studying at school, at home, etc. is sent to the system side, and modeled by machine learning (reinforcement learning and regression) 72 to create a general model that minimizes the total time for humans to repeatedly review. At that time, the general model is generated with factors (memory ability, etc.) that differ from user to user as constants. In addition, the data sent from each user 70 to the system side is also used for personalization 74, and factors (memory ability, etc.) that differ from user to user are calculated for each user and substituted into the constant part of the general model to generate a personalized model for each user. Using the personalized model, a personalized service is provided to each user, for example, guidance is provided according to a review plan that minimizes the total time for review by presenting the next review time.

[0051] The learning data is provided not only by users but also by various specialists 71. For example, a sports gym specialist 71 transmits the results of a compilation of muscle training (hereinafter referred to as "muscle training") of many members to the system side. Based on the learning data of the compilation results, a model is created by machine learning (reinforcement learning and regression) 72 to create a general model that maximizes the effect of muscle training performed repeatedly by humans. At that time, the general model is generated with elements that differ from user to user (initial load such as a barbell and load increase coefficient, etc.) as constants. In addition, the compilation results of muscle training from each user 70 are transmitted to the system side and used for personalization 74, and elements that differ from user to user (initial load, load increase coefficient, etc.) are calculated for each user and substituted into the constant part of the general model to generate a model personalized for each user.

[0052] Using this personalized model, we provide each user with personalized services, for example, by presenting the next muscle training time and load and providing guidance according to a muscle training plan that maximizes the effect of muscle training. At that time, advertisements for professional businesses (sports gyms, etc.) 71 that provided learning data consisting of the aggregated results of muscle training are presented together with the muscle training schedule. This can motivate various professional businesses to provide learning data.

[0053] FIG. 10 shows in more detail the overall system of FIG. 9 described above. Referring to FIG. 10, specialized data is provided to the artificial intelligence server 9 from PCs 10 of various specialists. In addition, data collected by mobile communication devices 3 such as wearable computers of many users (around data such as the image and gaze position of the user 70, voice, and position data such as GPS) is provided to the artificial intelligence server 9. The artificial intelligence server 9 converts the provided data into a set of numerical values ​​and labels 51 to generate learning data. This learning data is generated using deep learning to convert raw data into a set of numerical values ​​and labels. In addition, "heterogeneous mixture learning" is used to handle raw data containing a mixture of multiple regularities. This is a method of generating a model suitable for each pattern when multiple regularities exist in the collected data.

[0054] For example, when collecting time-series data on a building's electricity consumption to create a model that predicts electricity consumption, the building's electricity consumption patterns change depending on the day of the week and time of day. Conventionally, humans would mobilize specialized knowledge to distinguish between cases to determine when the patterns change, and create a model appropriate for each case. With this "heterogeneous mixture learning," machine learning is used to find the pattern changes themselves, and a model is generated for each pattern. When new data is generated, the compatibility with the generated model is checked, and if the model does not match well, the system starts over by determining whether the pattern changes. In other words, the machine repeatedly "discriminates between patterns" and "creates models" to create the optimal model for each pattern.

[0055] In addition, in cases where the data cannot be digitized into a set of values ​​and labels even using deep learning, artificial intelligence companies will manually digitize the data into a set of values ​​and labels.

[0056] This artificial intelligence server 9 uses a general von Neumann-type computer, but it may also use a neural net processor (NNP). The NNP chip is equipped with a large number of "artificial neurons" modeled after real neurons, and each neuron is connected to each other in a network.

[0057] In addition, a quantum computer that employs the "quantum annealing method" may be used. This can significantly reduce the time required for optimization calculations in machine learning. Note that "artificial intelligence" is a broad concept that includes software agents.

[0058] The learning data converted into a set of numerical values ​​and labels is sent to the learning algorithm 52 and modeled 53. As the learning algorithm 52, various algorithms are prepared, such as regression and discrimination as supervised learning, model estimation and data mining as unsupervised learning, and reinforcement learning and deep learning as intermediate methods. In the modeling using the learning algorithm 52, for example, a general model is generated using elements that differ for each user (such as individual ability differences and preferences) as constants. The generated general model is stored in the artificial intelligence DB 17. In FIG. 10, examples of the stored general models are shown as a building maintenance inspection model, a rote learning model, a muscle training model, a dialogue model, and a task processing model. The above-mentioned learning data is also stored in the learning DB 60.

[0059] The rote learning model Ti=T0·i is a general model that minimizes the total time that humans spend repeatedly reviewing, where i is the number of repetitions, T0 is the period from the first learning to just before forgetting (specifically, the time from the first learning until memory retention rate reaches 70%), and Ti is the time from the number of repetitions (i-1) to i. T0 in this general model is an element that differs for each user (individual differences in ability such as memory), and is expressed as a constant. The method for generating this rote learning model Ti=T0·i will be explained in detail later.

[0060] The muscle training model Fi=F0+(i-1) / a is a general model that maximizes the effect of repetitive muscle training performed by humans, where i is the number of repetitions, F0 is the load (e.g., the weight of a barbell) during the first muscle training session, and a is the load increase coefficient that increases with each repetition of the muscle training.

[0061] The dialogue model is a general model used when a user dialogues with an artificial intelligence, and is made up of dialogue templates of initial targets such as intellectual males, cute females, famous celebrities, and change functions of emotions such as joy, anger, sadness, and happiness. This will be described in detail with reference to FIG. 19.

[0062] The task processing model is a general model used when a user processes a task such as work, and is composed of knowledge utilization functions of initial objects such as patent attorneys, lawyers, famous scientists, etc. This will be described in detail with reference to FIG.

[0063] The around data sent from the user's mobile communication device 3 is classified 54 for various models in the artificial intelligence server 9, and personalized data for the various models is created 55. The personalized data is stored in the user DB 12. In Fig. 10, specific examples of the various models are shown, such as for rote learning, for muscle training, for dialogue, for task processing, etc.

[0064] In the user DB12, personalized data and around data are stored in association with each other for each user ID. The personalized data for rote learning is the period TK from first learning to just before forgetting (specifically, the time from first learning until memory retention rate reaches 70%). Human memory information decreases according to the forgetting curve of psychologist Hermann Ebbinghaus. The time it takes for memory retention rate to reach 70% according to this forgetting curve is defined as TK. This TK is the time it takes for an actual user with user ID "1" to reach 70% memory retention rate, and is expressed as an actual numerical value.

[0065] The personalized data for muscle training includes the initial load FK, such as the weight of the barbell at the first muscle training session, the load increase coefficient az that increases with the repetition of muscle training, and the super-recovery time CT. These are expressed as actual values ​​for an actual user with user ID "1." Note that "super-recovery" refers to the phenomenon in which muscle strength levels rise to levels higher than before training by taking a 48-72 hour rest after training. The time required for this super-recovery is the "super-recovery time."

[0066] The personalized data for dialogue includes an initial target selected by the user from among initial targets such as intellectual males, cute females, and famous celebrities, as well as personal weights for emotions. In a general model, the initial weights for emotions are determined for each initial target, and while the user is dialogue with the initial target selected by the user, the initial weights for emotions are gradually modified in response to requests from the user, until the personal weights are optimal for the user, enabling dialogue that expresses emotions that match the user's preferences. This will be described in detail later.

[0067] The personalized data for task processing includes an initial object selected by the user from initial objects such as patent attorneys, lawyers, and famous scientists, and a personalized weight for each piece of knowledge. In a general model, the initial weight for the knowledge of each initial object is determined, and the initial weight for the knowledge to be utilized is gradually modified in response to questions and requests from the user during a work-related dialogue with the initial object selected by the user, and is finally made into a personal weight optimal for the user, enabling answers and advice that match the knowledge required by the user for work. This will be described in detail later.

[0068] The artificial intelligence server 9 uses the above personalized data to provide personalized guidance (services) for each user. Specifically, it uses the personalized data for memorization to provide guidance according to a review plan (review schedule) that minimizes the total review time for the user, uses the personalized data for muscle training to provide guidance according to a muscle training schedule that maximizes the effect of repeated muscle training, uses the personalized data for dialogue to provide dialogue that expresses emotions that match the user's preferences, and uses the personalized data for task processing to provide answers and advice that match the knowledge required by the user for work.

[0069] Next, with reference to FIG. 11, a flowchart of a subroutine program of the external display process (S2), the display process (S3), and the "Like" process by the SNS server 11 in FIG. 4 will be described.

[0070] FIG. 11 shows the process of a digital menu board, which is an example of the display 5. A digital menu board is a display for displaying menus that is installed in restaurants and the like, and by displaying the menu digitally, the time and cost for the restaurant can be significantly reduced by immediately displaying sold-out items, changing the menu, and the like. In this embodiment, as shown in FIG. 1, the digital menu board 5 is connected to the Internet 1 and is configured to be able to communicate with the artificial intelligence server 9 and the SNS server 11. In the flowchart of FIG. 11, images and videos taken by a user using a wearable computer 3 or the like and uploaded to the SNS DB 13, the user DB 12, etc. are displayed on the digital menu board 5, so that friends who come to the restaurant together can view them and enjoy eating and drinking together.

[0071] First, in S100, it is determined whether the menu has been updated. If the restaurant performs an operation to update today's menu, control proceeds to S101, where the menu is updated. Next, control proceeds to S102, where the menu is displayed.

[0072] Next, when the user wants to display on the digital menu board 5 an image or video that has been taken with the wearable computer 3 or the like and uploaded to the SNS DB 13 or the user DB 12, the user first performs an operation to access the artificial intelligence server 9. Then, a YES determination is made in S104, and control proceeds to S105, where the user ID and an access request are transmitted to the artificial intelligence server 9 for access processing. The artificial intelligence server 9 that has received the access request makes a YES determination in S106. The mobile communication device 3 communicates with the artificial intelligence server 9 in S107 and S108, and the user selects and specifies an image or video that he or she wants to display on the digital menu board 5. The artificial intelligence server 9 searches the user DB 12 in S108 to identify the selected image or video.

[0073] Once the user (customer) has identified the image they wish to display, they tap the touch screen of the digital menu board 5 to perform a communication operation. Then, a YES determination is made in S103, and communication start processing with the wearable computer 3 is performed in S109. The user (customer) also performs a communication operation on his / her own wearable computer 3. Then, a YES determination is made in S108, control proceeds to S110, communication start processing is performed, and a connection for communication between the wearable computer 3 and the digital menu board 5 is established.

[0074] Next, the digital menu board 5 transmits the display ID assigned to that digital menu board 5 to the wearable computer 3 (S111). The wearable computer 3 receives it in S112 and transmits the display ID to the artificial intelligence server 9 (S113). The artificial intelligence server 9 receives it in S114 and transmits the data specified in S108 to the display 5 with the received ID (S105). The digital menu board 5 receives it and judges YES in S116 to display the received data (S117). As a result, the image or video specified by the user (customer) is displayed on the digital menu board 5. As a result, the image or video can be viewed on the relatively large display screen of the digital menu board 5.

[0075] A noteworthy point in the above-described control is that when a user selects and specifies data uploaded onto the Internet, the artificial intelligence server 9 is accessed and specified directly from the user's mobile communication terminal 3 without going through the display 5. If the display 5 itself is connected to the Internet and data to be displayed on the display 5 is specified, it is quicker to access the data on the Internet via the display 5 at the display destination and display the data on the display 5. However, the data uploaded onto the Internet by the user is personal information related to the user's privacy, and if the user's ID or the like is transmitted via the display 5 to access the personal information, there is a risk that the user's ID and personal information will be leaked. Therefore, in this embodiment, when a user selects and specifies data uploaded onto the Internet, the artificial intelligence server 9 is accessed and specified directly from the user's mobile communication terminal 3 without going through the display 5.

[0076] Next, the digital menu board 5 determines in S118 whether data display has ended, and if so, control proceeds to S119, where processing is performed to allow the user (visitor) to access the store's homepage. Specifically, the URL of the store's homepage is sent to the mobile communication device 3. The mobile communication terminal 3, having received this, sends an access request to the page of that URL to the SNS server 11 in S120. The SNS server 11, having received this in S121, sends the store's homepage to the mobile communication device 3 in S122.

[0077] The mobile communication device 3 that has received this displays the store's homepage in S123. Meanwhile, the digital menu board 5 displays a message "Please tap on the like button on our store's homepage" in S124. If a user (customer) who sees this taps on the like button, a YES decision is made in S125, control proceeds to S126, and a like signal is sent to the SNS server 11. The SNS server 11 that receives this in S127 performs a like registration process in S128. As a result, the store can provide information to the user (customer) through the SNS. For example, it is possible to introduce new seasonal menu items, distribute coupons, notify the user of events, and so on, which can encourage the user to return to the store.

[0078] If a return is made in the external display process, the process proceeds to S4, and if a return is made in the display process, the process proceeds to S5.

[0079] Next, referring to Fig. 12(a), a flow chart of the subroutine program for processing expert data shown in S6 of Fig. 4 will be described. Maintenance data processing is performed in S135, muscle training data processing is performed in S136, other processing is performed in S137, and then the program returns and proceeds to S8. The maintenance data processing is to receive and process data on the results of maintenance and inspection of buildings sent from a maintenance specialist, which is an example of various specialists. The muscle training data processing is to receive and process aggregated data on the muscle training results of many members sent from a sports gym, which is an example of various specialists.

[0080] Next, a flowchart of the subroutine program for the above-mentioned maintenance data processing will be described with reference to Fig. 12(b). The maintenance professional performs maintenance on the building based on the detection data transmitted from the wireless sensor network 4 shown in Fig. 8 and stored in the wireless sensor DB 16, and inputs the results (KO, NG, etc.) for each sensor ID into the PC 10 (S140).

[0081] Then, the PC 10 of the maintenance specialist transmits the input inspection results to the artificial intelligence server 9 for each sensor ID in S141. The artificial intelligence server 9 determines whether or not the inspection results have been received in S145, and if not, the control proceeds to S151. On the other hand, if the inspection results have been received, the control proceeds to S146, and the inspection result data is additionally stored in the learning DB 60 for each sensor ID. Next, in S147, the wireless sensor data is read from the wireless sensor DB 16, and the inspection result data is added, and data mining is performed to find a pattern NGP1 for NG. For example, a pattern NGP1 is found in which, if the distortion and the vibration period have a predetermined relationship, there is a high probability that the inspection result will be abnormal (NG).

[0082] Next, in S148, it is determined whether NGP1 is a new pattern different from those found so far. All NG patterns found so far are stored in the storage area of ​​the "building maintenance and inspection model" of the artificial intelligence DB17, and it is determined whether the NGP1 found this time has already been stored in the artificial intelligence DB17. If it is determined that the same pattern has already been stored, control proceeds to S151, but if it is determined that it has not yet been stored, control proceeds to S149, where NGP1 is additionally stored as an NG pattern in the storage area of ​​the "building maintenance and inspection model" of the artificial intelligence DB17.

[0083] Next, in S150, the wireless sensor DB16 is searched to extract and report the sensor ID having the NGP1 pattern from among the wireless sensors that have been periodically checked. The wireless sensors are periodically checked at regular intervals (e.g., every 24 hours) (S151, S152), and the newly found NGP1 is applied to the past sensor data that has already been periodically checked to perform a recheck.

[0084] Next, in S151, it is determined whether or not it is time for a regular check, and if it is time for a regular check, in S152, the wireless sensor DB 16 is searched to extract a sensor ID having an NG pattern from the wireless sensor data added and stored after the previous regular check, and an NG report is issued. As described above, new sensor data is added and stored in the wireless sensor DB 16 from time to time, and all NG patterns stored in the artificial intelligence DB are applied to new sensor data that has not yet been checked regularly to check it. After the process of S152, the process returns, and control moves to S136.

[0085] Next, with reference to FIG. 13(a), a flow chart of the subroutine program of the around data terminal processing shown in S4 and the around data server processing shown in S5 of FIG. 4 will be described.

[0086] A learning terminal process for a user is executed in S160, a learning server process for a user is executed in S161, a muscle training terminal process is executed in S162, a muscle training server process is executed in S163, and other processes are executed in S164 and S165.

[0087] The user learning terminal process and the user learning server process are processes for collecting around data from each user who is studying, creating a general rote learning model for the user, and generating personalized data for studying for each user. The muscle training terminal process and the muscle training server process are processes for collecting around data from each user who is studying, and generating personalized data for muscle training for each user.

[0088] Next, with reference to FIG. 13(b), a flowchart of the subroutine program of the personal service terminal processing shown in S7 of FIG. 4 and the personal service server processing shown in S8 will be described.

[0089] A learning service terminal process is executed in S170, a learning service server process is executed in S171, a muscle training service terminal process is executed in S172, and a muscle training service server process is executed in S173.

[0090] The learning service terminal process provides guidance to the user according to the most efficient review plan based on the user's general rote learning model and personalized data for learning. The muscle training service terminal process and the muscle training service server process provide guidance to the user according to the most efficient muscle training plan based on the user's general muscle training model and personalized data for muscle training.

[0091] Next, a flowchart of a subroutine program for the user learning terminal process and the user learning server process will be described with reference to FIG.

[0092] In S180, it is determined whether the user is studying. This determination may be made by accepting an explicit expression of intent from the user, or may be made autonomously by the artificial intelligence server 9 by processing the user's voice by natural language processing or analyzing the image in the user's line of sight. If it is determined that the user is not studying, the process returns and control shifts to S162, but if it is determined that the user is studying, control proceeds to S181, and around data such as the image in the user's line of sight, line of sight position, and voice is transmitted to the artificial intelligence server 9. The artificial intelligence server 9, which has received this in S182, searches a specified DB for useful information such as the frequency of questions in past entrance exams and schools that have asked questions related to the subject currently being studied from the received around data in S183, and transmits the information to the user (mobile communication device 3 such as a wearable computer).

[0093] The user's mobile communication device 3 receives the useful data in S184 and displays the received useful data in an overlay using AR (Augmented Reality) in S185. Since the user can receive guidance according to the most efficient review plan by sending the around data during study to the artificial intelligence server 9, the user will take the initiative to send the around data to the artificial intelligence server 9, but the display of useful information such as the frequency of questions in the entrance exam further increases the incentive for the user to send the around data to the artificial intelligence server 9.

[0094] Next, the artificial intelligence server 9 performs a process of classifying the received data for various models and storing them in the user DB 12 and the learning DB 60 in S186. Next, in S187, it is determined whether the memorization learning model is complete. If it is not yet complete, control proceeds to S188, where the received data is converted into numerical values ​​and labels to create data for machine learning, and in S189, the data is modeled using a learning algorithm (reinforcement learning, regression, etc.). An example of this modeling will be described.

[0095] Using a huge amount of past learning data collected for each user as learning data, the system uses reinforcement learning to find a review plan that minimizes the total time for repeated review from the first learning to the end point, under the condition that the memory retention rate at a specified end point such as an entrance exam date can be maintained at a constant value (e.g., 90%) for each user. For example, if the period from the i-th review to the next review is Ti and the time required for the i-th review is THi, then the total review time T = ΣTHi. Reinforcement learning is performed to find Ti that minimizes the above T under the condition that the end point memory retention rate is ≧ 90%.

[0096] In reinforcement learning, when the value of performing action at in state st is Q(st,at), a model-based method can be used to estimate the Q value when knowledge that models the environment is given, i.e., the probability distribution of state transition probabilities and rewards, but when the environmental model is unknown, TD (Temporal Difference) learning is used. First, since it is necessary to explore the environment, the ε-greedy method is used. In the early stages of exploration, various actions are tried, and the concept of temperature is introduced so that the most optimal actions are selected as the exploration settles down. With temperature as T, an action is selected according to the probability expressed by the following formula.

[0097] P(a|s)={exp(Q(s,a) / T)} / {Σexp(Q(s,b) / T} (Note that b∈A is written under Σ, but is omitted in the above formula) Here, a is an action, Q(s,a) is the value of performing action a in state s,

[0098] T is called the annealing temperature; if it is high, actions are selected with a probability close to equal, and if it is low, the action is biased towards the optimal one. As learning progresses, the learning results can be stabilized by decreasing the value of T. It is also possible to instruct the user to actually perform the actions selected in this way, actively collecting learning data.

[0099] An example of the results of the reinforcement learning is shown in Figure 15. Referring to Figure 15, the vertical axis of the graph is Ti (review interval), and the horizontal axis is i (the number of reviews indicating how many times the review has been). The results of reinforcement learning for the user with user ID: 1 are plotted as indicated by ○. The results of reinforcement learning for the user with user ID: 2 are plotted as indicated by ×. The results of reinforcement learning for the user with user ID: 3 are plotted as indicated by △.

[0100] Next, using a "regression" algorithm, the machine learning results of these multiple users are used as input data, and this input data is considered to output a target based on a certain function, and that function is calculated. The calculated function is Ti = T0 i The coefficient T0 is a factor that differs from user to user (memory ability, etc.). Specifically, it is the period from the first learning to just before forgetting for each user (specifically, the time from the first learning until the memory retention rate reaches 70%). In this way, a general model that expresses factors that differ from user to user (memory ability, etc.) as a constant T0 is: Ti = T0 i It is.

[0101] 14, in step S190, the rote learning model (Ti=T0·i) is stored in the artificial intelligence DB 17. If an old and incomplete rote learning model is already stored, it is updated to a newer and more complete rote learning model (Ti=T0·i).

[0102] On the other hand, if it is determined in S187 that the rote learning model is complete, control proceeds to S191, where personalized data is created based on the classified received data and stored in the user DB 12. In this case, the "personalized data" is the element (memory ability, etc.) T0 that differs for each user, that is, the time from the first learning until the memory retention rate reaches 70%. This can be easily derived from the around data of the user during learning stored in the user DB 12.

[0103] Next, with reference to FIG. 16, a flowchart of the learning service terminal process shown in S170 of FIG. 13(b) and the learning service server process shown in S171 will be described.

[0104] A mobile communication device 3 such as a wearable computer judges whether or not a review request has been received from the user in S200. If there has been no review request, the process returns and control shifts to S172. If there has been a review request, the process transmits the review request to the artificial intelligence server 9 in S201. The artificial intelligence server 9 receives the request in S202 and judges whether or not the rote learning model is complete in S203. If it is not complete, control shifts to S205. If it is already complete, control advances to S204, where T0=TK is substituted into a general rote learning model Ti=T0·i, and learning items that are close to the time for repetitive review are extracted from the around data of the user DB12. The above TK refers to the period from the first learning of the user to just before forgetting (specifically, the time from the first learning to when the memory retention rate reaches 70%).

[0105] Next, in S205, the learning items when the user is looking away during class or the like are extracted from the around data in the user DB 12. Since the gaze position of the user while studying is transmitted as around data to the artificial intelligence server 9 (S181) and stored in the user DB 12, it becomes possible to determine whether the user is looking away. Next, in S206, the learning items extracted in S204 and S205 are transmitted to the mobile communication device 3. The mobile communication device 3 receives them in S207, and in S208 performs a process of having the user review the received learning items.

[0106] Also, instead of or in addition to the fact that the user is looking away, the system may control the user to review the learning matter while the user is chatting during class or the like. That is, the system includes a collection means for collecting information that can identify the user's attitude during learning, a recording means for recording the learning content that the user is receiving, a discrimination means for discriminating the part of the user's attitude that is inappropriate for learning from the information collected by the collection means, an extraction means for extracting the learning content corresponding to the part discriminated by the discrimination means from the recording means, and a transmission means for transmitting the learning content extracted by the extraction means to the user terminal. Note that the above S205 does not necessarily have to be performed. After the process of S206, the system returns and the control moves to S173.

[0107] In the above control, the user's personalized data is substituted into the general model to create a personalized model for the user, and then the personalized model is used to provide the user with a personalized service. However, a handmade personalized model may be generated for the user, and the personalized service may be provided to the user using the handmade personalized model. For example, when YES is determined in S187 of FIG. 14, the results of machine learning for each of the multiple users (subjects) who contributed to the creation of the general learning model are available (see FIG. 15). Therefore, the results of the machine learning performed on each of the subject users' learning can become a handmade personalized model, and the personalized service may be provided to the user using the personalized model. Furthermore, even if a user other than the subject wants to generate a handmade personalized model for himself, he may be controlled to provide a large amount of his own learning data (review data) to the artificial intelligence server 9 to create a handmade personalized model.

[0108] Next, with reference to FIG. 17, a flowchart of the subroutine program for processing muscle training data shown in S136 of FIG. 12(a) will be described.

[0109] PC 10 of a sports gym, an example of a professional business, compiles muscle training data of many members (subjects) and transmits it to the artificial intelligence server 9 in S210. The muscle training data includes, for example, the subject's initial load, muscle training time and number of repetitions, muscle training interval from the previous muscle training to the current muscle training, load increase coefficient, and muscle thickness measurement data. The artificial intelligence server 9 receives the muscle training data in S211, and in S212 classifies the received data for various models and stores it in the learning DB 60. Next, in S213, the received data is converted into data of numerical values ​​and a label set to create learning data.

[0110] Next, in S214, a model is created based on the learning data using a learning algorithm such as reinforcement learning or regression. This modeling method is the same as that described with reference to FIG. 15. A general model for muscle training is as follows: Fi = F0 + (i-1) / a Here, i is the number of repetitions indicating the number of repetitions of muscle training, Fi is the load (weight of a barbell, etc.) in the i-th muscle training, F0 is the load (weight of a barbell, etc.) in the first muscle training, and a is the load increase coefficient that increases with the repetition of muscle training. These F0 and a are elements that differ for each user (individual differences in muscle strength, etc.).

[0111] Next, in S215, the general model (Fi=F0+(i-1) / a) is stored in the muscle training learning model storage area of ​​the artificial intelligence DB 17. If an old and incomplete muscle training learning model is already stored, it is updated to a newer and more complete rote learning model (Fi=F0+(i-1) / a).

[0112] Next, a flowchart of the muscle training terminal process shown in S162 and the muscle training server process shown in S163 in FIG. 13(a) will be described with reference to FIG. 18(a).

[0113] The user's mobile communication device 3 determines in S220 whether the user is doing muscle training. If the user is not doing muscle training, the process returns and control proceeds to S164. If the user is doing muscle training, the process proceeds to S221, where the user's muscle training data is compiled and sent to the artificial intelligence server 9. The artificial intelligence server 9 receives the data in S222 and stores the received data in the learning DB 60 in S113, and calculates the user's initial load FK, load increase coefficient az, and super recovery time CT, and stores them in the user DB 12. After S113, the process returns and control proceeds to S165.

[0114] Next, a flowchart of the muscle training service terminal process shown in S172 and the muscle training service server process shown in S173 in FIG. 13(b) will be described with reference to FIG. 18(b).

[0115] In S226, the user's mobile communication device 3 transmits a muscle training menu request signal to the artificial intelligence server 9 in response to the user's request for a muscle training menu. In S227, the artificial intelligence server 9 receives the signal, reads the user's initial load FK, load increase coefficient az, and super recovery time CT from the user DB 12 in S228, and substitutes them into the general muscle training model (Fi=F0+(i-1) / a) to create the muscle training menu for this time. In S229, the artificial intelligence server 9 transmits the muscle training menu and an advertisement for the muscle training professional (see S210) who provided the muscle training data to the user's mobile communication device 3. In S230, the mobile communication device 3 notifies the user of the received muscle training menu and advertisement for the muscle training professional in S231. After S231, the process returns and the control shifts to S9, and after S229, the process returns and the control shifts to S10.

[0116] FIG. 19(a) shows details (specific example) of data stored in the memory area of ​​the dialogue model in the artificial intelligence DB 17 of FIG. 10. The dialogue model memory area stores data on the initial target, dialogue template, initial weights of emotions, and emotion change functions. The initial targets are stored as various selection targets such as intellectual male, intellectual female, wild male, cute female, talent A, talent B, and talent C. The dialogue template, initial weights of emotions, and emotion change functions are different for each type of initial target, and each data is stored in association with each type of initial target. The dialogue template is a dialogue template collected in large quantities to find a response to a user's dialogue by template matching. For example, in the case of intellectual male, the proportion of intellectual dialogue templates is high.

[0117] The initial weights of joy, anger, sadness, and happiness and the joy, anger, sadness, and happiness change functions are used to control the change in the emotions of joy, anger, sadness, and happiness according to the dialogue. If the user praises, the emotion is controlled to change to "joy", if the user criticizes, the emotion is controlled to change to "anger", if the user is sad, the emotion is controlled to change to "sad", and if the user is happy, the emotion is controlled to change to "happy".

[0118] However, since the weights of emotions and the manner of change differ depending on the type of initial object, different initial weights of emotions and change functions of emotions are used depending on the type of initial object. The initial weights of emotions and change functions of emotions are found by machine learning using a large number of dialogue contents collected in dialogues with a large number of users as learning data. A large number of users are classified into male intellectual type, female intellectual type, male wild type, and female cute type, and the average of each type is calculated to create initial weights of emotions and change functions of emotions appropriate for each initial object. For talent A, talent B, talent C, etc., a large number of dialogue data collected in dialogues with an actual talent (for example, talent A) are used as learning data to find initial weights of emotions and change functions of emotions by machine learning. The change functions of emotions may be a unified function common to all people, and the differences (variations) between individuals may be controlled to be adjusted by the initial weights of emotions.

[0119] An example of the joy, anger, sadness, and happiness change function F(x) is as follows. Let joy, anger, sadness, and happiness be represented as K, D, I, and R, respectively, and an emotionless state be represented as M. Let the number of times the user praised someone be x1, the number of times they criticized them be x2, the number of times they were sad be x3, and the number of times they enjoyed be x4. Let the initial weights of joy, anger, sadness, and happiness be K, D, I, and R, respectively. Also, the sigmoid function 1 / (1+e -x ) is conveniently expressed as S(x), F(x)=KS(x1-K)+DS(x2-D)+IS(x3-I)+RS(x4-R)+M(1-S(x1-K)-S(2x-D)-S(x3-I)-S(x4-R)) In other words, if any of x1, x2, x3, and x4 exceed the respective thresholds (initial weights) K, D, I, and R, they will change to the emotion that they exceeded (happiness, K, anger, D, sadness, I, and R), but if none of them exceed the above thresholds, they will be emotionless, M.

[0120] Fig. 19(b) shows details of the data stored in the storage area of ​​personalized data for dialogue in the user DB 12 of Fig. 10. Fig. 19(b) shows a case where a user with user ID: 1 has selected and specified "Talent A" as the initial target of the dialogue model. And "0.7AK, 1.5AD, 1.3AI, 0.5AR" are stored as personal weights for joy, anger, sadness, and happiness.

[0121] The initial joy, anger, sadness, and emotions weights for "Talent A" are "AK, AD, AI, AR" as shown in Figure 19(a), but while the user is engaged in a dialogue based on these initial joy, anger, sadness, and emotions weights "AK, AD, AI, AR" are gradually modified according to the user's request, and are currently "0.7AK, 1.5AD, 1.3AI, 0.5AR". As a result, the joy, anger, sadness, and emotions change function for this user's personal joy, anger, sadness, and emotions weights "0.7AK, 1.5AD, 1.3AI, 0.5AR" is as follows: F(x)=KS(x1-0.7AK)+DS(x2-1.5AD)+IS(x3-1.3AI)+RS(x4-0.5AR)+M(1-S(x1-0.7AK)-S(2x-1.5AD)-S(x3-1.3AI)-S(x4-0.5AR))

[0122] As a result, compared to the actual talent A, happiness K and happiness R are more likely to appear, and anger D and sadness I are less likely to appear.

[0123] In this way, not only can the user select and specify an initial target that matches their preferences, but the user can also modify the personal weights of joy, anger, sadness, and happiness to better reflect their preferences as the conversation continues. In other words, when dealing with a human being, it is almost impossible to change the other person into the person of one's dreams, but with artificial intelligence, it is possible to create (raise) the other person (artificial intelligence) into one's ideal partner. Until now, we have been in an era where we searched for and found our ideal lover, best friend, or partner, but in the coming future world, it will be an era where we will be able to create our ideal lover, best friend, or partner on the network (cloud).

[0124] Next, a flowchart of the subroutine program of the dialogue terminal process shown in S9 and the dialogue server process shown in S10 in FIG. 4 will be described with reference to FIG.

[0125] In S235, the mobile communication device 3 such as a wearable computer determines whether or not the user has started a dialogue, and if not, returns and control proceeds to S11. If the user has started a dialogue, control proceeds to S236, where the user ID and a dialogue start signal are transmitted to the artificial intelligence server 9. The artificial intelligence server 9, having received the signal, determines YES in S241, and determines whether or not an initial target has been received in S242, and if not, control proceeds to S244.

[0126] Meanwhile, in the mobile communication device 3, in S237, it is determined whether or not an initial object has been selected. If no selection has been made, control proceeds to S239, but if a selection has been made, the designated initial object is transmitted to the artificial intelligence server 9 in S238. The artificial intelligence server 9 that receives it determines YES in S242 and performs processing in S243 to update the dialogue initial object corresponding to the user ID in the user DB 12 to the designated one.

[0127] Next, the mobile communication device 3 and the artificial intelligence server 9 communicate with each other at S239 and S244, respectively. At this time, the artificial intelligence server 9 communicates according to the initial object for dialogue corresponding to the user ID in the user DB 12, the personal weights of emotions, and the corresponding emotion change functions in the artificial intelligence DB 17. The artificial intelligence server 9 is equipped with a natural language processing engine, and in addition to the text mining processes of "morphological analysis" and "syntactic analysis (processing to determine dependency relationships between phrases)," it incorporates two technologies: "context analysis" required to grasp the structure of a sentence, and "semantic analysis" required to understand the intent of a sentence. This makes it possible to analyze the user's voice "faster" and "more precisely."

[0128] The artificial intelligence server 9 judges whether or not it is necessary to change the personal weights of joy, anger, sadness, and happiness during the above dialogue (S245), and if it is necessary, updates the personal weights of joy, anger, sadness, and happiness in S246. For example, the artificial intelligence server 9 recognizes that the user has uttered a predefined set phrase such as "Please reduce the weight of anger a little more," and performs control to update the personal weights of joy, anger, sadness, and happiness. Note that instead of or in addition to the set phrase, the personal weights of anger, sadness, and happiness may be updated in response to the user's utterance such as "You get angry too much."

[0129] Next, in the artificial intelligence server 9, in S247, it is determined whether the dialogue content is work-related or not. If it is not work-related, the process proceeds to S249, where it is determined whether the dialogue has ended or not, but if it is work-related, the process proceeds to S248, where task server processing is performed.

[0130] In the artificial intelligence server 9, if it is determined in S249 that the dialogue has not ended, control proceeds to S244, where steps S244 to S249 are repeatedly executed, and when the dialogue has ended, control returns and proceeds to S12. On the other hand, in the mobile communication device 3, if it is determined in S240 that the dialogue has not ended, control proceeds to S237, where steps S237 to S240 are repeatedly executed, and when the dialogue has ended, control returns and proceeds to S11.

[0131] Fig. 21(a) shows details (specific example) of data stored in the storage area of ​​the task processing model in the artificial intelligence DB 17 of Fig. 10. The storage area of ​​the task processing model stores data on the initial object, the initial weight of each piece of knowledge, and the knowledge utilization function. The initial objects stored include various selectable objects such as patent attorney, lawyer, physicist, chemist, scientist A, scientist B, and scientist C. The initial weight of each piece of knowledge and the data on the knowledge utilization function differ for each type of initial object, and each data is stored in association with each type of initial object.

[0132] The initial weight and knowledge utilization function of each piece of knowledge are intended to make it easier for each initial subject to use knowledge that matches their field of expertise when performing task processing such as work. The initial weight and knowledge utilization function of each piece of knowledge are found by machine learning using a large number of dialogue contents collected in work-related dialogues with a large number of users as learning data. The large number of users are classified by profession, such as patent attorneys, lawyers, physicists, and chemists, and the average of each profession is calculated to create the initial weight and knowledge utilization function of each piece of knowledge appropriate for each initial subject.

[0133] For example, a patent attorney user asks the AI ​​server 9 many questions and consultations about work, and a large amount of data consisting of responses such as "satisfied" or "not satisfied" to the responses from the AI ​​server 9 is used as learning data and modeled using algorithms such as reinforcement learning and regression. For scientist A, scientist B, scientist C, etc., a large amount of dialogue data collected from actual dialogue between a certain scientist is used as learning data, and the initial weights of knowledge and the knowledge utilization function are found by machine learning. The knowledge utilization function may be a unified function common to all people, and the differences (variations) between individuals may be controlled to be adjusted by the initial weights of knowledge.

[0134] An example of the knowledge utilization function G(x) is as follows. Each piece of knowledge is represented as n1, n2, n3, ..., the number of positive responses such as "satisfied" by users for each piece of knowledge is represented as x1k, x2k, x3k, ..., the number of negative responses such as "not satisfied" is represented as x1h, x2h, x3h, ..., and the initial weights of each piece of knowledge are represented as w1, w2, w3, .... In addition, the sigmoid function 1 / (1+e -x ) is conveniently expressed as S(x), G(x)=w1n1S(x1k-x1h)+w2n2S(x2k-x2h)+w3n3S(x3k-x3h)+... In other words, knowledge with a greater number of positive responses than negative responses is deemed to be useful knowledge, and the coefficients (weights w1, w2, w3, ...) of each piece of knowledge n1, n2, n3, ... can be controlled so that knowledge with larger values ​​is used first.

[0135] Fig. 21(b) shows details of the data stored in the storage area of ​​the personalized data for task processing in the user DB 12 of Fig. 10. Fig. 21(b) shows a case where a user with user ID: 1 selects and specifies "Patent Attorney" as the initial target of the dialogue model. And, "1.2·1w1 1n1, 0.8·1w2 1n2, 0.7·1w3 1n3, 1.3·1w4 1n4" are stored as the personal weights w of each knowledge n.

[0136] The initial weights of each piece of knowledge for "patent attorney" are "1w11n1, 1w21n2, 1w31n3, 1w41n4" as shown in Figure 21(a), but while the user is engaged in a business conversation based on the initial weights of each piece of knowledge, the initial weights of each piece of knowledge "1w11n1, 1w21n2, 1w31n3, 1w41n4" are gradually modified according to the user's request, and are currently "1.2·1w11n1, 0.8·1w21n2, 0.7·1w31n3, 1.3·1w41n4". In this way, the initial target that matches the user's profession can be selected and specified, and the weights of each piece of knowledge can be modified to reflect the user's questions and consultations more as business conversations are repeated. In other words, in the future world, it will be an era in which you can create your ideal business partner (partner) on the network (cloud).

[0137] Next, with reference to FIG. 22, a flowchart of the task terminal process shown in S11 of FIG. 4 and the subroutine program of the task server process shown in S12 and S248 of FIG. 20 will be described.

[0138] In S255, the user's mobile communication device 3 determines whether or not the user has requested that the user's personal weight be registered as an initial target in the artificial intelligence DB 17. If not, control proceeds to S259, where it is determined whether or not there has been a request to update the initial target, and if not, control proceeds to S265.

[0139] On the other hand, if a request is made in S255 to register the user's (the user's) personal weight as the initial target in the artificial intelligence DB 17, S255 judges YES, control proceeds to S256, and the user ID and a request to register the initial target are sent to the artificial intelligence server 9.

[0140] In the artificial intelligence server 9, S257 determines whether or not an initial target registration request has been received, and if not, control transfers to S261, and it is determined whether or not a requested initial target has been received, and if not, control transfers to S267.

[0141] If the user ID and the initial target registration request are transmitted from the mobile communication device 3 according to the process of S256, a YES decision is made in S257, and control proceeds to S258, where the personalized weight of each piece of knowledge corresponding to the user ID is read from the user DB 12 and registered in the AI ​​DB 17 together with the user name. In this way, in this embodiment, the user can register the personalized weight of each piece of knowledge in the AI ​​DB 17 and distribute it widely to the general public. For example, when efficiently training a subordinate in one's work to make him / her a full-fledged expert, if the user transfers the personalized weight of each piece of work knowledge that the supervisor has cultivated up to now to the subordinate, the AI ​​server 9 will respond to the subordinate's questions and consultations on his / her behalf, and the AI ​​server 9 will take over the training of the subordinate. When the user transfers the personalized weight of each piece of knowledge to another person, a payment means for payment between the user and the transferee may be provided so that the weight is transferred for a fee.

[0142] When the user issues a request to update the initial object to the mobile communication device 3 and specifies a new initial object (requested initial object), a YES determination is made in S259 and control proceeds to S260, where the user ID and the requested initial object are sent to the artificial intelligence server 9. The artificial intelligence server 9 that receives the request makes a YES determination in S261 and control proceeds to S262, where it determines whether the received requested initial object is one that has been used by the user before. If the requested initial object has not been used before, a process is performed in S264 to read the requested initial object from the artificial intelligence DB 17 and store it in the user DB 12 in association with the user ID, and control proceeds to S267.

[0143] If the received requested initial object is one that has been used by the user before, the initial object may already be stored in the user DB 12, and the personal weight may have been updated with the use of the initial object (see S271). Therefore, if the received requested initial object is one that has been used by the user before, control proceeds to S263, and the personal weight of the previously used knowledge stored in the user DB 12 is used.

[0144] 23, the artificial intelligence server 9 reads out an initial object corresponding to the user ID from the user DB 12 and a personal weight of each piece of knowledge in S267, and reads out a knowledge utilization function corresponding to the read initial object from the artificial intelligence DB 17 in S268. When the user uses the mobile communication device 3 to carry out a work-related dialogue, a dialogue is carried out at S265 on the mobile communication device 3 side and at S269 on the artificial intelligence server 9 side. At S269, the work-related dialogue is carried out using the read knowledge utilization function and personal weight. At the mobile communication device 3 side, the work-related dialogue control at S265 continues until it is determined at S266 that the work-related dialogue has ended. At the artificial intelligence server 9 side, the controls at S267 to S274 are repeated until it is determined at S274 that the work-related dialogue has ended, and the work-related dialogue control at S269 continues.

[0145] When it is determined in S270 that the personal weight needs to be changed while the controls of S267 to S274 are being repeated, the control proceeds to S271, where the personal weight in the user DB 12 is updated. When the personal weight is also registered in the AI ​​DB 17 by the user (see S255 to S258), a YES determination is made in S272, and the weight in the AI ​​DB 17 is also updated in S273. This determination in S270 is made by, for example, the user uttering a predefined set phrase such as "satisfied" or "not satisfied" in response to a response from the AI ​​server 9, to determine the need to change the weight of the knowledge used on the AI ​​server 9 side. Note that instead of or in addition to the set phrase, the determination may be made based on the user's normal conversation such as "Thanks for the great answer."

[0146] Then, when it is determined in S266 on the mobile communication device 3 side that the business dialogue has ended, an instruction to end the business dialogue is sent to the artificial intelligence server 9 in S273, and the process returns and the control shifts to S1. The artificial intelligence server 9, having received the instruction, judges YES in S274, returns, and the control shifts to S3.

[0147] Modifications and features of the above-described embodiment will be described below. (1) At least one or all of the IoT server 9 and the IoT device DB 15, the artificial intelligence server 9, the artificial intelligence DB 17, the user DB 12 and the learning DB 60, and the SNS server 11 and the SNS DB 13 may be configured as a network cloud. In the above-described embodiment, the mobile communication device 3 is mainly used as an example of a user terminal, but the user terminal may be a user PC 7 or a robot 6. In addition, a wearable computer such as smart glasses has been shown as a specific example of the mobile communication device 3, but the mobile communication device 3 is not limited to this, and various devices such as a mobile phone, a smartphone, and a vehicle such as an automobile having a communication function may be used.

[0148] (2) The IoT server 8 is provided with an identification means (S24 to S26, S38, S40) for identifying IoT devices with which information needs to be exchanged. The geographical locations of the IoT devices identified by the identification means may be transmitted to the wearable computer and notified to the user, encouraging the user to act as an intermediary. In this case, the user may be notified that high scores (points, etc.) will be given to IoT devices with a particularly high need for intermediation.

[0149] (3) It is possible to control the system so that only a limited number of IoT devices are given high-value rewards (points, etc.), and to promote intermediation activities by utilizing lottery-like psychology. It is also possible to control the system so that intermediation activities of IoT devices installed in geographical locations are performed by using location games.

[0150] (4) The above-described embodiments disclose the following inventions. The present invention relates to a system for realizing IoT at low cost by reducing the cost of IoT devices (sensors, actuators, etc.) that are installed all over the earth, for example.

[0151] Background technologies related to systems that realize IoT at low cost include, for example, the following. A border router (gateway device) is introduced between an IoT sensor and a server on the Internet, and the server's address is not set in the sensor; instead, the sensor transmits sensor data information to the border router after its own address is determined. The border router relays the sensor information to a sensor data management server and collects data. This allows the server to detect the sensor's address and also enables pull-type data collection addressed to the sensor address (JP Patent Publication 2014-78773).

[0152] However, in the case of this background technology, although pull-type data collection from the server to the sensor address is possible, it is necessary to provide each sensor with an Internet connection function such as a network I / F, or to prepare a proprietary adapter for connecting to the network, which has the disadvantage of increasing the costs in realizing IoT.

[0153] On the other hand, some IoT devices installed all over the world do not necessarily require pull-type data collection from the server to the sensor address. For example, in the case of an illuminance sensor installed to collect illuminance throughout the year, it is sufficient to store the periodic illuminance detection results in memory and transmit the stored illuminance detection results to the server by the time the server collects the illuminance throughout the year, which is done once a year. In addition, in the case of IoT devices installed in places with a lot of human traffic, a method of sending data from the IoT device to the server by using a human artificial method is also possible.

[0154] The present invention has been made in view of the above circumstances, and has an object to provide a system that can reduce the costs involved in implementing IoT.

[0155] The present invention relates to a wearable computer carried by a user, a server communicating with the wearable computer via the Internet; Equipped with a group of IoT devices, The IoT device group includes a communication means for communicating with the wearable computer, The wearable computer includes: A device communication means for communicating with the IoT device group; a server communication means for communicating with the server via the Internet; and an intermediary means for mediating between the IoT device that is the communication partner of the device communication means and the server that is the communication partner of the server communication means, thereby enabling the exchange of information between the two.

[0156] With this configuration, the IoT devices do not necessarily need to have Internet connection capabilities, which reduces costs and enables IoT to be realized at low cost.

[0157] Preferably, the server further includes reward giving means for giving a predetermined reward to the wearable computer that has performed the mediation through the mediation means.

[0158] According to this configuration, it is possible to provide the user with an incentive to act as an intermediary, and it is possible to encourage the user to act as an intermediary.

[0159] More preferably, the server further includes a determination means for determining whether or not there is an IoT device with which information needs to be exchanged.

[0160] With this configuration, there is no need for the IoT device group to have a function for determining whether or not the need for information exchange has arisen, which reduces the processing burden on the IoT device group and further reduces costs.

[0161] More preferably, the wearable computer further includes a location information transmission means for transmitting information capable of identifying a current location to the server, The server, a location determination means for determining whether or not the IoT device identified by the identification means is present in the vicinity of the wearable computer whose location is identified by the information transmitted by the location information transmission means; an intermediation command transmission means for transmitting a command signal to the wearable computer to cause the IoT device to perform mediation by the intermediation means when the location determination means determines that the IoT device is present; The mediation means performs mediation in accordance with the command signal from the mediation command transmission means.

[0162] According to this configuration, the wearable computer requests the IoT device that needs mediation to act as an intermediary, so there is no need for the IoT device group to transmit a request signal for mediation.

[0163] More preferably, the wearable computer comprises: A sensor that requires a predetermined time to detect; and a notification means (e.g., S71) for notifying a user of a detection time required by the sensor when performing detection using the sensor at a desired geographical position, When the sensor is able to perform the detection for the detection time within the detection area of ​​the desired geographical position, the detection data is mediated by the mediation means and transmitted to the server.

[0164] According to this configuration, if a certain amount of time is required for detection, the wearable computer notifies the user of that required detection time, thereby encouraging the user to cooperate in collecting detection data that requires time.

[0165] (5) In the above-described embodiment, the creation of a building maintenance inspection model, a memorization learning model, a muscle training model, a dialogue model, and a task processing model has been shown as specific examples of machine learning, but the present invention is not limited to these, and various models such as a learning model for infants, an artificial secretary model, and an artificial tutor model are possible. The personalized data is also not limited to memorization learning, muscle training, dialogue, and task processing, and various models such as a learning model for infants, an artificial secretary model, and an artificial tutor model are possible. These general models and personalized data are controlled so that they can be switched and used according to the user's situation. As a result, shortly after birth, humans grow up while receiving personalized services based on artificial intelligence using their own personalized data, and live their entire lives together with artificial intelligence. In other words, in the future world, humans and artificial intelligence will be paired together. It is a world in which humans and artificial intelligence are paired together to form one personality. In addition, when using the general model and the personalized data, in this embodiment, the previous personalized data for dialogue is used unless the user expresses an intention to change it, but when the general model and the personalized data are changed, the personalized data for dialogue may be automatically changed to another suitable one. For example, in general daily conversation, talent A is used as the initial target for dialogue, and in business-related dialogue, it is automatically changed to a female intellectual type (see FIG. 19).

[0166] (6) Recommendation control may be performed to recommend artificial intelligence or a general model that is thought to match the user based on around data collected from the user by the mobile communication device 3 or the like. In addition, when a user searches for an artificial intelligence or a general model, the search results displayed in a list may be controlled to be preferentially displayed from those that are thought to match the user based on around data collected from the user. Furthermore, word-of-mouth from users about the artificial intelligence or the general model may be collected and displayed. In this case, as described above, a human and an artificial intelligence are paired to form one personality, so word-of-mouth about the paired human and artificial intelligence may be collected and displayed as a set.

[0167] (7) For infants who have been trained using the general model for infant learning and personalized data, a huge amount of data consisting of the provided learning materials (learning information) and learning results may be collected, and the data may be used as learning data for machine learning to machine learn human intellectual growth (supervised learning, etc.). The same learning materials (learning information) provided to the infant may also be provided to the artificial intelligence, and the learning results of the artificial intelligence that has achieved intellectual growth as a result may be compared with those of an actual infant, and reinforcement learning or regression may be performed to minimize the differences, creating an artificial intelligence learning model that enables learning close to that of a human. In particular, in areas where artificial intelligence is weak (e.g., common sense, emotions, creativity, etc.), comparison of the learning results of the two may be beneficial for artificial intelligence in machine learning (supervised learning, etc.).

[0168] (8) In this embodiment, personalization means (e.g., S191, S223, S246, S271) for personalizing the general model to a model suitable for the user based on information sent from the user is provided in the artificial intelligence server 9, but instead of or in addition to this, it may be provided in the user's terminal (e.g., a mobile communication device 3 such as a wearable computer, a user PC, a robot, etc.).

[0169] The above-described embodiments disclose the following inventions. The present disclosure relates to a service providing system and a program that utilizes machine learning by artificial intelligence, for example. More specifically, the present disclosure relates to a service providing system that provides a service that utilizes machine learning by artificial intelligence, and a program that is executed by a computer so that a user can receive a service that utilizes machine learning by artificial intelligence.

[0170] There have been techniques for creating learning data from multiple information sources in order to perform machine learning using artificial intelligence (for example, JP 2011-232997 A).

[0171] However, machine learning requires a huge amount of training data, and in order to create this vast amount of training data, it is necessary to collect a large amount of necessary information from sources. In order to put machine learning to practical use, it is essential to reduce the effort and cost required for collecting this information.

[0172] Furthermore, for example, a general model created by collecting large amounts of information from an unspecified number of people and performing machine learning using the huge amount of learning data tends to become an average model for the entire unspecified number of people, and if that general model is used to provide a service to each user, each of whom has individual differences, there is a risk that the service will not be a good match for users who deviate from the average.

[0173] The present disclosure was conceived in view of the above circumstances, and its purpose is to reduce the effort and cost required for collecting a huge amount of information required for creating training data. Another purpose is to solve the inconvenience that occurs when a service using a general model resulting from machine learning does not match a user.

[0174] Next, an example of the correspondence between various means for solving the problems and the embodiments will be shown below in parentheses.

[0175] The present disclosure relates to a service providing system (e.g., FIG. 9 and FIG. 10 ) that provides a service using machine learning by artificial intelligence, A machine learning means (e.g., S189, S214, FIG. 15, etc.) for generating a general model (e.g., a memorization learning model Ti=T0·I, a muscle training model Fi=F0+(i-1) / a, etc.) modeled by machine learning by inputting learning data based on information sent from a user (e.g., around data such as an image viewed by the user 70, a gaze position, a voice, and GPS location data, etc.); A personalization means (e.g., S191 and S204, S223 and S228, etc.) for personalizing the general model to a model suitable for the user based on information sent by the user; A service providing means (e.g., S206, S229, etc.) for providing a personalized service to the user using the personalized model (e.g., providing study items based on the most efficient review plan, providing a muscle training menu based on the most efficient muscle training plan, etc.), Information sent by the user is used for both the machine learning and the personalization.

[0176] According to this configuration, by providing users with a service that utilizes the results of machine learning, many users will proactively provide information for learning data. Moreover, since the information provided by the users is also used to personalize a general model into a model suitable for the user, it is possible to prevent the inconvenience of each user relying on others to provide information for learning data and only enjoying the service.

[0177] Preferably, the machine learning means generates the general model by using factors that differ for each user (e.g., individual ability differences such as memorization ability, initial load for muscle training, load increase coefficient, preferences, etc.) as constants (e.g., T0, F0, a, etc.) (e.g., S189, S214, FIG. 15, etc.), The personalization means comprises: A value derivation means (e.g., S191, S223, etc.) for deriving actual values ​​(e.g., TK, FK, az, CT, etc.) of the user that fit the constant part in the general model based on information sent from the user; and a substitution means (e.g., S204, 228, etc.) for substituting the actual value derived by the value derivation means into a constant part in the general model to personalize the model suitable for the user.

[0178] More preferably, the machine learning means also inputs learning data based on information sent from a specialist (e.g., a muscle training specialist) and generates a general model modeled by machine learning (e.g., S214, etc.).

[0179] Another aspect of the present invention is a service providing system that provides a service using machine learning by artificial intelligence, A storage means (e.g., an artificial intelligence DB 17, etc.) for storing a plurality of types of general models (e.g., the dialogue model of FIG. 19(a), the task processing model of FIG. 21(a), etc.) generated by modeling using machine learning; a service providing means (e.g., S241 to S244, S247, S248, S261 to S263, S267 to S269, etc.) for providing a service (e.g., dialogue service, response to business-related questions or inquiries, etc.) to a user by utilizing a general model selected by the user from a plurality of types of general models stored in the storage means; A personalization means (e.g., S246, S271, etc.) for personalizing the general model into a model suitable for the user in response to the user's reaction to the service provided by the service providing means, The service providing means includes a personal service providing means (for example, executing S244, S269, etc. according to the updated personal weights) for providing a service to the user using the model personalized by the personalization means.

[0180] According to this configuration, a user can select a desired general model from among multiple types of general models generated by modeling using machine learning, and a service is provided to the user using the selected general model, so that the user can enjoy a service based on a general model that reflects the user's desires. Moreover, the general model is personalized to a model suitable for the user based on the user's reaction to the service, so that the personalization of the service provided makes it possible to provide a service that matches the user.

[0181] Preferably, the personal service providing means has means (e.g., S263, etc.) for providing a service to the user using the personalized model at the time of use when the general model selected by the user has previously been used to provide a service to the user (e.g., YES in S262).

[0182] More preferably, the system further comprises a model using means (for example, S257, S258, etc.) for allowing other people to use the model personalized by the personalizing means.

[0183] Another aspect of the present disclosure is a program executed by a computer to allow a user to receive a service using machine learning by artificial intelligence, comprising: A step (e.g., S237 and S238, S259 and S260, etc.) in which the user selects and specifies a desired model from among multiple types of general models (e.g., the dialogue model in FIG. 19(a), the task processing model in FIG. 21(a), etc.) generated by modeling using machine learning by artificial intelligence; A processing step (e.g., S236 to S239, S260, S265, etc.) for a user to receive a service using the general model selected by the selecting and specifying step; A step of providing the artificial intelligence with the user's reaction to personalize the general model into a model suitable for the user according to the reaction of the user who has received the service by the processing step (e.g., S239, S265, etc.); causing the computer to execute The processing steps include a personal service receiving step (e.g., receiving a response according to the updated personal weights in S239, S265, etc.) for the user to receive personalized services using the model personalized by the artificial intelligence.

[0184] According to this configuration, a user can select a desired general model from among multiple types of general models generated by modeling using machine learning, and a service is provided to the user using the selected general model, so that the user can enjoy a service based on a general model that reflects the user's desires. Moreover, the general model is personalized to a model suitable for the user based on the user's reaction to the service, so that the personalization of the service provided makes it possible to provide a service that matches the user.

[0185] Although the embodiment of the present invention has been described above, the embodiment disclosed herein is illustrative in all respects and should not be considered as being limiting. The scope of the present invention is indicated by the claims, not the above description, and is intended to include the meaning equivalent to the claims, and all modifications within the scope of the claims. [Explanation of symbols]

[0186] 1 Internet, 2 IoT devices, 3 Mobile communication devices, 4 Wireless sensor networks, 8 IoT servers, 9 Artificial intelligence servers, 10 PCs of various specialist vendors, 12 User database, 15 IoT device database, 17 Artificial intelligence database, 60 Learning database, 38 Various sensors.

Claims

1. A service providing system that enables task processing using a specific person model that has learned knowledge from a specific person, A generating means for generating the specific person model using artificial intelligence; A storage means for storing the specific person model; A dialogue unit that enables a user to have a dialogue using the specific person model read out from the storage unit by the user, A service providing system that enables task processing using the specific person model.

2. The generating means: A first means for generating a generic person model by performing machine learning using learning data collected from a large number of people; 2. The service providing system according to claim 1, further comprising: second means for generating said specific person model by having the general person model generated by said first means learn knowledge from a specific person.

3. A human model production method for producing the specific human model according to claim 1, comprising: A first step of generating a generic person model by performing machine learning using learning data collected from a large number of people; a second step of generating the specific person model by having the general person model generated in the first step learn knowledge from a specific person.

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