System

Abstract

An object of a system according to an embodiment is to create an application by causing an IoT sensor and AR / MR goggles to cooperate with each other without expert knowledge.SOLUTION: A system includes a reception unit, an analysis unit, and a creation unit. The reception unit receives sensor data. The analysis unit analyzes the data received by the reception unit. The creation unit creates an application based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of creating an app by linking IoT sensors with AR / MR goggles was complicated, making it difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to enable users without specialized knowledge to create apps by linking IoT sensors with AR / MR goggles. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, an analyzing unit, and a creating unit. The receiving unit receives sensor data. The analyzing unit analyzes the data received by the receiving unit. The creating unit creates an application based on the data analyzed by the analyzing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to create apps by linking IoT sensors with AR / MR goggles, even if they do not have specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention links AR / MR goggles and IoT sensors, enabling anyone to easily create apps. This system allows users to wear AR / MR goggles and install IoT sensors to receive and analyze sensor data and create apps. For example, a user can install temperature and humidity sensors in a factory and wear AR / MR goggles. The location and type of sensors can be freely selected. The AR / MR goggles then receive data from the IoT sensors. For example, temperature data from a temperature sensor and humidity data from a humidity sensor are received in real time. This data is analyzed by an analysis unit within the AR / MR goggles. Based on the analyzed data, users can intuitively create apps. For example, they can create apps that display an alert when the temperature exceeds a certain range or automatically activate a humidifier when humidity drops. Users can easily configure the app through the interface of the AR / MR goggles. This allows anyone to easily create apps without specialized knowledge. The system allows users to create the apps they need, improving work efficiency. You can also create an app that automates temperature and humidity management at home to create a comfortable living environment.

[0029] An application development system according to an embodiment includes a receiving unit, an analyzing unit, and a creating unit. The receiving unit receives sensor data. The sensor data includes, but is not limited to, temperature data, humidity data, and location data. For example, the receiving unit receives data from a temperature sensor in real time. The receiving unit can also receive data from a humidity sensor in real time. The receiving unit can also receive data from a location sensor in real time. For example, the receiving unit receives data from the temperature sensor at regular intervals and monitors temperature changes in real time. The data from the humidity sensor is received in an event-driven manner, receiving data only when humidity changes. The data from the location sensor is received periodically and continuously monitoring location changes. The analyzing unit analyzes the data received by the receiving unit. The analysis can be performed by, for example, data filtering, statistical analysis, or application of a machine learning algorithm, but is not limited to, such examples. For example, the analyzing unit filters the data to remove noise. The analyzing unit can also perform statistical analysis to identify data trends. The analyzing unit can also apply a machine learning algorithm to analyze data patterns. For example, the analysis unit uses filtering techniques to remove noise from sensor data. Statistical analysis calculates the average value and standard deviation of the data to identify trends in the data. Machine learning algorithms learn data patterns and detect outliers. The creation unit creates an app based on the data analyzed by the analysis unit. The app is created according to criteria such as, for example, the type of app, the platform used, and the creation procedure, but is not limited to these examples. For example, the creation unit creates a temperature control app based on temperature data. The creation unit can also create a humidity control app based on humidity data. The creation unit can also create a location tracking app based on location data. For example, the creation unit creates a temperature control app that displays an alert when the temperature exceeds a certain range. The creation unit can also create a humidity control app that automatically activates a humidifier when humidity drops. The location tracking app displays a movement route based on location data.As a result, the application creation system according to the embodiment receives and analyzes sensor data and creates an application, allowing anyone to easily create an application.

[0030] The application creation system includes a sensor unit that standardizes the type and installation method of sensors. The sensor unit standardizes the type and installation method of sensors. Standardization includes, but is not limited to, examples of sensor interfaces, communication protocols, and installation procedures. For example, the sensor unit standardizes the interface of temperature sensors so that sensors from different manufacturers can be connected using the same interface. The sensor unit can also standardize the communication protocol so that data can be exchanged between different sensors. The sensor unit can also standardize the installation procedure so that sensor installation can be simplified. For example, the sensor unit standardizes the interface of temperature sensors so that sensors from different manufacturers can be connected using the same interface. The communication protocol is standardized so that data can be exchanged between different sensors. The installation procedure is standardized so that sensor installation can be simplified. Standardizing the type and installation method of sensors enables flexible sensor installation.

[0031] The application creation system includes an interface unit that provides an interface that is easy for users to operate. The interface unit provides an interface that allows users to operate intuitively. Ease of operation includes, but is not limited to, user interface design, simplified operation procedures, and provision of a help function, for example. For example, the interface unit may adopt a simple, highly visible design to allow users to operate intuitively. The interface unit may also simplify the operation procedures to allow users to operate without hesitation. The interface unit may also provide a help function to allow users to immediately confirm how to operate. For example, the interface unit may adopt a simple, highly visible design to allow users to operate intuitively. The operation procedures are simplified to allow users to operate without hesitation. The help function is provided to allow users to immediately confirm how to operate. This facilitates application creation by providing an interface that is intuitive for users to operate.

[0032] The receiving unit can receive sensor data in real time. Real time includes, but is not limited to, for example, a delay time in receiving data and an update frequency. For example, the receiving unit receives data from a temperature sensor in real time. The receiving unit can also receive data from a humidity sensor in real time. The receiving unit can also receive data from a position sensor in real time. For example, the receiving unit receives data from a temperature sensor at regular intervals and monitors temperature changes in real time. Data from a humidity sensor is received in an event-driven manner, and data is received only when humidity changes. Data from a position sensor is received periodically and position changes are continuously monitored. As a result, receiving sensor data in real time enables immediate data analysis.

[0033] The analysis unit can analyze the received data in real time. Real time includes, but is not limited to, for example, a delay time in data analysis and an update frequency. The analysis unit can, for example, filter the data to remove noise. The analysis unit can also perform statistical analysis to identify data trends. The analysis unit can also apply machine learning algorithms to analyze data patterns. For example, the analysis unit can use filtering techniques to remove noise from sensor data. Statistical analysis calculates the average value and standard deviation of the data to identify data trends. Machine learning algorithms learn data patterns and detect outliers. This allows for real-time data analysis, enabling immediate feedback.

[0034] The creation unit can create an app based on the analysis results. App creation includes, but is not limited to, criteria such as the type of app, the platform used, and the creation procedure. For example, the creation unit can create a temperature control app based on temperature data. The creation unit can also create a humidity control app based on humidity data. The creation unit can also create a location tracking app based on location data. For example, the creation unit can create a temperature control app that displays an alert when the temperature exceeds a certain range. The creation unit can also create a humidity control app that automatically activates a humidifier when humidity drops. The location tracking app displays a movement route based on the location data. This allows users to intuitively create apps by creating apps based on the analysis results.

[0035] The receiving unit can automatically switch the data receiving method depending on the type of sensor. For example, the receiving unit receives data from a temperature sensor at regular intervals and monitors temperature changes in real time. The receiving unit can also receive data from a motion sensor in an event-driven manner and receive data only when motion is detected. The receiving unit can also periodically receive data from an environmental sensor and continuously monitor environmental changes. For example, the receiving unit receives data from a temperature sensor at regular intervals and monitors temperature changes in real time. The receiving unit receives data from a motion sensor in an event-driven manner and receives data only when motion is detected. The receiving unit receives data from an environmental sensor in a regular manner and continuously monitors environmental changes. This enables efficient data reception by switching the data receiving method depending on the type of sensor.

[0036] The receiving unit can adjust the data reception range based on the installation location of the sensor. For example, if the sensor covers a wide area, the receiving unit widens the data reception range to collect detailed information. Furthermore, if the sensor monitors a specific area, the receiving unit can narrow the data reception range to improve accuracy. Furthermore, if the sensor is movable, the receiving unit can dynamically adjust the reception range according to the installation location. For example, if the sensor covers a wide area, the receiving unit widens the data reception range to collect detailed information. If the sensor monitors a specific area, the receiving unit narrows the data reception range to improve accuracy. If the sensor is movable, the receiving range is dynamically adjusted according to the installation location. In this way, highly accurate data collection is possible by adjusting the data reception range based on the installation location of the sensor.

[0037] The receiving unit can monitor the remaining battery level of the sensor and adjust the frequency of data reception as necessary. For example, when the remaining battery level of the sensor is low, the receiving unit reduces the frequency of data reception to extend battery life. Furthermore, when the remaining battery level of the sensor is sufficient, the receiving unit can increase the frequency of data reception to collect detailed data. Furthermore, when the remaining battery level of the sensor is medium, the receiving unit can adjust the frequency of data reception appropriately. For example, when the remaining battery level of the sensor is low, the receiving unit reduces the frequency of data reception to extend battery life. When the remaining battery level of the sensor is sufficient, the receiving unit increases the frequency of data reception to collect detailed data. When the remaining battery level of the sensor is medium, the receiving unit adjusts the frequency of data reception appropriately. In this way, by adjusting the frequency of data reception according to the remaining battery level of the sensor, it is possible to extend battery life.

[0038] The receiving unit can preferentially receive highly relevant data by taking into account the geographical location information of the sensor. For example, if the sensor is installed in a specific area, the receiving unit preferentially receives data from that area. Furthermore, if the sensor is movable, the receiving unit can also receive highly relevant data based on its current location. Furthermore, if the sensor covers multiple areas, the receiving unit can also preferentially receive data from important areas. For example, if the sensor is installed in a specific area, the receiving unit preferentially receives data from that area. If the sensor is movable, the receiving unit receives highly relevant data based on its current location. If the sensor covers multiple areas, the receiving unit preferentially receives data from important areas. In this way, highly relevant data can be efficiently received by taking into account the geographical location information of the sensor.

[0039] The receiving unit can share sensor data with other devices and centrally manage the data. For example, the receiving unit uploads the sensor data to a cloud to make it accessible to other devices. The receiving unit can also share the sensor data within a local network to be used by multiple devices. The receiving unit can also send the sensor data to a specific device and centrally manage the data on that device. For example, the receiving unit uploads the sensor data to a cloud to make it accessible to other devices. The sensor data is shared within a local network to be used by multiple devices. The sensor data is sent to a specific device and centrally managed on that device. This enables centralized management of data by sharing sensor data with other devices.

[0040] The receiving unit can store sensor data in the cloud so that it can be analyzed later. For example, the receiving unit uploads sensor data to the cloud in real time and analyzes it later. The receiving unit can also back up the sensor data to the cloud periodically and analyze it as needed. The receiving unit can also store sensor data in the cloud and access and analyze it from another device. For example, the receiving unit uploads sensor data to the cloud in real time and analyzes it later. The receiving unit can back up sensor data to the cloud periodically and analyze it as needed. The receiving unit can store sensor data in the cloud and access and analyze it from another device. In this way, storing sensor data in the cloud enables it to be analyzed later.

[0041] The analysis unit can detect abnormal values ​​in the sensor data and generate an alert when an abnormality occurs. For example, the analysis unit generates an alert when data from a temperature sensor exceeds a set range. The analysis unit can also generate an alert when data from a humidity sensor changes suddenly. The analysis unit can also generate an alert when a motion sensor detects abnormal movement. For example, the analysis unit generates an alert when data from a temperature sensor exceeds a set range. The analysis unit generates an alert when data from a humidity sensor changes suddenly. The analysis unit generates an alert when a motion sensor detects abnormal movement. In this way, by detecting abnormal values ​​in the sensor data, an immediate response can be made when an abnormality occurs.

[0042] The analysis unit can refer to the sensor data history and analyze long-term trends. For example, the analysis unit can refer to past temperature data and analyze temperature changes by season. The analysis unit can also refer to past humidity data and analyze humidity fluctuations over the course of a year. The analysis unit can also refer to past operation data and analyze operation patterns over a specific time period. For example, the analysis unit can refer to past temperature data and analyze temperature changes by season. It can refer to past humidity data and analyze humidity fluctuations over the course of a year. It can also refer to past operation data and analyze operation patterns over a specific time period. In this way, by referring to the sensor data history, long-term trends can be understood.

[0043] The analysis unit can analyze correlations between sensor data and integrate and analyze data from multiple sensors. For example, the analysis unit integrates data from a temperature sensor and a humidity sensor to analyze environmental changes. The analysis unit can also integrate data from a motion sensor and an environmental sensor to detect abnormal behavior. The analysis unit can also integrate data from multiple temperature sensors to analyze temperature changes over a wide range. For example, the analysis unit integrates data from a temperature sensor and a humidity sensor to analyze environmental changes. The analysis unit can integrate data from a motion sensor and an environmental sensor to detect abnormal behavior. The analysis unit can integrate data from multiple temperature sensors to analyze temperature changes over a wide range. In this way, by integrating and analyzing data from multiple sensors, more accurate analysis is possible.

[0044] The analysis unit can analyze the geographical distribution of sensor data to understand trends by region. For example, the analysis unit geographically analyzes data from a temperature sensor to understand temperature trends by region. The analysis unit can also geographically analyze data from a humidity sensor to understand humidity trends by region. The analysis unit can also geographically analyze data from a motion sensor to understand behavior patterns by region. For example, the analysis unit geographically analyzes data from a temperature sensor to understand temperature trends by region. The analysis unit geographically analyzes data from a humidity sensor to understand humidity trends by region. The analysis unit geographically analyzes data from a motion sensor to understand behavior patterns by region. In this way, by analyzing the geographical distribution of sensor data, trends by region can be understood.

[0045] The analysis unit can integrate the sensor data with other data sources to perform more accurate analysis. For example, the analysis unit can integrate the sensor data with weather data to analyze environmental changes. The analysis unit can also integrate the sensor data with traffic data to analyze traffic conditions. The analysis unit can also integrate the sensor data with energy data to analyze energy consumption. For example, the analysis unit can integrate the sensor data with weather data to analyze environmental changes. The analysis unit can integrate the sensor data with traffic data to analyze traffic conditions. The analysis unit can integrate the sensor data with energy data to analyze energy consumption. In this way, by integrating with other data sources, more accurate analysis is possible.

[0046] The analysis unit can link the analysis results of the sensor data with other systems and provide feedback in real time. For example, the analysis unit can link the analysis results with a smart home system and adjust the environment in real time. The analysis unit can also link the analysis results with a factory control system and optimize the production process in real time. The analysis unit can also link the analysis results with a medical system and monitor the patient's condition in real time. For example, the analysis unit can link the analysis results with a smart home system and adjust the environment in real time. The analysis results can link with a factory control system and optimize the production process in real time. The analysis results can link with a medical system and monitor the patient's condition in real time. This makes it possible to provide feedback in real time by linking with other systems.

[0047] The creation unit can automatically generate an app template based on the analysis results. For example, the creation unit generates a temperature management app template based on the analysis results of a temperature sensor. The creation unit can also generate a humidity management app template based on the analysis results of a humidity sensor. The creation unit can also generate a motion monitoring app template based on the analysis results of a motion sensor. For example, the creation unit generates a temperature management app template based on the analysis results of a temperature sensor. The creation unit generates a humidity management app template based on the analysis results of a humidity sensor. The creation unit generates a motion monitoring app template based on the analysis results of a motion sensor. This enables efficient app creation by generating app templates based on the analysis results.

[0048] The creation unit can suggest an optimal app creation method by referring to the user's past app creation history. The creation unit, for example, can suggest an optimal template based on the history of apps created by the user in the past. The creation unit can also suggest frequently used functions from the user's past app creation history. The creation unit can also analyze the user's past app creation history to suggest an efficient creation method. For example, the creation unit can suggest an optimal template based on the history of apps created by the user in the past. It can suggest frequently used functions from the user's past app creation history. It can analyze the user's past app creation history to suggest an efficient creation method. In this way, the optimal app creation method can be suggested by referring to the user's past app creation history.

[0049] The creation unit can improve the functionality of the app by reflecting user feedback. For example, the creation unit can improve the UI of the app based on user feedback. The creation unit can also add functions to the app based on user feedback. The creation unit can also improve performance of the app based on user feedback. For example, the creation unit can improve the UI of the app based on user feedback. Add functions to the app based on user feedback. Improve performance of the app based on user feedback. This allows the app's functionality to be continuously improved by reflecting user feedback.

[0050] The creation unit can apply different app creation algorithms depending on the type of sensor data. For example, the creation unit applies an algorithm to create a temperature management app based on data from a temperature sensor. The creation unit can also apply an algorithm to create a humidity management app based on data from a humidity sensor. The creation unit can also apply an algorithm to create a behavior monitoring app based on data from a behavior sensor. For example, the creation unit applies an algorithm to create a temperature management app based on data from a temperature sensor. It applies an algorithm to create a humidity management app based on data from a humidity sensor. It applies an algorithm to create a behavior monitoring app based on data from the behavior sensor. In this way, by applying an app creation algorithm depending on the type of sensor data, an optimal app can be created.

[0051] The creation unit can make suggestions when creating an app by referring to apps created by other users. For example, the creation unit can suggest an optimal template by referring to a temperature control app created by other users. The creation unit can also suggest optimal functions by referring to a humidity control app created by other users. The creation unit can also suggest an optimal UI by referring to a behavior monitoring app created by other users. For example, the creation unit can suggest an optimal template by referring to a temperature control app created by other users. It can suggest optimal functions by referring to a humidity control app. It can suggest an optimal UI by referring to a behavior monitoring app. This makes it possible to create an optimal app by referring to apps created by other users.

[0052] The creation unit can propose an optimal app by taking into consideration the user's device information when creating an app. For example, if the user uses a smartphone, the creation unit can propose an app optimized for the smartphone. Furthermore, if the user uses a tablet, the creation unit can also propose an app optimized for the tablet. Furthermore, if the user uses a smartwatch, the creation unit can also propose an app optimized for the smartwatch. For example, if the user uses a smartphone, the creation unit can propose an app optimized for the smartphone. If the user uses a tablet, the creation unit can propose an app optimized for the tablet. If the user uses a smartwatch, the creation unit can propose an app optimized for the smartwatch. In this way, the optimal app can be proposed by taking into consideration the user's device information.

[0053] The sensor unit can automatically adjust the installation method depending on the type of sensor. For example, the sensor unit can install a temperature sensor in a high position to collect accurate temperature data. The sensor unit can also install a humidity sensor in a low position to collect accurate humidity data. The sensor unit can also install a motion sensor in a position that covers a wide range to collect accurate motion data. For example, the sensor unit can install a temperature sensor in a high position to collect accurate temperature data. The sensor unit can install a humidity sensor in a low position to collect accurate humidity data. The sensor unit can install a motion sensor in a position that covers a wide range to collect accurate motion data. This makes it possible to collect accurate data by adjusting the installation method depending on the type of sensor.

[0054] The sensor unit can monitor the installation environment of the sensor and propose an optimal installation method. The sensor unit, for example, monitors the temperature of the installation environment and proposes an optimal installation location for the temperature sensor. The sensor unit can also monitor the humidity of the installation environment and propose an optimal installation location for the humidity sensor. The sensor unit can also monitor the operation of the installation environment and propose an optimal installation location for the operation sensor. For example, the sensor unit monitors the temperature of the installation environment and proposes an optimal installation location for the temperature sensor. The sensor unit monitors the humidity of the installation environment and proposes an optimal installation location for the humidity sensor. The sensor unit can monitor the operation of the installation environment and propose an optimal installation location for the operation sensor. In this way, by monitoring the sensor installation environment, an optimal installation method can be proposed.

[0055] The sensor unit can check the operation of the sensor after installation to check whether it is operating normally. For example, the sensor unit can check the operation of a temperature sensor after installation to check whether accurate temperature data can be collected. The sensor unit can also check the operation of a humidity sensor after installation to check whether accurate humidity data can be collected. The sensor unit can also check the operation of an operating sensor after installation to check whether accurate operating data can be collected. For example, the sensor unit can check the operation of a temperature sensor after installation to check whether accurate temperature data can be collected. The sensor unit can check the operation of a humidity sensor after installation to check whether accurate humidity data can be collected. The sensor unit can check the operation of an operating sensor after installation to check whether accurate operating data can be collected. In this way, by checking the operation of the sensors after installation, accurate data can be collected.

[0056] The sensor unit can make suggestions for avoiding interference with other devices when installing sensors. For example, the sensor unit installs sensors at an appropriate distance to avoid radio wave interference with other devices. The sensor unit can also install sensors in appropriate positions to avoid physical interference with other devices. The sensor unit can also install sensors using different frequency bands to avoid data interference with other devices. For example, the sensor unit installs sensors at an appropriate distance to avoid radio wave interference with other devices. The sensor unit installs sensors in appropriate positions to avoid physical interference. The sensor unit installs sensors using different frequency bands to avoid data interference. This makes it possible to avoid interference with other devices and collect accurate data.

[0057] The sensor unit can suggest the optimal installation method by referring to the user's past installation history when installing a sensor. The sensor unit can suggest the optimal installation method, for example, based on the history of sensors that the user has installed in the past. The sensor unit can also suggest a frequently used installation method from the user's past installation history. The sensor unit can also analyze the user's past installation history and suggest an efficient installation method. For example, the sensor unit can suggest the optimal installation method based on the history of sensors that the user has installed in the past. It can suggest a frequently used installation method from the user's past installation history. It can analyze the user's past installation history and suggest an efficient installation method. In this way, the optimal installation method can be suggested by referring to the user's past installation history.

[0058] The sensor unit can propose the optimal installation location by taking into consideration the geographical location information of the user when installing the sensor. The sensor unit proposes the optimal installation location for the sensor based on, for example, the user's current location. The sensor unit can also propose a sensor installation location that is suitable for the environment based on the user's geographical location information. The sensor unit can also propose a sensor installation location that is suitable for data collection based on the user's geographical location information. For example, the sensor unit proposes the optimal installation location for the sensor based on the user's current location. The sensor unit proposes a sensor installation location that is suitable for the environment based on the user's geographical location information. The sensor unit proposes a sensor installation location that is suitable for data collection based on the user's geographical location information. In this way, the optimal installation location can be proposed by taking into consideration the user's geographical location information.

[0059] The interface unit can select the optimal display method by referring to the user's past operation history. The interface unit, for example, suggests the optimal display method based on display methods used by the user in the past. The interface unit can also suggest a frequently used display method from the user's past operation history. The interface unit can also analyze the user's past operation history and suggest an efficient display method. For example, the interface unit suggests the optimal display method based on display methods used by the user in the past. It suggests a frequently used display method from the user's past operation history. It analyzes the user's past operation history and suggests an efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history.

[0060] The interface unit can customize the display content according to the user's current task. For example, when the user is analyzing data, the interface unit provides display content that emphasizes the analysis results. Furthermore, when the user is creating an app, the interface unit can also provide display content that emphasizes the creation steps. Furthermore, when the user is installing a sensor, the interface unit can also provide display content that emphasizes the installation steps. For example, when the user is analyzing data, the interface unit provides display content that emphasizes the analysis results. When the user is creating an app, the interface unit provides display content that emphasizes the creation steps. When the user is installing a sensor, the interface unit provides display content that emphasizes the installation steps. In this way, by customizing the display content according to the user's current task, efficient work is possible.

[0061] The interface unit can improve the display method by reflecting user feedback. For example, the interface unit improves the UI of the interface based on user feedback. The interface unit can also add display content based on user feedback. The interface unit can also improve display performance based on user feedback. For example, the interface unit improves the UI of the interface based on user feedback. Adds display content based on user feedback. Improves display performance based on user feedback. In this way, the display method can be continuously improved by reflecting user feedback.

[0062] The interface unit can select the optimal display method by taking into account the user's device information. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, when the user is using a tablet, the interface unit can also provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the interface unit can also provide a display method that is simple and highly visible. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. When the user is using a tablet, the interface unit provides a display method that is optimized for a large screen. When the user is using a smartwatch, the interface unit provides a display method that is simple and highly visible. This makes it possible to select the optimal display method by taking into account the user's device information.

[0063] The interface unit can make the display content multilingual in accordance with the user's language setting. The interface unit, for example, automatically sets the display content based on the language setting of the user's device. The interface unit can also provide a language switching function when the user uses multiple languages. The interface unit can also provide the display content in a specific language when the user selects that language. For example, the interface unit automatically sets the display content based on the language setting of the user's device. The interface unit provides a language switching function when the user uses multiple languages. When the user selects a specific language, the display content is provided in that language. This makes it possible to make the display content multilingual in accordance with the user's language setting, thereby providing a display that is suitable for the user.

[0064] The interface unit can customize the display method according to the visual and auditory characteristics of the user. For example, if the user is visually impaired, the interface unit provides large characters and audio guidance. Furthermore, if the user is hearing impaired, the interface unit can also emphasize the visual display method. Furthermore, the interface unit can adjust the color contrast if the user is color blind. For example, if the user is visually impaired, the interface unit provides large characters and audio guidance. If the user is hearing impaired, the interface unit emphasizes the visual display method. If the user is color blind, the interface unit adjusts the color contrast. In this way, the display method can be customized according to the visual and auditory characteristics of the user, thereby enabling a display that is suitable for the user.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can detect abnormal values ​​in sensor data and generate alerts when an abnormality occurs. For example, an alert is generated when temperature sensor data exceeds a set range. An alert can also be generated when humidity sensor data changes suddenly. Furthermore, an alert can also be generated when a motion sensor detects abnormal movement. This allows for immediate response when an abnormal value in sensor data is detected.

[0067] The receiver can monitor the remaining battery level of the sensor and adjust the frequency of data reception as necessary. For example, if the remaining battery level of the sensor is low, the frequency of data reception can be reduced to extend battery life. Also, if the remaining battery level of the sensor is sufficient, the frequency of data reception can be increased to collect more detailed data. Furthermore, if the remaining battery level of the sensor is medium, the frequency of data reception can be adjusted appropriately. In this way, by adjusting the frequency of data reception according to the remaining battery level of the sensor, battery life can be extended.

[0068] The analysis unit can refer to the sensor data history and analyze long-term trends. For example, it can refer to past temperature data to analyze seasonal temperature changes. It can also refer to past humidity data to analyze annual humidity fluctuations. It can also refer to past operation data to analyze operation patterns during specific time periods. This makes it possible to understand long-term trends by referring to the sensor data history.

[0069] The analysis unit can analyze correlations between sensor data and integrate and analyze data from multiple sensors. For example, data from a temperature sensor and a humidity sensor can be integrated to analyze environmental changes. Data from a motion sensor and an environmental sensor can also be integrated to detect abnormal behavior. Furthermore, data from multiple temperature sensors can be integrated to analyze temperature changes over a wide range. This allows for more accurate analysis by integrating and analyzing data from multiple sensors.

[0070] The analysis unit can integrate sensor data with other data sources to perform more accurate analysis. For example, sensor data can be integrated with weather data to analyze environmental changes. Sensor data can also be integrated with traffic data to analyze traffic conditions. Furthermore, sensor data can be integrated with energy data to analyze energy consumption. In this way, integration with other data sources enables more accurate analysis.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The receiver receives sensor data. The sensor data includes temperature data, humidity data, location data, etc. The receiver can, for example, receive data from a temperature sensor in real time, and also receive data from a humidity sensor and location sensor in real time. The receiver receives data from a temperature sensor at regular intervals, data from a humidity sensor in an event-driven manner, and data from a location sensor periodically. Step 2: The analysis unit analyzes the data received by the receiving unit. The analysis is performed by methods such as data filtering, statistical analysis, and the application of machine learning algorithms. For example, the analysis unit may use filtering techniques to remove noise, statistical analysis to identify data trends, and machine learning algorithms to analyze data patterns. Step 3: The creation unit creates an app based on the data analyzed by the analysis unit. The app is created according to criteria such as the type of app, the platform used, and the creation procedure. For example, the creation unit creates a temperature control app based on the temperature data, a humidity control app based on the humidity data, and a location tracking app based on the location data.

[0073] (Example 2) A system according to an embodiment of the present invention links AR / MR goggles and IoT sensors, enabling anyone to easily create apps. This system allows users to wear AR / MR goggles and install IoT sensors to receive and analyze sensor data and create apps. For example, a user can install temperature and humidity sensors in a factory and wear AR / MR goggles. The location and type of sensors can be freely selected. The AR / MR goggles then receive data from the IoT sensors. For example, temperature data from a temperature sensor and humidity data from a humidity sensor are received in real time. This data is analyzed by an analysis unit within the AR / MR goggles. Based on the analyzed data, users can intuitively create apps. For example, they can create apps that display an alert when the temperature exceeds a certain range or automatically activate a humidifier when humidity drops. Users can easily configure the app through the interface of the AR / MR goggles. This allows anyone to easily create apps without specialized knowledge. The system allows users to create the apps they need, improving work efficiency. You can also create an app that automates temperature and humidity management at home to create a comfortable living environment.

[0074] An application development system according to an embodiment includes a receiving unit, an analyzing unit, and a creating unit. The receiving unit receives sensor data. The sensor data includes, but is not limited to, temperature data, humidity data, and location data. For example, the receiving unit receives data from a temperature sensor in real time. The receiving unit can also receive data from a humidity sensor in real time. The receiving unit can also receive data from a location sensor in real time. For example, the receiving unit receives data from the temperature sensor at regular intervals and monitors temperature changes in real time. The data from the humidity sensor is received in an event-driven manner, receiving data only when humidity changes. The data from the location sensor is received periodically and continuously monitoring location changes. The analyzing unit analyzes the data received by the receiving unit. The analysis can be performed by, for example, data filtering, statistical analysis, or application of a machine learning algorithm, but is not limited to, such examples. For example, the analyzing unit filters the data to remove noise. The analyzing unit can also perform statistical analysis to identify data trends. The analyzing unit can also apply a machine learning algorithm to analyze data patterns. For example, the analysis unit uses filtering techniques to remove noise from sensor data. Statistical analysis calculates the average value and standard deviation of the data to identify trends in the data. Machine learning algorithms learn data patterns and detect outliers. The creation unit creates an app based on the data analyzed by the analysis unit. The app is created according to criteria such as, for example, the type of app, the platform used, and the creation procedure, but is not limited to these examples. For example, the creation unit creates a temperature control app based on temperature data. The creation unit can also create a humidity control app based on humidity data. The creation unit can also create a location tracking app based on location data. For example, the creation unit creates a temperature control app that displays an alert when the temperature exceeds a certain range. The creation unit can also create a humidity control app that automatically activates a humidifier when humidity drops. The location tracking app displays a movement route based on location data.As a result, the application creation system according to the embodiment receives and analyzes sensor data and creates an application, allowing anyone to easily create an application.

[0075] The application creation system includes a sensor unit that standardizes the type and installation method of sensors. The sensor unit standardizes the type and installation method of sensors. Standardization includes, but is not limited to, examples of sensor interfaces, communication protocols, and installation procedures. For example, the sensor unit standardizes the interface of temperature sensors so that sensors from different manufacturers can be connected using the same interface. The sensor unit can also standardize the communication protocol so that data can be exchanged between different sensors. The sensor unit can also standardize the installation procedure so that sensor installation can be simplified. For example, the sensor unit standardizes the interface of temperature sensors so that sensors from different manufacturers can be connected using the same interface. The communication protocol is standardized so that data can be exchanged between different sensors. The installation procedure is standardized so that sensor installation can be simplified. Standardizing the type and installation method of sensors enables flexible sensor installation.

[0076] The application creation system includes an interface unit that provides an interface that is easy for users to operate. The interface unit provides an interface that allows users to operate intuitively. Ease of operation includes, but is not limited to, user interface design, simplified operation procedures, and provision of a help function, for example. For example, the interface unit may adopt a simple, highly visible design to allow users to operate intuitively. The interface unit may also simplify the operation procedures to allow users to operate without hesitation. The interface unit may also provide a help function to allow users to immediately confirm how to operate. For example, the interface unit may adopt a simple, highly visible design to allow users to operate intuitively. The operation procedures are simplified to allow users to operate without hesitation. The help function is provided to allow users to immediately confirm how to operate. This facilitates application creation by providing an interface that is intuitive for users to operate.

[0077] The receiving unit can receive sensor data in real time. Real time includes, but is not limited to, for example, a delay time in receiving data and an update frequency. For example, the receiving unit receives data from a temperature sensor in real time. The receiving unit can also receive data from a humidity sensor in real time. The receiving unit can also receive data from a position sensor in real time. For example, the receiving unit receives data from a temperature sensor at regular intervals and monitors temperature changes in real time. Data from a humidity sensor is received in an event-driven manner, and data is received only when humidity changes. Data from a position sensor is received periodically and position changes are continuously monitored. As a result, receiving sensor data in real time enables immediate data analysis.

[0078] The analysis unit can analyze the received data in real time. Real time includes, but is not limited to, for example, a delay time in data analysis and an update frequency. The analysis unit can, for example, filter the data to remove noise. The analysis unit can also perform statistical analysis to identify data trends. The analysis unit can also apply machine learning algorithms to analyze data patterns. For example, the analysis unit can use filtering techniques to remove noise from sensor data. Statistical analysis calculates the average value and standard deviation of the data to identify data trends. Machine learning algorithms learn data patterns and detect outliers. This allows for real-time data analysis, enabling immediate feedback.

[0079] The creation unit can create an app based on the analysis results. App creation includes, but is not limited to, criteria such as the type of app, the platform used, and the creation procedure. For example, the creation unit can create a temperature control app based on temperature data. The creation unit can also create a humidity control app based on humidity data. The creation unit can also create a location tracking app based on location data. For example, the creation unit can create a temperature control app that displays an alert when the temperature exceeds a certain range. The creation unit can also create a humidity control app that automatically activates a humidifier when humidity drops. The location tracking app displays a movement route based on the location data. This allows users to intuitively create apps by creating apps based on the analysis results.

[0080] The receiving unit can estimate the user's emotions and adjust the timing of receiving sensor data based on the estimated user emotions. For example, when the user is stressed, the receiving unit reduces the frequency of receiving sensor data to reduce the burden on the user. Furthermore, when the user is relaxed, the receiving unit can increase the frequency of receiving sensor data to collect detailed data. Furthermore, when the user is in a hurry, the receiving unit can prioritize receiving only important sensor data. For example, when the user is stressed, the receiving unit reduces the frequency of receiving sensor data to reduce the burden on the user. When the user is relaxed, the receiving unit increases the frequency of receiving sensor data to collect detailed data. When the user is in a hurry, the receiving unit prioritizes receiving only important sensor data. In this way, the timing of receiving sensor data is adjusted according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The receiving unit can automatically switch the data receiving method depending on the type of sensor. For example, the receiving unit receives data from a temperature sensor at regular intervals and monitors temperature changes in real time. The receiving unit can also receive data from a motion sensor in an event-driven manner and receive data only when motion is detected. The receiving unit can also periodically receive data from an environmental sensor and continuously monitor environmental changes. For example, the receiving unit receives data from a temperature sensor at regular intervals and monitors temperature changes in real time. The receiving unit receives data from a motion sensor in an event-driven manner and receives data only when motion is detected. The receiving unit receives data from an environmental sensor in a regular manner and continuously monitors environmental changes. This enables efficient data reception by switching the data receiving method depending on the type of sensor.

[0082] The receiving unit can adjust the data reception range based on the installation location of the sensor. For example, if the sensor covers a wide area, the receiving unit widens the data reception range to collect detailed information. Furthermore, if the sensor monitors a specific area, the receiving unit can narrow the data reception range to improve accuracy. Furthermore, if the sensor is movable, the receiving unit can dynamically adjust the reception range according to the installation location. For example, if the sensor covers a wide area, the receiving unit widens the data reception range to collect detailed information. If the sensor monitors a specific area, the receiving unit narrows the data reception range to improve accuracy. If the sensor is movable, the receiving range is dynamically adjusted according to the installation location. In this way, highly accurate data collection is possible by adjusting the data reception range based on the installation location of the sensor.

[0083] The receiving unit can monitor the remaining battery level of the sensor and adjust the frequency of data reception as necessary. For example, when the remaining battery level of the sensor is low, the receiving unit reduces the frequency of data reception to extend battery life. Furthermore, when the remaining battery level of the sensor is sufficient, the receiving unit can increase the frequency of data reception to collect detailed data. Furthermore, when the remaining battery level of the sensor is medium, the receiving unit can adjust the frequency of data reception appropriately. For example, when the remaining battery level of the sensor is low, the receiving unit reduces the frequency of data reception to extend battery life. When the remaining battery level of the sensor is sufficient, the receiving unit increases the frequency of data reception to collect detailed data. When the remaining battery level of the sensor is medium, the receiving unit adjusts the frequency of data reception appropriately. In this way, by adjusting the frequency of data reception according to the remaining battery level of the sensor, it is possible to extend battery life.

[0084] The receiving unit can estimate the user's emotion and determine the priority of the sensor data to be received based on the estimated user's emotion. For example, when the user is stressed, the receiving unit prioritizes receiving only important sensor data. Furthermore, when the user is relaxed, the receiving unit can receive all sensor data equally. Furthermore, when the user is in a hurry, the receiving unit can prioritize receiving urgent sensor data. For example, when the user is stressed, the receiving unit prioritizes receiving only important sensor data. When the user is relaxed, the receiving unit receives all sensor data equally. When the user is in a hurry, the receiving unit prioritizes receiving urgent sensor data. In this way, by determining the priority of the sensor data according to the user's emotion, important data can be received preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The receiving unit can preferentially receive highly relevant data by taking into account the geographical location information of the sensor. For example, if the sensor is installed in a specific area, the receiving unit preferentially receives data from that area. Furthermore, if the sensor is movable, the receiving unit can also receive highly relevant data based on its current location. Furthermore, if the sensor covers multiple areas, the receiving unit can also preferentially receive data from important areas. For example, if the sensor is installed in a specific area, the receiving unit preferentially receives data from that area. If the sensor is movable, the receiving unit receives highly relevant data based on its current location. If the sensor covers multiple areas, the receiving unit preferentially receives data from important areas. In this way, highly relevant data can be efficiently received by taking into account the geographical location information of the sensor.

[0086] The receiving unit can share sensor data with other devices and centrally manage the data. For example, the receiving unit uploads the sensor data to a cloud to make it accessible to other devices. The receiving unit can also share the sensor data within a local network to be used by multiple devices. The receiving unit can also send the sensor data to a specific device and centrally manage the data on that device. For example, the receiving unit uploads the sensor data to a cloud to make it accessible to other devices. The sensor data is shared within a local network to be used by multiple devices. The sensor data is sent to a specific device and centrally managed on that device. This enables centralized management of data by sharing sensor data with other devices.

[0087] The receiving unit can store sensor data in the cloud so that it can be analyzed later. For example, the receiving unit uploads sensor data to the cloud in real time and analyzes it later. The receiving unit can also back up the sensor data to the cloud periodically and analyze it as needed. The receiving unit can also store sensor data in the cloud and access and analyze it from another device. For example, the receiving unit uploads sensor data to the cloud in real time and analyzes it later. The receiving unit can back up sensor data to the cloud periodically and analyze it as needed. The receiving unit can store sensor data in the cloud and access and analyze it from another device. In this way, storing sensor data in the cloud enables it to be analyzed later.

[0088] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is stressed, the analysis unit can reduce the level of analysis detail to provide concise results. Furthermore, if the user is relaxed, the analysis unit can increase the level of analysis detail to provide detailed results. Furthermore, if the user is in a hurry, the analysis unit can analyze only important data to provide quick results. For example, if the user is stressed, the analysis unit can reduce the level of analysis detail to provide concise results. If the user is relaxed, the analysis unit can increase the level of analysis detail to provide detailed results. If the user is in a hurry, the analysis unit can analyze only important data to provide quick results. This allows the level of analysis detail to be adjusted according to the user's emotions, thereby providing analysis results suited to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The analysis unit can detect abnormal values ​​in the sensor data and generate an alert when an abnormality occurs. For example, the analysis unit generates an alert when data from a temperature sensor exceeds a set range. The analysis unit can also generate an alert when data from a humidity sensor changes suddenly. The analysis unit can also generate an alert when a motion sensor detects abnormal movement. For example, the analysis unit generates an alert when data from a temperature sensor exceeds a set range. The analysis unit generates an alert when data from a humidity sensor changes suddenly. The analysis unit generates an alert when a motion sensor detects abnormal movement. In this way, by detecting abnormal values ​​in the sensor data, an immediate response can be made when an abnormality occurs.

[0090] The analysis unit can refer to the sensor data history and analyze long-term trends. For example, the analysis unit can refer to past temperature data and analyze temperature changes by season. The analysis unit can also refer to past humidity data and analyze humidity fluctuations over the course of a year. The analysis unit can also refer to past operation data and analyze operation patterns over a specific time period. For example, the analysis unit can refer to past temperature data and analyze temperature changes by season. It can refer to past humidity data and analyze humidity fluctuations over the course of a year. It can also refer to past operation data and analyze operation patterns over a specific time period. In this way, by referring to the sensor data history, long-term trends can be understood.

[0091] The analysis unit can analyze correlations between sensor data and integrate and analyze data from multiple sensors. For example, the analysis unit integrates data from a temperature sensor and a humidity sensor to analyze environmental changes. The analysis unit can also integrate data from a motion sensor and an environmental sensor to detect abnormal behavior. The analysis unit can also integrate data from multiple temperature sensors to analyze temperature changes over a wide range. For example, the analysis unit integrates data from a temperature sensor and a humidity sensor to analyze environmental changes. The analysis unit can integrate data from a motion sensor and an environmental sensor to detect abnormal behavior. The analysis unit can integrate data from multiple temperature sensors to analyze temperature changes over a wide range. In this way, by integrating and analyzing data from multiple sensors, more accurate analysis is possible.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a concise and highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the user is stressed, the analysis unit provides a concise and highly visible display method. If the user is relaxed, the analysis unit provides a display method including detailed information. If the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby enabling a display that is suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The analysis unit can analyze the geographical distribution of sensor data to understand trends by region. For example, the analysis unit geographically analyzes data from a temperature sensor to understand temperature trends by region. The analysis unit can also geographically analyze data from a humidity sensor to understand humidity trends by region. The analysis unit can also geographically analyze data from a motion sensor to understand behavior patterns by region. For example, the analysis unit geographically analyzes data from a temperature sensor to understand temperature trends by region. The analysis unit geographically analyzes data from a humidity sensor to understand humidity trends by region. The analysis unit geographically analyzes data from a motion sensor to understand behavior patterns by region. In this way, by analyzing the geographical distribution of sensor data, trends by region can be understood.

[0094] The analysis unit can integrate the sensor data with other data sources to perform more accurate analysis. For example, the analysis unit can integrate the sensor data with weather data to analyze environmental changes. The analysis unit can also integrate the sensor data with traffic data to analyze traffic conditions. The analysis unit can also integrate the sensor data with energy data to analyze energy consumption. For example, the analysis unit can integrate the sensor data with weather data to analyze environmental changes. The analysis unit can integrate the sensor data with traffic data to analyze traffic conditions. The analysis unit can integrate the sensor data with energy data to analyze energy consumption. In this way, by integrating with other data sources, more accurate analysis is possible.

[0095] The analysis unit can link the analysis results of the sensor data with other systems and provide feedback in real time. For example, the analysis unit can link the analysis results with a smart home system and adjust the environment in real time. The analysis unit can also link the analysis results with a factory control system and optimize the production process in real time. The analysis unit can also link the analysis results with a medical system and monitor the patient's condition in real time. For example, the analysis unit can link the analysis results with a smart home system and adjust the environment in real time. The analysis results can link with a factory control system and optimize the production process in real time. The analysis results can link with a medical system and monitor the patient's condition in real time. This makes it possible to provide feedback in real time by linking with other systems.

[0096] The creation unit can estimate the user's emotions and adjust the app creation method based on the estimated user emotions. For example, if the user is stressed, the creation unit provides a simple app creation method. Furthermore, if the user is relaxed, the creation unit can provide a detailed customizable app creation method. Furthermore, if the user is in a hurry, the creation unit can provide a quick app creation method. For example, if the user is stressed, the creation unit provides a simple app creation method. If the user is relaxed, the creation unit provides a detailed customizable app creation method. If the user is in a hurry, the creation unit provides a quick app creation method. This makes it possible to create an app that is suitable for the user by adjusting the app creation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The creation unit can automatically generate an app template based on the analysis results. For example, the creation unit generates a temperature management app template based on the analysis results of a temperature sensor. The creation unit can also generate a humidity management app template based on the analysis results of a humidity sensor. The creation unit can also generate a motion monitoring app template based on the analysis results of a motion sensor. For example, the creation unit generates a temperature management app template based on the analysis results of a temperature sensor. The creation unit generates a humidity management app template based on the analysis results of a humidity sensor. The creation unit generates a motion monitoring app template based on the analysis results of a motion sensor. This enables efficient app creation by generating app templates based on the analysis results.

[0098] The creation unit can suggest an optimal app creation method by referring to the user's past app creation history. The creation unit, for example, can suggest an optimal template based on the history of apps created by the user in the past. The creation unit can also suggest frequently used functions from the user's past app creation history. The creation unit can also analyze the user's past app creation history to suggest an efficient creation method. For example, the creation unit can suggest an optimal template based on the history of apps created by the user in the past. It can suggest frequently used functions from the user's past app creation history. It can analyze the user's past app creation history to suggest an efficient creation method. In this way, the optimal app creation method can be suggested by referring to the user's past app creation history.

[0099] The creation unit can improve the functionality of the app by reflecting user feedback. For example, the creation unit can improve the UI of the app based on user feedback. The creation unit can also add functions to the app based on user feedback. The creation unit can also improve performance of the app based on user feedback. For example, the creation unit can improve the UI of the app based on user feedback. Add functions to the app based on user feedback. Improve performance of the app based on user feedback. This allows the app's functionality to be continuously improved by reflecting user feedback.

[0100] The creation unit can estimate the user's emotions and determine the priority of apps based on the estimated user emotions. For example, when the user is stressed, the creation unit prioritizes creating important apps. Furthermore, when the user is relaxed, the creation unit can also create all apps equally. Furthermore, when the user is in a hurry, the creation unit can also prioritize creating apps with high urgency. For example, when the user is stressed, the creation unit prioritizes creating important apps. When the user is relaxed, the creation unit creates all apps equally. When the user is in a hurry, the creation unit prioritizes creating apps with high urgency. In this way, by determining the priority of apps according to the user's emotions, important apps can be created with priority. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The creation unit can apply different app creation algorithms depending on the type of sensor data. For example, the creation unit applies an algorithm to create a temperature management app based on data from a temperature sensor. The creation unit can also apply an algorithm to create a humidity management app based on data from a humidity sensor. The creation unit can also apply an algorithm to create a behavior monitoring app based on data from a behavior sensor. For example, the creation unit applies an algorithm to create a temperature management app based on data from a temperature sensor. It applies an algorithm to create a humidity management app based on data from a humidity sensor. It applies an algorithm to create a behavior monitoring app based on data from the behavior sensor. In this way, by applying an app creation algorithm depending on the type of sensor data, an optimal app can be created.

[0102] The creation unit can make suggestions when creating an app by referring to apps created by other users. For example, the creation unit can suggest an optimal template by referring to a temperature control app created by other users. The creation unit can also suggest optimal functions by referring to a humidity control app created by other users. The creation unit can also suggest an optimal UI by referring to a behavior monitoring app created by other users. For example, the creation unit can suggest an optimal template by referring to a temperature control app created by other users. It can suggest optimal functions by referring to a humidity control app. It can suggest an optimal UI by referring to a behavior monitoring app. This makes it possible to create an optimal app by referring to apps created by other users.

[0103] The creation unit can propose an optimal app by taking into consideration the user's device information when creating an app. For example, if the user uses a smartphone, the creation unit can propose an app optimized for the smartphone. Furthermore, if the user uses a tablet, the creation unit can also propose an app optimized for the tablet. Furthermore, if the user uses a smartwatch, the creation unit can also propose an app optimized for the smartwatch. For example, if the user uses a smartphone, the creation unit can propose an app optimized for the smartphone. If the user uses a tablet, the creation unit can propose an app optimized for the tablet. If the user uses a smartwatch, the creation unit can propose an app optimized for the smartwatch. In this way, the optimal app can be proposed by taking into consideration the user's device information.

[0104] The sensor unit can estimate the user's emotions and suggest a sensor installation location based on the estimated user's emotions. For example, when the user is stressed, the sensor unit installs the sensor in an easily accessible location. Furthermore, when the user is relaxed, the sensor unit can install the sensor in a location where optimal data can be collected. Furthermore, when the user is in a hurry, the sensor unit can install the sensor in a location where installation can be quickly performed. For example, when the user is stressed, the sensor unit installs the sensor in an easily accessible location. When the user is relaxed, the sensor unit installs the sensor in a location where optimal data can be collected. When the user is in a hurry, the sensor unit installs the sensor in a location where installation can be quickly performed. This allows the sensor installation location to be suggested according to the user's emotions, enabling optimal installation. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The sensor unit can automatically adjust the installation method depending on the type of sensor. For example, the sensor unit can install a temperature sensor in a high position to collect accurate temperature data. The sensor unit can also install a humidity sensor in a low position to collect accurate humidity data. The sensor unit can also install a motion sensor in a position that covers a wide range to collect accurate motion data. For example, the sensor unit can install a temperature sensor in a high position to collect accurate temperature data. The sensor unit can install a humidity sensor in a low position to collect accurate humidity data. The sensor unit can install a motion sensor in a position that covers a wide range to collect accurate motion data. This makes it possible to collect accurate data by adjusting the installation method depending on the type of sensor.

[0106] The sensor unit can monitor the installation environment of the sensor and propose an optimal installation method. The sensor unit, for example, monitors the temperature of the installation environment and proposes an optimal installation location for the temperature sensor. The sensor unit can also monitor the humidity of the installation environment and propose an optimal installation location for the humidity sensor. The sensor unit can also monitor the operation of the installation environment and propose an optimal installation location for the operation sensor. For example, the sensor unit monitors the temperature of the installation environment and proposes an optimal installation location for the temperature sensor. The sensor unit monitors the humidity of the installation environment and proposes an optimal installation location for the humidity sensor. The sensor unit can monitor the operation of the installation environment and propose an optimal installation location for the operation sensor. In this way, by monitoring the sensor installation environment, an optimal installation method can be proposed.

[0107] The sensor unit can check the operation of the sensor after installation to check whether it is operating normally. For example, the sensor unit can check the operation of a temperature sensor after installation to check whether accurate temperature data can be collected. The sensor unit can also check the operation of a humidity sensor after installation to check whether accurate humidity data can be collected. The sensor unit can also check the operation of an operating sensor after installation to check whether accurate operating data can be collected. For example, the sensor unit can check the operation of a temperature sensor after installation to check whether accurate temperature data can be collected. The sensor unit can check the operation of a humidity sensor after installation to check whether accurate humidity data can be collected. The sensor unit can check the operation of an operating sensor after installation to check whether accurate operating data can be collected. In this way, by checking the operation of the sensors after installation, accurate data can be collected.

[0108] The sensor unit can estimate the user's emotions and determine the installation order of sensors based on the estimated user emotions. For example, when the user is stressed, the sensor unit prioritizes installing important sensors. Furthermore, when the user is relaxed, the sensor unit can also install all sensors evenly. Furthermore, when the user is in a hurry, the sensor unit can prioritize installing sensors with high urgency. For example, when the user is stressed, the sensor unit prioritizes installing important sensors. When the user is relaxed, all sensors are installed evenly. When the user is in a hurry, the sensor unit prioritizes installing sensors with high urgency. This enables efficient installation by determining the installation order of sensors according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The sensor unit can make suggestions for avoiding interference with other devices when installing sensors. For example, the sensor unit installs sensors at an appropriate distance to avoid radio wave interference with other devices. The sensor unit can also install sensors in appropriate positions to avoid physical interference with other devices. The sensor unit can also install sensors using different frequency bands to avoid data interference with other devices. For example, the sensor unit installs sensors at an appropriate distance to avoid radio wave interference with other devices. The sensor unit installs sensors in appropriate positions to avoid physical interference. The sensor unit installs sensors using different frequency bands to avoid data interference. This makes it possible to avoid interference with other devices and collect accurate data.

[0110] The sensor unit can suggest the optimal installation method by referring to the user's past installation history when installing a sensor. The sensor unit can suggest the optimal installation method, for example, based on the history of sensors that the user has installed in the past. The sensor unit can also suggest a frequently used installation method from the user's past installation history. The sensor unit can also analyze the user's past installation history and suggest an efficient installation method. For example, the sensor unit can suggest the optimal installation method based on the history of sensors that the user has installed in the past. It can suggest a frequently used installation method from the user's past installation history. It can analyze the user's past installation history and suggest an efficient installation method. In this way, the optimal installation method can be suggested by referring to the user's past installation history.

[0111] The sensor unit can propose the optimal installation location by taking into consideration the geographical location information of the user when installing the sensor. The sensor unit proposes the optimal installation location for the sensor based on, for example, the user's current location. The sensor unit can also propose a sensor installation location that is suitable for the environment based on the user's geographical location information. The sensor unit can also propose a sensor installation location that is suitable for data collection based on the user's geographical location information. For example, the sensor unit proposes the optimal installation location for the sensor based on the user's current location. The sensor unit proposes a sensor installation location that is suitable for the environment based on the user's geographical location information. The sensor unit proposes a sensor installation location that is suitable for data collection based on the user's geographical location information. In this way, the optimal installation location can be proposed by taking into consideration the user's geographical location information.

[0112] The interface unit can estimate the user's emotion and adjust the interface display method based on the estimated user emotion. For example, when the user is stressed, the interface unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the interface unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the interface unit can provide a display method that focuses on the main points. For example, when the user is stressed, the interface unit provides a simple, highly visible display method. When the user is relaxed, the interface unit provides a display method including detailed information. When the user is in a hurry, the interface unit provides a display method that focuses on the main points. This allows the interface display method to be adjusted according to the user's emotion, thereby enabling a display that is suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The interface unit can select the optimal display method by referring to the user's past operation history. The interface unit, for example, suggests the optimal display method based on display methods used by the user in the past. The interface unit can also suggest a frequently used display method from the user's past operation history. The interface unit can also analyze the user's past operation history and suggest an efficient display method. For example, the interface unit suggests the optimal display method based on display methods used by the user in the past. It suggests a frequently used display method from the user's past operation history. It analyzes the user's past operation history and suggests an efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history.

[0114] The interface unit can customize the display content according to the user's current task. For example, when the user is analyzing data, the interface unit provides display content that emphasizes the analysis results. Furthermore, when the user is creating an app, the interface unit can also provide display content that emphasizes the creation steps. Furthermore, when the user is installing a sensor, the interface unit can also provide display content that emphasizes the installation steps. For example, when the user is analyzing data, the interface unit provides display content that emphasizes the analysis results. When the user is creating an app, the interface unit provides display content that emphasizes the creation steps. When the user is installing a sensor, the interface unit provides display content that emphasizes the installation steps. In this way, by customizing the display content according to the user's current task, efficient work is possible.

[0115] The interface unit can improve the display method by reflecting user feedback. For example, the interface unit improves the UI of the interface based on user feedback. The interface unit can also add display content based on user feedback. The interface unit can also improve display performance based on user feedback. For example, the interface unit improves the UI of the interface based on user feedback. Adds display content based on user feedback. Improves display performance based on user feedback. In this way, the display method can be continuously improved by reflecting user feedback.

[0116] The interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, the interface unit simplifies the operation procedures when the user is stressed. The interface unit can also provide detailed operation procedures when the user is relaxed. The interface unit can also provide procedures that allow for quick operation when the user is in a hurry. For example, the interface unit simplifies the operation procedures when the user is stressed. It provides detailed operation procedures when the user is relaxed. It provides procedures that allow for quick operation when the user is in a hurry. This allows for adjustment of the interface operation procedures according to the user's emotions, enabling operation that is suitable for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] The interface unit can select the optimal display method by taking into account the user's device information. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, when the user is using a tablet, the interface unit can also provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the interface unit can also provide a display method that is simple and highly visible. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. When the user is using a tablet, the interface unit provides a display method that is optimized for a large screen. When the user is using a smartwatch, the interface unit provides a display method that is simple and highly visible. This makes it possible to select the optimal display method by taking into account the user's device information.

[0118] The interface unit can make the display content multilingual in accordance with the user's language setting. The interface unit, for example, automatically sets the display content based on the language setting of the user's device. The interface unit can also provide a language switching function when the user uses multiple languages. The interface unit can also provide the display content in a specific language when the user selects that language. For example, the interface unit automatically sets the display content based on the language setting of the user's device. The interface unit provides a language switching function when the user uses multiple languages. When the user selects a specific language, the display content is provided in that language. This makes it possible to make the display content multilingual in accordance with the user's language setting, thereby providing a display that is suitable for the user.

[0119] The interface unit can customize the display method according to the visual and auditory characteristics of the user. For example, if the user is visually impaired, the interface unit provides large characters and audio guidance. Furthermore, if the user is hearing impaired, the interface unit can also emphasize the visual display method. Furthermore, the interface unit can adjust the color contrast if the user is color blind. For example, if the user is visually impaired, the interface unit provides large characters and audio guidance. If the user is hearing impaired, the interface unit emphasizes the visual display method. If the user is color blind, the interface unit adjusts the color contrast. In this way, the display method can be customized according to the visual and auditory characteristics of the user, thereby enabling a display that is suitable for the user. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned receiving unit, analyzing unit, creating unit, sensor unit, and interface unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receiving unit can receive data from an IoT sensor via the communication I / F 44 of the smart device 14. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The creating unit is realized by the specific processing unit 290 of the data processing device 12 and creates an app based on the analyzed data. The sensor unit is realized by the control unit 46A of the smart device 14 and standardizes the type and installation method of sensors. The interface unit is realized by the control unit 46A of the smart device 14 and provides an interface that the user can intuitively operate. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, creating unit, sensor unit, and interface unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit can receive data from an IoT sensor via the communication I / F 44 of the smart glasses 214. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The creating unit is realized by the specific processing unit 290 of the data processing device 12 and creates an app based on the analyzed data. The sensor unit is realized by the control unit 46A of the smart glasses 214 and standardizes the type and installation method of the sensor. The interface unit is realized by the control unit 46A of the smart glasses 214 and provides an interface that the user can intuitively operate. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, creating unit, sensor unit, and interface unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the receiving unit can receive data from an IoT sensor via the communication I / F 44 of the headset type terminal 314. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The creating unit is realized by the specific processing unit 290 of the data processing device 12 and creates an app based on the analyzed data. The sensor unit is realized by the control unit 46A of the headset type terminal 314 and standardizes the type and installation method of sensors. The interface unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface that can be intuitively operated by the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, creating unit, sensor unit, and interface unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the receiving unit can receive data from an IoT sensor via the communication I / F 44 of the robot 414. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The creating unit is realized by the specific processing unit 290 of the data processing device 12 and creates an app based on the analyzed data. The sensor unit is realized by the control unit 46A of the robot 414 and standardizes the type and installation method of sensors. The interface unit is realized by the control unit 46A of the robot 414 and provides an interface that can be intuitively operated by the user.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can detect abnormal values ​​in sensor data and generate alerts when an abnormality occurs. For example, an alert is generated when temperature sensor data exceeds a set range. An alert can also be generated when humidity sensor data changes suddenly. Furthermore, an alert can also be generated when a motion sensor detects abnormal movement. This allows for immediate response when an abnormal value in sensor data is detected.

[0122] The receiver can monitor the remaining battery level of the sensor and adjust the frequency of data reception as necessary. For example, if the remaining battery level of the sensor is low, the frequency of data reception can be reduced to extend battery life. Also, if the remaining battery level of the sensor is sufficient, the frequency of data reception can be increased to collect more detailed data. Furthermore, if the remaining battery level of the sensor is medium, the frequency of data reception can be adjusted appropriately. In this way, by adjusting the frequency of data reception according to the remaining battery level of the sensor, battery life can be extended.

[0123] The analysis unit can refer to the sensor data history and analyze long-term trends. For example, it can refer to past temperature data to analyze seasonal temperature changes. It can also refer to past humidity data to analyze annual humidity fluctuations. It can also refer to past operation data to analyze operation patterns during specific time periods. This makes it possible to understand long-term trends by referring to the sensor data history.

[0124] The analysis unit can analyze correlations between sensor data and integrate and analyze data from multiple sensors. For example, data from a temperature sensor and a humidity sensor can be integrated to analyze environmental changes. Data from a motion sensor and an environmental sensor can also be integrated to detect abnormal behavior. Furthermore, data from multiple temperature sensors can be integrated to analyze temperature changes over a wide range. This allows for more accurate analysis by integrating and analyzing data from multiple sensors.

[0125] The analysis unit can integrate sensor data with other data sources to perform more accurate analysis. For example, sensor data can be integrated with weather data to analyze environmental changes. Sensor data can also be integrated with traffic data to analyze traffic conditions. Furthermore, sensor data can be integrated with energy data to analyze energy consumption. In this way, integration with other data sources enables more accurate analysis.

[0126] The receiving unit can estimate the user's emotions and adjust the timing of receiving sensor data based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of receiving sensor data can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of receiving sensor data can be increased to collect more detailed data. Furthermore, if the user is in a hurry, only important sensor data can be received with priority. In this way, the timing of receiving sensor data can be adjusted according to the user's emotions, thereby reducing the burden on the user.

[0127] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is feeling stressed, the level of analysis detail can be reduced to provide concise results. Alternatively, if the user is relaxed, the level of analysis detail can be increased to provide detailed results. Furthermore, if the user is in a hurry, only important data can be analyzed to provide quick results. In this way, by adjusting the level of analysis detail according to the user's emotions, analysis results suited to the user can be provided.

[0128] The creation unit can estimate the user's emotions and adjust the app creation method based on the estimated user emotions. For example, if the user is feeling stressed, a simple app creation method can be provided. Alternatively, if the user is relaxed, a detailed customizable app creation method can be provided. Furthermore, if the user is in a hurry, a method that allows for quick app creation can be provided. In this way, by adjusting the app creation method according to the user's emotions, it is possible to create an app that is suitable for the user.

[0129] The sensor unit can estimate the user's emotions and suggest sensor installation locations based on the estimated user emotions. For example, if the user is feeling stressed, the sensor can be installed in an easily accessible location. If the user is relaxed, the sensor can be installed in a location that allows optimal data to be collected. Furthermore, if the user is in a hurry, the sensor can be installed in a location that allows quick installation. This allows the sensor installation location to be suggested according to the user's emotions, enabling optimal installation.

[0130] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the interface display method according to the user's emotions, it is possible to provide a display that is suitable for the user.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The receiver receives sensor data. The sensor data includes temperature data, humidity data, location data, etc. The receiver can, for example, receive data from a temperature sensor in real time, and also receive data from a humidity sensor and location sensor in real time. The receiver receives data from a temperature sensor at regular intervals, data from a humidity sensor in an event-driven manner, and data from a location sensor periodically. Step 2: The analysis unit analyzes the data received by the receiving unit. The analysis is performed by methods such as data filtering, statistical analysis, and the application of machine learning algorithms. For example, the analysis unit may use filtering techniques to remove noise, statistical analysis to identify data trends, and machine learning algorithms to analyze data patterns. Step 3: The creation unit creates an app based on the data analyzed by the analysis unit. The app is created according to criteria such as the type of app, the platform used, and the creation procedure. For example, the creation unit creates a temperature control app based on the temperature data, a humidity control app based on the humidity data, and a location tracking app based on the location data.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a receiving unit that receives sensor data; an analysis unit that analyzes the data received by the receiving unit; a creation unit that creates an application based on the data analyzed by the analysis unit. A system characterized by:

2. Equipped with a sensor unit with standardized sensor types and installation methods 2. The system of claim 1.

3. An interface unit is provided that provides an interface that can be easily operated by the user.

2. The system of claim 1.

4. The receiving unit Receive sensor data in real time 2. The system of claim 1.

5. The analysis unit Analyzing received data in real time 2. The system of claim 1.

6. The creation unit Create an app based on the analysis results 2. The system of claim 1.

7. The receiving unit The system estimates the user's emotions and adjusts the timing of receiving sensor data based on the estimated user emotions.

2. The system of claim 1.

8. The receiving unit Automatically switches data reception method depending on the type of sensor 2. The system of claim 1.

9. The receiving unit Adjust data coverage based on sensor location 2. The system of claim 1.

10. The receiving unit Monitors the sensor's battery level and adjusts the frequency of data reception as needed 2. The system of claim 1.

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