Patent Publication Number: US-2023140019-A1

Title: Data collection device, data acquisition device, and data collection method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to Japanese Patent Application No. 2021-176840 filed Oct. 28, 2021, which is incorporated herein by reference in its entirety, including the specification, drawings, and abstract. 
     TECHNICAL FIELD 
     The present disclosure relates to a data collection device, a data acquisition device, and a data collection method. 
     BACKGROUND 
     In smart cities, it has been proposed to collect data from multiple entities within the community. In particular, in JP2013-069084A1, since there is uncertainty in data obtained from information systems of different business entities, it has been proposed to collect data obtained by correcting the obtained data in order to solve the uncertainty. 
     Incidentally, in a smart city or the like, it is conceivable that data relating to a person located in the smart city is collected from a data acquisition device, and the collected data is used for processing using a machine learning model or training of a machine learning model. However, it is conceivable that a resident residing in a smart city and a visitor not residing in the smart city have different necessary outputs, and thus different machine learning models may be used. In this case, the input parameters to be input to the machine learning model are different between the resident and the visitor. Even in such a case, if the same parameters for the resident and the visitor are collected from the data acquisition device to the data collection device, partially unnecessary data is collected from the data acquisition device, resulting in an increase in the amount of communication between the data acquisition device and the data collection device. 
     In view of the above problems, an object of the present disclosure is to enable collection of appropriate data for a resident and a visitor while suppressing the amount of communication between a data acquisition device and a data collection device. 
     SUMMARY 
     (1) A data collection device for collecting data from a data acquisition device for acquiring data relating to a person located within a predetermined target area, the data collection device comprising a processor, the processor being configured to: 
     determine whether data acquired by the data acquisition device is data relating to a resident within the target area; and 
     control transmission of data from the data acquisition device to the data collection device, wherein 
     the processor is configured to cause the data acquisition device to transmit, to the data collection device, data of parameters that are at least partially different between a case where data acquired by the data acquisition device relates to the resident and a case where data acquired by the data acquisition device does not relate to the resident. 
     (2) The data collection device according to above (1), wherein 
     the data acquisition device is a terminal device held by a person, and 
     the processor is configured to determine whether or not the data acquired by the data acquisition device is data related to the resident in the target area, based on whether or not the person holding the terminal device is the resident in the target area. 
     (3) The data collection device according to above (1) or (2), wherein the processor is configured to cause the data acquisition device to transmit data relating to more parameters to the data collection device when the data acquired by the data acquisition device relates to the resident, than when the data acquired by the data acquisition device does not relate to the resident. 
     (4) The data collection device according to above (3), wherein the parameters for causing the processor to transmit data when the data acquired by the data acquisition device relates to the resident include all parameters for causing the processor to transmit data when the data acquired by the data acquisition device does not relate to the resident, and other parameters. 
     (5) The data collection device according to any one of above (1) to (4), wherein the processor is configured to cause the data acquisition device to transmit data relating to parameters relating to a current health state of a person to the data collection device, regardless of whether or not the data acquired by the data acquisition device is data relating to the resident. 
     (6) A data acquisition device for acquiring data relating to a person located in a predetermined target area and transmitting the data to a data collection devices, the data acquisition device comprising a processor, the processor being configured to: 
     determine whether data acquired by the data acquisition device is data relating to a resident in the target area; and 
     control transmission of data from the data acquisition device to the data collection device, wherein 
     the processor is configured to cause the data acquisition device to transmit, to the data collection device, data of parameters that are at least partially different between a case where the data acquired by the data acquisition device relates to the resident and a case where the data acquired by the data acquisition device does not relate to the resident. 
     (7) A data collection method for collecting data from a data acquisition device for acquiring data relating to a person located within a predetermined target area, the data collection method comprising: 
     determining whether data acquired by the data acquisition device is data relating to a resident within the target area; and 
     controlling transmission from the data acquisition device to cause the data acquisition device to transmit data of parameters that are at least partially different between when data acquired by the data acquisition device relates to the resident and when data acquired by the data acquisition device does not relate to the resident. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic configuration diagram of a machine learning system. 
         FIG.  2    is a diagram schematically showing a hardware configuration of a terminal device. 
         FIG.  3    is a functional block diagram of the processor of the terminal device. 
         FIG.  4    is a diagram schematically illustrating a hardware configuration of a server. 
         FIG.  5    is a function block diagram of a processor of a server. 
         FIG.  6    is an operation sequence diagram of data collection processing. 
         FIG.  7    is a flowchart showing the flow of processing using the machine learning model in the server. 
         FIG.  8    is a flowchart showing a flow of training processing of the machine learning model in the server. 
         FIG.  9    is a functional block diagram of the processor of the terminal device according to the second embodiment. 
         FIG.  10    is an operation sequence diagram of data collection processing according to the second embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments will be described in detail with reference to the drawings. In the following description, similar components are denoted by the same reference numerals. 
     First Embodiment 
     Configuration of the Machine Learning System 
     The configuration of the machine learning system  1  according to the first embodiment will be described with reference to  FIGS.  1  to  5   .  FIG.  1    is a schematic configuration diagram of a machine learning system  1 . The machine learning system  1  executes the processing using the machine learning model in the server, and trains the machine learning model in the server. The machine learning system  1  also functions as a data collection system that collects data necessary for processing using the machine learning model and training of the machine learning model. 
     As shown in  FIG.  1   , the machine learning system  1  includes a plurality of mobile terminal devices  10  and a server  20  capable of communicating with the terminal devices  10 . Each of the plurality of terminal devices  10  and the server  20  are configured to be able to communicate with each other, via a communication network  4  configured by an optical communication line or the like and a radio base station  5  connected to the communication network  4  via a gateway (not shown). As the communication between the terminal device  10  and the radio base station  5 , various types of wide-area wireless communication having a long communication distance can be used, and for example, communication that conforms to any communication standard such as 4G, LTE, or 5G, WiMAX established by 3GPP, IEEE is used. 
     In particular, in the present embodiment, the server  20  communicates with the terminal device  10  located within a predetermined target area. The target area is a range surrounded by predetermined boundaries. For example, the target area may be a smart city defined as “a sustainable city or region that solves various problems faced by cities and regions and continues to create new value through the sophistication of management (e.g., planning, maintenance, management, operation, etc.) while utilizing new technologies such as ICT (Information and Communication Technology). The server  20  may be capable of communicating with the terminal device  10  located outside the target area. 
     The terminal device  10  is an example of a data acquisition device that acquires data necessary for processing using a machine learning model and for training of the machine learning model, which will be described later. In particular, in the present embodiment, the terminal device  10  is a device that is individually held and acquires data of an individual holding the terminal device  10 . Therefore, in the present embodiment, the terminal device  10  functions as a mobile data acquisition device that acquires personal data in a predetermined target area or an area around the target area. Therefore, in the present embodiment, the terminal device  10  moves along with the movement of the individual holding the terminal device  10 . Therefore, when an individual holding the terminal device  10  moves into the target area, the terminal device  10  held by the individual also moves into the target area. Conversely, when an individual holding the terminal device  10  moves out of the target area, the terminal device  10  held by the individual also moves out of the target area. 
     Specifically, in the present embodiment, the terminal device  10  includes, for example, a wearable terminal, such as a watch type terminal (smart watch), a wristband type terminal, a clip type terminal, and an eyeglass type terminal (smart glass), or a portable terminal. The terminal device  10  acquires, for example, position information of each individual in the target area and personal data relating to the state of the user wearing the terminal device  10 . The personal data includes, for example, vital signs (heart rate, body temperature, blood pressure, and respiration rate), blood oxygen concentration, electrocardiogram (ECG), blood glucose level, step count, calorie consumption, fatigue, sleep state, and the like. 
     In the present embodiment, the terminal device  10  includes, in particular, a watch type terminal and a portable terminal that communicates with the watch type terminal by short-range wireless communication. As the short-range radio communication, for example, communication conforming to any communication standard (for example, Bluetooth™ or ZigBee™) established by IEEE, ISO, IEC, or the like is used. 
       FIG.  2    is a diagram schematically showing a hardware configuration of the terminal device  10 . As shown in  FIG.  2   , the terminal device  10  includes a communication module  11 , a sensor  12 , an input device  13 , an output device  14 , a memory  15 , and a processor  16 . The communication module  11 , the sensor  12 , the input device  13 , the output device  14  and the memory  15  are connected to the processor  16  via signal lines. 
     The communication module  11  is an example of a communication unit that communicates with other devices. The communication module  11  is, for example, a device for communicating with the server  20 . In particular, the communication module  11  is a device that communicates with the radio base station  5  through the wide area wireless communication described above, so that the communication module  11  communicates with the server  20  through the radio base station  5  and the communication network  4 . 
     The sensor  12  detects various parameters relating to the individual holding the terminal device  10 . The sensor  12  also detects various parameters, such as parameters relating to the status of the terminal device  10  and the status around the terminal device  10 . In particular, the sensor  12  has a plurality of discrete sensors that detect different parameters. The values of the various parameters detected by the sensor  12  are transmitted to the processor  16  or the memory  15  via signal lines. 
     Specifically, the sensor  12  may include a sensor for detecting parameters relating to a user holding the terminal device  10 . For example, when the terminal device  10  is a watch type terminal (smart watch), the sensor  12  includes a sensor for detecting personal data (including biometric data) of a user wearing the terminal device  10 . In addition, the sensor  12  may include a sensor for detecting the status of the terminal device  10 , for example, a GNSS receiver for detecting the current position of the terminal device  10 . The sensor  12  may also include a sensor that detects environmental data around the terminal device  10 . For example, the terminal device  10  may include a sensor that detects air temperature or humidity around the terminal device  10 . 
     The input device  13  is a device for the user of the terminal device  10  to use to input information. Specifically, the input device  13  includes a touch panel, a microphone, a button, a dial, or the like. Information input via the input device  13  is transmitted to the processor  16  or the memory  15  via a signal line. 
     The output device  14  is a device for the terminal device  10  to use to output information. Specifically, the output device  14  includes a display, a speaker, or the like. The output device  14  performs output based on a command transmitted from the processor  16  via a signal line. For example, the display displays an image on the screen based on commands from the processor  16 , the speaker outputs sounds based on commands from the processor  16 . 
     The memory  15  includes, for example, a volatile semiconductor memory (e.g., RAM), a nonvolatile semiconductor memory (e.g., ROM), and the like. The memory  15  stores a computer program for executing various processing by the processor  16 , various data used when various processing is executed by the processor  16 , and the like. 
     The processor  16  includes one or more CPUs (Central Processing Unit) and peripheral circuits thereof. The processor  16  may further comprise an arithmetic circuit, such as a logical arithmetic unit or a numerical arithmetic unit. The processor  16  executes various kinds of processing based on a computer program stored in the memory  15 . Specific processing executed by the processor  16  of the terminal device  10  will be described later. 
       FIG.  3    is a functional block diagram of the processor  16  of the terminal device  10 . As shown in  FIG.  3   , the processor  16  of the terminal device  10  includes a data transmission unit  161 , a data acquisition unit  162 , and a notification control unit  163 . These functional blocks of the processor  16  of the terminal device  10  are functional modules implemented, for example, by a computer program running on the processor  16 . Alternatively, the functional blocks included in the processor  16  may be dedicated arithmetic circuits provided in the processor  16 . The details of each of these functional blocks will be described later. 
     The server  20  is connected to a plurality of terminal devices  10  via the communication network  4 . In the present embodiment, the server  20  executes processing using the machine learning model and trains the machine learning model. The server  20  also functions as a data collection device that collects data necessary for execution of processing using the machine learning model and training of the machine learning model. 
       FIG.  4    is a diagram schematically showing a hardware configuration of the server  20 . The server  20  includes a communication module  21 , a storage device  22 , and a processor  23 , as illustrated in  FIG.  4   . The server  20  may include input devices such as a keyboard and a mouse, and output devices such as a display and a speaker. 
     The communication module  21  is an example of a communication device for communicating with devices outside the server  20 . The communication module  21  has an interface circuit for connecting the server  20  to the communication network  4 . The communication module  21  is configured to be able to communicate with each of the plurality of terminal devices  10  via the communication network  4  and the radio base station  5 . 
     The storage device  22  is an example of a storage device for storing data. The storage device  22  includes, for example, a hard disk drive (HDD), a solid state drive (SSD), or an optical recording medium. The storage device  22  may include a volatile semiconductor memory (e.g., RAM), a nonvolatile semiconductor memory (e.g., ROM), or the like. The storage device  22  stores a computer program for executing various processing by the processor  23  and various data used when various processing is executed by the processor  23 . In particular, the storage device  22  stores data received from the terminal device  10 , data related to the machine learning model (e.g., configuration of the machine learning model and learning parameters (e.g., weights, biases, etc.)), data used for processing using the machine learning model, and data used for training the machine learning model. 
     The processor  23  has one or a plurality of CPUs and peripheral circuits thereof. The processor  23  may further have an arithmetic circuit such as a GPU or a logical or numerical unit. The processor  23  executes various kinds of processing based on a computer program stored in the storage device  22 . 
       FIG.  5    is a functional block diagram of the processor  23  of the server  20 . As shown in  FIG.  5   , the processor  23  includes an attribute determination unit  231 , a transmission control unit  232 , a state estimation unit  233 , a data transmission unit  234 , a data set creation unit  235 , and a training unit  236 . These functional blocks of the processor  23  of the server  20  are, for example, functional modules implemented by computer programs running on the processor  23 . Alternatively, the functional blocks included in the processor  23  may be dedicated arithmetic circuits provided in the processor  23 . The details of each of these functional blocks will be described later. 
     Machine Learning Model 
     In the present embodiment, a machine learning model subjected to machine learning is used when a predetermined process is performed in the server  20 . In the present embodiment, the machine learning model is a model for outputting information on the health of an individual holding the terminal device  10 , based on data transmitted from the terminal device  10 . Information about an individual&#39;s health may include, for example, whether the health state of an individual is abnormal, the lifestyle of an individual to improve, a predicted cholesterol level of the individual, etc. The personal health information output in this manner from the server  20  is transmitted to the terminal device  10  held by the individual and notified to the individual. 
     In particular, in the present embodiment, a plurality of machine learning models are stored in the server  20 , and the input parameters and the output parameter are different for each machine learning model. However, in any machine learning model, at least a part of the input parameters include the data transmitted from the terminal device  10 . In the present embodiment, a first machine learning model and a second machine learning model are stored in the server  20 . The first machine learning model outputs whether or not an abnormality occurs in the health state of an individual holding the terminal device  10 , based on data transmitted from the terminal device  10  or the like. On the other hand, the second machine learning model outputs a lifestyle to be improved, a predicted value of cholesterol, and the like of an individual holding the terminal device  10  based on data and the like transmitted from the terminal device  10 . 
     Further, in the present embodiment, personal data of the user holding the terminal device  10  (in particular, biometric data) and surrounding environment data of the terminal device  10  are input as input parameters to these machine learning models. Data relating to the body of the user holding the terminal device  10  and environment data are acquired from the sensor  12  of the terminal device  10 . Alternatively, the environmental data may be obtained, not from the sensor  12 , but from another server distributing the temperature and humidity of each location via the communication network  4 . In the present embodiment, when some personal data (for example, data including vital signs, blood oxygen concentration, electrocardiogram, etc.; hereinafter referred to as “first personal data”) and environmental data are input to the first machine learning model, whether or not an abnormality has occurred in the health state of the individual is output. On the other hand, when some or all of the personal data (for example, including data relating to parameters such as blood glucose level, calorie consumption, fatigue degree, etc., in addition to the parameters included in the first personal data; hereinafter referred to as “second personal data”) that are at least partially different from the first personal data is input to the second machine learning model, the lifestyle of the individual to be improved, predicted values of cholesterol, etc., are output. 
     Various machine learning algorithms can be used for the machine learning model. In the present embodiment, the machine learning model is a model trained by supervised learning, such as a neural network (NN), a support vector machine (SVM), and a decision tree (DT). In particular, in the present embodiment, the machine learning model may be a recurrent neural network (RNN) model in which personal data and environment data of a user are input as input parameters in time series. 
     In the present embodiment, the training of the machine learning model as described above is performed by the server  20 . The machine learning model is trained using a training data set. The training data set includes data used as input parameters and ground truth data (ground truth value or ground truth label) of output parameters corresponding to the data. In particular, in the present embodiment, the training data set of the first machine learning model includes time series data acquired by the terminal device  10  for a certain subject, and data on whether or not an abnormality, such as heat stroke, has occurred in the subject. In the present embodiment, the training data set of the second machine learning model includes time series data acquired by the terminal device  10  for a certain subject, data of lifestyle-related diseases occurring in the subject, actual measured values of cholesterol of the subject, and the like. The training data set may be generated by performing preprocessing (e.g., processing for missing data, normalization, standardization, etc.) on the output value of the sensor  12 . 
     In training (learning) of the machine learning model, for example, using any known technique (e.g., an error back propagation method), model parameters in the machine learning model (parameters whose values are updated by training, such as weights w and biases b of NN) are updated repeatedly. The model parameters are repeatedly updated so that, for example, the difference between the output value of the machine learning model and the ground truth value of the output parameter included in the training data set becomes small. As a result, the machine learning model is trained, and a trained machine learning model is generated. 
     Processing in the Machine Learning System 
     In the present embodiment, mainly in the terminal device  10 , data necessary for processing using the machine learning model and training of the machine learning model is acquired. The server  20  collects data acquired by the terminal devices  10 . In particular, in the present embodiment, data is collected from the terminal device  10  located in the target area. 
     Here, as described above, the first machine learning model outputs whether or not an abnormality has occurred in the health state of the individual. Whether or not an abnormality has occurred in the health state of the individual can be estimated if there is first individual data or environmental data in a relatively short period of time. On the other hand, as described above, the second machine learning model outputs predicted values of lifestyle, cholesterol, and the like to be improved by the individual. In order to estimate the lifestyle and cholesterol to be improved for an individual, the second individual data for a longer period of time is required. The server  20  can only collect data in a relatively short period of time for visitors who temporarily come into the target area (i.e., visitors who do not reside in the target area). On the other hand, the server  20  can collect data for a long period of time for a resident who resides in the target area. 
     For this reason, in the present embodiment, the server  20  estimates whether or not an abnormality has occurred in the health state of an individual by using only the first machine learning model for the visitor, based on the personal data and the environment data collected from the terminal device  10  held by the visitor. The estimation result is transmitted from the server  20  to the terminal device  10 , and the terminal device  10  notifies the user based on the estimation result. 
     On the other hand, the server  20  estimates whether or not an abnormality has occurred in the health state of an individual for a resident using the first machine learning model, based on personal data and environment data collected from the terminal device  10  held by the resident, and estimates the lifestyle, cholesterol, and the like of the individual to be improved using the second machine learning model. The estimation result is transmitted from the server  20  to the terminal device  10 , and the terminal device  10  notifies the user based on the estimation result. 
     Data Collection 
     Next, a description will be given of collecting data from the terminal device  10  by the server  20 , with reference to  FIG.  6   .  FIG.  6    is an operation sequence diagram of data collection processing. In the present embodiment, data acquired by the terminal device  10  located in the target area is transmitted to the server  20 . In particular, in the present embodiment, in the terminal device  10  held by the visitor in which estimation is performed only by the first machine learning model, only the first personal data and the environment data among the acquired data are transmitted to the server  20 . On the other hand, in the terminal device  10  held by the resident in which the estimation is performed by the first machine learning model and the second machine learning model, the second personal data and the environment data among the acquired data are transmitted to the server  20 . In collecting data from the terminal device  10  by the server  20 , the data transmission unit  161  and the data acquisition unit  162  of the processor  16  of the terminal device  10  are used, and the attribute determination unit  231  and the transmission control unit  232  of the processor  23  of the server  20  are used. 
     As shown in  FIG.  6   , in collecting data, first, the data transmission unit  161  of the terminal device  10  transmits identification information of the terminal device  10  to the server  20  (Step S 11 ). The identification information of the terminal device  10  may be an identification number assigned to each terminal device  10 , or may be identification information associated with a user of the terminal device  10 , such as a mail address of a user of the terminal device  10 . The identification information is transmitted from the terminal device  10 , for example, when the terminal device  10  intrudes into the target area from outside the target area. The transmission of identification information from the data transmission unit  161  to the server  20  is performed via the communication network  4 . 
     Upon receiving the identification information from each terminal device  10 , the attribute determination unit  231  of the server  20  determines whether or not the data acquired by the terminal device  10  that transmitted the identification information, is data related to the resident (Step S 12 ). In the present embodiment, the attribute determination unit  231  determines whether or not the data acquired by the terminal device  10  is data related to a resident, based on whether or not the user holding the terminal device  10  is a resident. Specifically, when the user holding the terminal device  10  is a resident, the attribute determination unit  231  determines that the data acquired by the terminal device  10  is data related to the resident. On the other hand, when the user holding the terminal device  10  is not a resident (i.e., is a visitor), the attribute determination unit  231  determines that the data acquired by the terminal device  10  is not data related to a resident. 
     The identification information of the resident is registered in advance and stored in the storage device  22  of the server  20 . Therefore, the attribute determination unit  231  checks the identification information stored in the storage device  22  of the server  20  and determines whether or not the user holding the terminal device  10  is a resident. Specifically, when the identification information received from the terminal device  10  conforms to the information stored in the storage device  22  as the identification information of the resident, the attribute determination unit  231  determines that the user holding the terminal device  10  is a resident. On the other hand, when the identification information received from the terminal device  10  does not conform to the information stored in the storage device  22  as the identification information of the resident, the attribute determination unit  231  determines that the user holding the terminal device  10  is not a resident. 
     When it is determined whether or not the data acquired by the terminal device  10  is data relating to a resident, the transmission control unit  232  which controls the transmission of the data from the terminal device  10  to the server  20  specifies the type of data to be transmitted by each terminal device  10  to the server  20  (Step S 13 ). In the present embodiment, specifically, when it is determined in step S 12  that the data acquired by the terminal device  10  is not data related to a resident, the transmission control unit  232  specifies the first personal data and the environment data as the type of data to be transmitted. On the other hand, when it is determined that the data acquired by the terminal device  10  is data related to the resident, the transmission control unit  232  specifies the second personal data different from the first personal data, and the environment data, as the type of the data to be transmitted. 
     When the type of data to be transmitted from each terminal device  10  to the server  20  is specified in step S 13 , the transmission control unit  232  requests the terminal device  10  to transmit the specified type of data to the server  20  (Step S 14 ). The transmission of the request signal requesting the transmission from the transmission control unit  232  to the server  20  is performed via the communication network  4 . 
     As described above, in the present embodiment, the transmission control unit  232  causes the terminal device  10  to transmit, to the server  20 , data of parameters at least partially different between the case where the data acquired by the terminal device  10  relates to the resident and the case where the data acquired by the terminal device  10  does not relate to the resident. 
     Here, as described above, in the present embodiment, the second personal data includes, in addition to all the parameters included in the first personal data, data relating to other parameters such as a blood glucose level. Thus, the second personal data is greater than the first personal data, and the second personal data includes data for all parameters contained in the first personal data and data for other parameters. 
     Therefore, in the present embodiment, when the data acquired by the terminal device  10  relates to the resident, the transmission control unit  232  causes the terminal device  10  to transmit data relating to more parameters to the server  20 , as compared with when the data acquired by the terminal device  10  does not relate to the resident. In addition, in the present embodiment, the parameters transmitted by the transmission control unit  232  when the data acquired by the terminal device  10  relates to the resident include all the parameters transmitted by the transmission control unit  232  when the data acquired by the terminal device  10  does not relate to the resident, and other parameters. By transmitting many parameters for the resident from the terminal device  10  to the server  20  in the above manner, it is possible to estimate more parameter values for the resident than for the visitor by the machine learning model. 
     In the present embodiment, the transmission control unit  232  causes the terminal device  10  to transmit the first personal data to the server  20 , regardless of whether or not the data acquired by the terminal device  10  is data relating to a resident. As described above, the first personal data is personal data including vital signs, blood oxygen concentrations, electrocardiograms, and the like, and is used to output whether or not an abnormality has occurred in the health state of an individual using the first machine learning model. In other words, the first personal data can be said to be data relating to parameters relating to the current health state of the individual. Therefore, in the present embodiment, regardless of whether or not the data acquired by the terminal device  10  is data related to the resident, the transmission control unit  232  causes the terminal device  10  to transmit the data related to the parameter related to the current health state of the person to the server  20 . As a result, it is possible to estimate whether or not there is an abnormality in the current health state of a person, which may require urgent response, regardless of the resident or visitor. 
     The data acquisition unit  162  of each terminal device  10  periodically acquires data from the sensor  12  (Step S 15 ). The data acquired by the data acquisition unit  162  includes first personal data, second personal data, and environment data. The data acquisition unit  162  may acquire all the data that can be acquired by the terminal device  10 , or may acquire only the type of data that is requested to be transmitted to the terminal device  10  in step S 14 . Therefore, for example, when the request signal requesting transmission to the server  20  requests transmission of only the first personal data, the data acquisition unit  162  does not need to acquire data not included in the first personal data, such as a blood glucose level. The data acquired by the data acquisition unit  162  is stored in the memory  15 . 
     When the data acquisition unit  162  acquires the data, the data transmission unit  161  transmits the data acquired by the terminal device  10  in step S 15  to the server  20  (Step S 16 ). In particular, in the present embodiment, the terminal device  10  transmits data to the server  20  in accordance with a request signal transmitted from the server  20  in step S 14 . Therefore, when the request signal requesting transmission to the server  20  requests transmission of the first personal data and the environment data, the data transmission unit  161  transmits the first personal data and the environment data to the server  20 . The transmitted data is stored in the storage device  22  of the server  20 , thus the data acquired by the terminal device  10  is stored in the storage device  22  of the server  20 . The data thus stored in the storage device  22  is used for processing using the machine learning model and training of the machine learning model. 
     Use of Machine Learning Models 
     Next, processing using the machine learning model in the server  20  will be described with reference to  FIG.  7   . In the present embodiment, the server  20  estimates information on the health of an individual holding the terminal device  10  using a machine learning model, based on data transmitted from the terminal device  10 . In particular, in the present embodiment, when the individual holding the terminal device  10  is a resident, the server  20  estimates whether or not an abnormality occurs in the health state of the individual holding the terminal device  10 , the lifestyle of the individual to be improved, the predicted value of cholesterol of the individual, and the like. On the other hand, when the individual holding the terminal device  10  is not a resident, the server  20  estimates whether or not an abnormality occurs in the health state of the individual holding the terminal device  10 . Then, the server  20  transmits the estimation result to the terminal device  10 . 
       FIG.  7    is a flowchart showing a flow of processing using the machine learning model in the server  20 . In processing using the machine learning model, the state estimation unit  233  and the data transmission unit  234  of the server  20  are used. 
     When the data (personal data, environment data, etc.) transmitted from each terminal device  10  is stored in the storage device  22 , the state estimation unit  233  acquires data about an arbitrary terminal device  10  from the storage device  22  (Step S 21 ). The state estimation unit  233  acquires data, for example, each time a predetermined amount of data about an arbitrary terminal device  10  is stored. 
     When obtaining the data for an arbitrary terminal device  10 , the state estimation unit  233  determines whether the data acquired by the terminal device  10  is data relating to the resident, based on the identification information of the terminal device  10  (Step S 22 ). This determination is performed in the same manner as in step S 12  of  FIG.  6   . 
     When it is determined in step S 22  that the data acquired by the terminal device  10  is data relating to the resident, the state estimation unit  233  estimates information relating to the health of the individual using the first machine learning model and the second machine learning model (Step S 23 ). Specifically, the state estimation unit  233  inputs the second personal data and the environment data transmitted from the terminal device  10  to the first machine learning model and the second machine learning model, and outputs whether or not an abnormality occurs in the health state of the individual holding the terminal device  10 , the lifestyle of the individual to be improved, the predicted value of the cholesterol of the individual, and the like. 
     On the other hand, if it is determined in step S 22  that the data acquired by the terminal device  10  does not relate to the resident, the state estimation unit  233  estimates information on the health of the individual using the first machine learning model (Step S 24 ). Specifically, the state estimation unit  233  inputs the first personal data and the environment data transmitted from the terminal device  10  to the first machine learning model, and outputs whether or not an abnormality occurs in the health state of the individual holding the terminal device  10 . 
     In step S 23  or step S 24 , when the state estimation unit  233  estimates information on the health of the individual holding the terminal device  10 , the data transmission unit  234  of the server  20  transmits the estimation result to the terminal device  10  (Step S 25 ). The estimation result is transmitted from the data transmission unit  234  to the terminal device  10  via the communication network  4 . 
     Upon receiving the estimation result from the server  20 , the notification control unit  163  of the terminal device  10  controls notification to the user holding the terminal device  10  based on the estimation result. Specifically, when receiving an estimation result indicating that an abnormality occurs in the health state of an individual, the notification control unit  163  causes the output device  14  of the terminal device  10  to output that estimation result. For example, the notification control unit  163  causes the display to display a message indicating that an abnormality occurs in the health state of an individual, or causes the speaker to output the message as an audio signal. In the same manner, the notification control unit  163  notifies the user holding the terminal device  10  of the lifestyle of the individual to be improved, the predicted value of cholesterol of the individual, or the like. 
     As described above, in the present embodiment, based on whether or not the data acquired by the terminal device  10  is data related to the resident, the values of different parameters are estimated using at least partially different machine learning models, and the estimation result is notified to the user. 
     Training of Machine Learning Models 
     Next, the training process of the machine learning model used in the server  20  will be described with reference to  FIG.  8   .  FIG.  8    is a flowchart showing a flow of training processing of the machine learning model in the server  20 . In the training process, the data set creation unit  235  and the training unit  236  of the server  20  are used. 
     When data (personal data, environmental data, and the like) transmitted from each terminal device  10  is stored in the storage device  22  to some extent, the data set creation unit  235  creates a training data set (Step S 31 ). The training data set includes measured values of input parameters of the machine learning model and ground truth data (ground truth value or ground truth label) of the output parameters. For example, in the present embodiment, the training data set includes data acquired by the terminal device  10  held by each individual, and information on the health of the individual (ground truth data). In particular, in the present embodiment, the training data set used in the first machine learning model includes first personal data and environment data acquired by the terminal device  10  held by the resident and the visitor, and information on the health of the individual (information on the output parameters of the first machine learning model). Similarly, the training data set used for the second machine learning model includes the second personal data and the environment data acquired by the terminal device  10  held by the resident, and information on the health of the individual (information on the output parameters of the second machine learning model). 
     Data acquired by the terminal device  10  held by each individual is stored in the storage device  22  of the server  20 . Therefore, the data set creation unit  235  uses the data stored in the storage device  22  in this manner when creating the learning data set. 
     Further, in the present embodiment, when each individual suffers from some kind of disease, the information is input to the terminal device  10  by the user himself/herself via the input device  13 . The suffering information input to the terminal device  10  is transmitted to the server  20  via the communication network  4 . The data set creation unit  235  uses the suffering information as ground truth data when creating the learning data set of the first machine learning model. 
     In addition, in the present embodiment, the suffering information of the lifestyle-related disease and cholesterol value of the resident are input to the terminal device  10  via the input device  13  by the user himself/herself. The user information input to the terminal device  10  is transmitted to the server  20  via the communication network  4 . The data set creation unit  235  uses the user information as the ground truth data when creating the training data set of the second machine learning model. 
     When a certain number of training data sets are created by the data set creation unit  235  in step S 31 , the training unit  236  trains the machine learning model using the created training data sets (Step S 32 ). Specifically, as described above, the training unit  236  updates the model parameters used in the machine learning model by using a known error back propagation method or the like. 
     When the training of the machine learning model is completed, the training unit  236  updates the values of the model parameters of the machine learning model used in steps S 23  and S 24  of  FIG.  7    using the model parameters of the trained machine learning model (Step S 33 ). After the values of the model parameters of the machine learning model are updated, various estimations are performed by the machine learning model using the updated model parameters in steps S 23  and S 24 . 
     Effects and Modifications 
     In the present embodiment, the processing using the second machine learning model is performed only for the resident, and is not performed for the visitor. This is because the processing using the second machine learning model requires data collected over a long period of time to some extent. As described above, in the present embodiment, the transmission control unit  232  causes the terminal device  10  to transmit, to the server  20 , data of parameters at least partially different between the case where the data acquired by the terminal device  10  relates to the resident and the case where the data acquired by the terminal device  10  does not relate to the resident. In particular, the data relating to the input parameters of the second machine learning model is not transmitted from the terminal device  10  to the server  20  if the data acquired by the terminal device  10  does not relate to the resident. As a result, unnecessary data is not transmitted from the terminal device  10  to the server  20 , therefore, it is possible to collect appropriate data for the resident and visitor while suppressing the communication amount of data between the terminal device  10  and the server  20 . 
     In the above embodiment, the mobile terminal device  10  is used as a data acquisition device for acquiring data necessary for processing using the machine learning model and training of the machine learning model. However, as the data acquisition device, various devices other than mobile terminal devices can be used. Specifically, the data acquisition device may include a sensor disposed in a public area in the target area, for example, a monitoring camera, a sensor for detecting air temperature and humidity or the like. Further, the data acquisition device may include, for example, a sensor disposed in a private area in the target area, for example, a sensor for detecting the power consumption of the electronic device in each facility, a sensor for detecting hot water supply amount by the water heater or the like. Furthermore, the data acquisition device includes a sensor or the like provided in devices to moving in the target area (e.g., an automobile or an electric bicycle). 
     In the above embodiment, the machine learning model is used to estimate information on the health of an individual holding the terminal device  10 , based on the biometric data and the environment data. However, a model having various input parameters and output parameters can be used as the machine learning model. The input parameters may include various parameters that can be acquired by a data acquisition device including the terminal device  10 . Specifically, the input parameters may include, in addition to the parameters described above, for example, time, images taken by the mobile terminal device  10  and the monitoring camera or the like, moving images, air temperature in the target area, humidity, weather, wind speed, and the like. The input parameters may also include the power consumption of the electronic devices of each facility in the target area, the amount of hot water supplied by the water heater, and the like. Further, the input parameters may include a destination, a charge, or the like of the device moving within the target area. The output parameters may include, for example, a predicted value of a future power consumption in the entire target area, a predicted value of a future hot water supply amount in the entire target area, or the like. Alternatively, the output parameters may include future predicted values for individuals or individual devices within the target area, such as, for example, information regarding the health of the individual as described above. 
     However, regardless of which model is used as the machine learning model, the input parameters input to the machine learning model differ between the resident and the visitor. Therefore, in the present embodiment, different data is transmitted from the data acquisition device to the server for processing using the machine learning model and training of the machine learning model. 
     Further, in the above-described embodiment, the second personal data transmitted from the terminal device  10  to the server  20  when the data acquired by the terminal device  10  is related to the resident includes data on other parameters such as the blood glucose level in addition to all the parameters included in the first personal data transmitted from the terminal device  10  to the server  20  when the data acquired by the terminal device  10  is not related to the resident. However, as long as data of at least partially different parameters is transmitted from the terminal device  10  to the server  20  between when the data acquired by the terminal device  10  is related to a resident and when the data acquired by the terminal device  10  is not related to a resident, the first personal data and the second personal data may each include any parameters. 
     Second Embodiment 
     Next, the machine learning system  1  according to the second embodiment will be described with reference to  FIGS.  9  and  10   . Hereinafter, points different from the machine learning system  1  according to the first embodiment will be mainly described. 
     In the first embodiment, the server  20  determines whether or not the data acquired by each terminal device  10  is data related to a resident, and the server  20  controls the transmission of data from the terminal device  10  to the server  20  based on the determination result. On the other hand, in the second embodiment, the terminal device  10  determines whether the data acquired by the terminal device  10  is data related to the resident, the terminal device  10  controls the transmission of data to the server  20  based on the determination result. 
       FIG.  9    is a functional block diagram of the processor  16  of the terminal device  10  according to the second embodiment. As shown in  FIG.  9   , the processor  16  of the terminal device  10  includes a data transmission unit  161 , a data acquisition unit  162 , a notification control unit  163 , an attribute determination unit  164 , and a transmission control unit  165 . 
       FIG.  10    is an operation sequence diagram of data collection processing according to the second embodiment. As shown in  FIG.  10   , in the present embodiment, when collecting data, the attribute determination unit  164  of the terminal device  10  first determines whether or not the data acquired by the terminal device  10  is data relating to a resident (Step S 41 ). In the present embodiment as well, similarly to step S 12  of  FIG.  6   , the attribute determination unit  164  determines whether or not the data acquired by the terminal device  10  is data related to a resident, based on whether or not the user holding the terminal device  10  is a resident. 
     In the present embodiment, the attribute determination unit  164  determines whether or not the user holding the terminal device  10  is a resident based on information registered by the user of the terminal device  10  via the input device  13 . When the user registers that the user is a resident, the attribute determination unit  164  determines that the user holding the terminal device  10  is a resident. On the other hand, when the user registers that the user is not a resident, or when the user does not register that the user is a resident, the attribute determination unit  164  determines that the user holding the terminal device  10  is a visitor. 
     When it is determined whether or not the data acquired by the terminal device  10  is data relating to a resident, the transmission control unit  165  of the terminal device  10  identifies the type of data to be transmitted by each terminal device  10  to the server  20 , as in step S 13  of  FIG.  6    (Step S 42 ). If the type of data is specified, the transmission control unit  165  requests the data transmission unit  161  of the terminal device  10  to transmit the specified type of data to the server  20 . 
     The data acquisition unit  162  of each terminal device  10  periodically acquires data from the sensor  12 , as in step S 15  of  FIG.  6    (Step S 43 ). If the data acquisition unit  162  acquires the data, the data transmission unit  161  transmits the data acquired by the terminal device  10  in step S 43  to the server  20 , as in step S 16  of  FIG.  6    (Step S 44 ). 
     In the present embodiment, the terminal device  10  determines whether or not the data acquired by the terminal device  10  is data related to a resident. Therefore, it is possible to reduce the amount of communication between the terminal device  10  and the server  20  that accompanies the determination. 
     While preferred embodiments of the present disclosure have been described above, the present disclosure is not limited to these embodiments, and various modifications and changes may be made within the scope of the appended claims.