System

Abstract

An object of a system according to an embodiment is to collect a state of a smartphone and notify a user of the state at an appropriate timing.SOLUTION: A system according to an embodiment includes a state collection unit, an information storage unit, an analysis unit, and a notification unit. The state collection unit collects a state of the smartphone. The information accumulation unit accumulates the information collected by the state collection unit in the server. The analysis unit analyzes the information accumulated by the information accumulation unit. The notification unit notifies the user of a result 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] Conventional technologies do not adequately collect smartphone status information in real time and notify users at the appropriate time, so there is room for improvement.

[0005] The system according to the embodiment aims to collect the status of the smartphone and notify the user at an appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a status collection unit, an information storage unit, an analysis unit, and a notification unit. The status collection unit collects the status of the smartphone. The information storage unit stores the information collected by the status collection unit in a server. The analysis unit analyzes the information stored by the information storage unit. The notification unit notifies the user of the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect the status of the smartphone and notify the user at an appropriate time. [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) The smartphone status collection system according to an embodiment of the present invention collects smartphone status, analyzes it using a generation AI, and proposes optimal services and timing for upgrading the model to the user. This allows the smartphone status collection system to grasp the usage status of the user's smartphone and proposes upgrading the model or using services at the optimal timing.

[0029] A smartphone status collection system according to an embodiment includes a status collection unit, an information storage unit, an analysis unit, and a notification unit. The status collection unit collects smartphone status information, such as remaining battery power, network connection status, and app usage status. The status collection unit can also collect health data, such as a user's walking pattern and heart rate. The information storage unit stores the information collected by the status collection unit in a server. For example, the information is stored while taking into consideration the data format, storage period, security measures, and the like. The analysis unit analyzes the information stored by the information storage unit. For example, the analysis is performed while taking into consideration the algorithm to be used, the purpose of the analysis, and how the analysis results will be used. The notification unit notifies the user of the results of the analysis by the analysis unit. For example, the notification is performed while taking into consideration the timing of the notification, the notification method (push notification, email, etc.), the content of the notification, and the like. This allows the smartphone status collection system to improve user convenience.

[0030] The status collection unit can collect health data such as the user's walking pattern or heart rate. For example, the status collection unit allows an app to collect the user's walking pattern and analyze the amount of daily exercise and activity level. For example, the collected data includes the number of steps, distance traveled, and frequency of exercise, which can be used to understand the user's health. The status collection unit also allows the app to monitor the user's heart rate in real time and analyze the user's stress level and cardiac health. For example, the status collection unit records heart rate fluctuations and sends an alert if an abnormality is detected. The status collection unit also allows the app to collect the user's sleep pattern and analyze the quality and duration of sleep. For example, the status collection unit monitors movements and heart rate during sleep and records the depth of sleep and frequency of interruptions. By collecting the user's health data, a more comprehensive understanding of usage can be achieved.

[0031] The status collection unit collects the user's location information and can suggest optimal settings or services according to the usage environment. For example, the status collection unit allows an app to collect the user's location information and analyze usage patterns in specific locations. For example, the status collection unit grasps smartphone usage at work and at home and suggests optimal settings. The status collection unit also allows an app to analyze the user's travel route and understand usage patterns during commuting and traveling. For example, the status collection unit suggests optimal music or news to listen to during commuting time. The status collection unit also allows an app to suggest nearby stores and services based on the user's location information. For example, when a user is in a specific area, the status collection unit provides information on nearby restaurants and cafes. This makes it possible to suggest optimal settings and services based on the user's location information.

[0032] The status collection unit is also pre-installed on other devices, allowing for integrated collection of usage status across multiple devices. For example, the status collection unit pre-installs an app on a tablet and collects integrated usage status of a smartphone and tablet. For example, it compares the app usage time and battery consumption of both devices. The status collection unit also pre-installs an app on a smartwatch and integrates data from the smartphone and smartwatch. For example, it links heart rate data from the smartwatch with smartphone usage status. The status collection unit also pre-installs the app on multiple devices and synchronizes data between devices in real time. For example, it ensures that operations on a smartphone are reflected on the tablet and smartwatch. This allows for integrated collection of usage status across multiple devices.

[0033] The status collection unit works in conjunction with smart home appliances in the home to collect comprehensive data, including the usage status of the appliances. For example, an app in the status collection unit works in conjunction with smart home appliances to collect the usage status of the appliances. For example, it monitors the operation status of refrigerators and washing machines and integrates this with smartphone usage data. The status collection unit also analyzes the data from smart home appliances through an app to understand energy consumption patterns within the home. For example, it analyzes the usage status of air conditioners and lighting and makes suggestions to improve energy efficiency. The status collection unit also analyzes lifestyle patterns within the home based on the data from smart home appliances through an app. For example, it understands the amount of time spent watching TV and how often the oven is used and suggests optimal appliance settings. This makes it possible to work in conjunction with smart home appliances in the home to collect comprehensive data.

[0034] The analysis unit can perform a more detailed lifestyle analysis, including the user's social media activity data. In the analysis unit, for example, the generation AI analyzes the content of the user's social media posts to understand lifestyle trends. For example, it identifies hobbies and interests from the frequency and content of posts. In addition, the analysis unit uses the generation AI to analyze the user's social media friendships and evaluate social connections. For example, it evaluates the user's sociability based on the number of friends and frequency of interactions. In addition, the analysis unit uses the generation AI to analyze the user's social media activity time and understand the daily rhythm of their life. For example, it identifies the user's activity patterns from the time of day and frequency of posts. This makes it possible to perform a more detailed lifestyle analysis by including the user's social media activity data.

[0035] The analysis unit can make service suggestions based on consumer behavior, including the user's purchasing history. In the analysis unit, for example, the generation AI analyzes the user's online shopping history to understand consumer behavior trends. For example, the user's consumption patterns are identified based on purchase frequency and categories of purchased items. The analysis unit also uses the generation AI to suggest optimal services and products based on the user's purchasing history. For example, related products and services are recommended based on past purchase history. The analysis unit also uses the generation AI to analyze the user's purchasing history to understand changes in consumer behavior. For example, purchasing patterns during specific seasons or events are identified and suggestions are made at appropriate times. This makes it possible to make service suggestions based on consumer behavior by including the user's purchasing history.

[0036] The analysis unit can compare data from different regions or cultural spheres to identify region-specific usage patterns. For example, the generation AI analyzes user data from different regions to identify region-specific usage patterns. For example, it compares app usage time and battery consumption patterns in specific regions. The analysis unit also analyzes data from different cultural spheres to identify culture-specific usage patterns. For example, it understands smartphone usage methods and preferences in specific cultural spheres. The analysis unit also allows the generation AI to propose region-specific services based on the data for each region. For example, it recommends apps and services that are popular in a specific region. This makes it possible to identify region-specific usage patterns by comparing data from different regions or cultural spheres.

[0037] The analysis unit can analyze the frequency of use of the user's smartphone or app usage patterns and suggest the optimal timing for changing models. For example, the generation AI in the analysis unit analyzes the frequency of use of apps by the user and suggests the optimal timing for changing models. For example, if a specific app is used frequently, a higher performance model is suggested. The analysis unit also analyzes the usage pattern of the user's smartphone and suggests the optimal timing for changing models. For example, if the battery drains quickly, a model with a long battery life is suggested. The analysis unit also suggests the optimal timing for changing models based on the frequency of use of the user's smartphone. For example, if the usage frequency is high, the latest model is suggested. In this way, by analyzing the frequency of use of the user's smartphone and app usage patterns, the optimal timing for changing models can be suggested.

[0038] The analysis unit can analyze the deterioration status of the user's smartphone hardware and suggest the timing for changing models based on the lifespan of the hardware. In the analysis unit, for example, the generation AI analyzes the deterioration status of the smartphone's battery and suggests the optimal timing for changing models. For example, if the battery is deteriorating, the generation AI suggests a model with a long battery life. In addition, the analysis unit can analyze the smartphone's storage usage status and suggest the optimal timing for changing models. For example, if there is little free storage space, the generation AI suggests a model with a large storage capacity. In addition, the analysis unit can analyze the deterioration status of the smartphone's overall hardware and suggest the optimal timing for changing models. For example, if the hardware is deteriorating, the generation AI suggests the latest model. In this way, by analyzing the deterioration status of the user's smartphone hardware, it is possible to suggest the timing for changing models based on the hardware lifespan.

[0039] The analysis unit can compare the user's smartphone usage with other users and suggest the timing for changing models based on common usage patterns. The analysis unit, for example, uses a generation AI to analyze usage data from multiple users and suggest the timing for changing models based on common usage patterns. For example, the analysis unit suggests the optimal timing for changing models for users with the same usage patterns. Furthermore, the analysis unit uses the generation AI to identify common usage patterns based on data from other users and suggest the timing for changing models. For example, the analysis unit suggests a model change for a user whose battery is deteriorating. Furthermore, the analysis unit uses the generation AI to compare data between users and suggest the timing for changing models based on common usage patterns. For example, the analysis unit suggests a model change for a user with low free storage space. In this way, by comparing the user's smartphone usage with other users, the analysis unit can suggest the timing for changing models based on common usage patterns.

[0040] The analysis unit can compare the user's smartphone usage with different devices and suggest the optimal timing for changing models between devices. In the analysis unit, for example, the generation AI compares the usage of a smartphone and a tablet and suggests the optimal timing for changing models. For example, if the tablet is used frequently, it suggests changing the smartphone model. In addition, the analysis unit can compare the usage of a smartphone and a smartwatch and suggest the optimal timing for changing models. For example, it suggests changing the smartphone model based on the data from the smartwatch. In addition, the analysis unit can analyze the usage data between different devices and suggest the optimal timing for changing models. For example, it compares the battery consumption patterns of a smartphone and a tablet and suggests a model change. In this way, by comparing the user's smartphone usage with different devices, it is possible to suggest the optimal timing for changing models between devices.

[0041] The analysis unit can analyze the user's app usage patterns and suggest the optimal subscription service. In the analysis unit, for example, the generation AI analyzes the user's app usage patterns and suggests the optimal subscription service. For example, if a music app is used frequently, a music streaming service is suggested. In addition, the analysis unit uses the generation AI to suggest the optimal subscription service based on the user's app usage data. For example, if a video viewing app is used frequently, a video streaming service is suggested. In addition, the analysis unit uses the generation AI to analyze the user's app usage patterns and suggest the optimal subscription service. For example, if a news app is used frequently, a news subscription service is suggested. In this way, the optimal subscription service can be suggested by analyzing the user's app usage patterns.

[0042] The analysis unit can analyze the user's smartphone usage and propose the optimal data plan or communication plan. In the analysis unit, for example, the generation AI analyzes the user's data usage and proposes the optimal data plan. For example, if data usage is high, an unlimited data plan is proposed. In addition, the analysis unit can analyze the user's communication patterns and propose the optimal communication plan. For example, if call time is long, an unlimited call plan is proposed. In addition, the analysis unit can propose the optimal data plan based on the user's data usage. For example, if data usage is low, a low-cost data plan is proposed. In this way, by analyzing the user's smartphone usage, the optimal data plan or communication plan can be proposed.

[0043] The analysis unit can compare the user's smartphone usage with different devices and suggest the optimal service between devices. In the analysis unit, for example, the generation AI compares smartphone and tablet usage and suggests the optimal service. For example, if the tablet is used frequently, a service for the tablet is suggested. In addition, the analysis unit can compare the smartphone and smartwatch usage and suggest the optimal service. For example, a service for the smartphone is suggested based on smartwatch data. In addition, the analysis unit can analyze the usage data between different devices and suggest the optimal service. For example, the generation AI can compare the usage patterns of the smartphone and tablet and suggest the optimal service between devices. In this way, by comparing the user's smartphone usage with different devices, the optimal service between devices can be suggested.

[0044] The analysis unit can analyze the user's app usage patterns and suggest the optimal smartphone model. In the analysis unit, for example, the generation AI analyzes the user's app usage patterns and suggests the optimal smartphone model. For example, if a game app is used frequently, a model with high gaming performance is suggested. In addition, the analysis unit uses the generation AI to suggest the optimal smartphone model based on the user's app usage data. For example, if a photo-taking app is used frequently, a model with excellent camera performance is suggested. In addition, the analysis unit uses the generation AI to analyze the user's app usage patterns and suggest the optimal smartphone model. For example, if a business app is used frequently, a model with extensive business features is suggested. In this way, the optimal smartphone model can be suggested by analyzing the user's app usage patterns.

[0045] The analysis unit can analyze the user's smartphone usage and suggest the optimal smartphone model. For example, the generation AI in the analysis unit analyzes the user's smartphone usage and suggests the optimal smartphone model. For example, if the battery drains quickly, the generation AI suggests a model with a long battery life. The analysis unit also suggests the optimal smartphone model based on the user's smartphone usage data. For example, if there is little free storage space, the generation AI suggests a model with a large storage capacity. The analysis unit also suggests the user's smartphone usage and suggests the optimal smartphone model. For example, if the screen is used for a long time, the generation AI suggests a model with a display that is easy on the eyes. In this way, the optimal smartphone model can be suggested by analyzing the user's smartphone usage.

[0046] The analysis unit can compare the user's smartphone usage with other users and suggest a smartphone model based on the common usage pattern. For example, the analysis unit uses a generation AI to analyze usage data from multiple users and suggest a smartphone model based on the common usage pattern. For example, the analysis unit suggests the optimal model for users with the same usage pattern. Furthermore, the analysis unit uses the generation AI to identify common usage patterns based on the data of other users and suggest a smartphone model. For example, the analysis unit suggests a model with a long battery life for a user whose battery is deteriorating. Furthermore, the analysis unit uses the generation AI to compare data between users and suggest a smartphone model based on the common usage pattern. For example, the analysis unit suggests a model with a large storage capacity for a user with little free storage space. This allows the analysis unit to suggest a smartphone model based on the common usage pattern by comparing the user's smartphone usage with other users.

[0047] The analysis unit can compare the user's smartphone usage with different devices and suggest the optimal smartphone model between the devices. In the analysis unit, for example, the generation AI compares the usage of the smartphone and tablet and suggests the optimal smartphone model. For example, if the tablet is used frequently, a model that is easy to link with the tablet is suggested. In addition, the analysis unit can compare the usage of the smartphone and smartwatch and suggest the optimal smartphone model. For example, a smartphone model is suggested based on smartwatch data. In addition, the analysis unit can analyze the usage data between different devices and suggest the optimal smartphone model. For example, the generation AI can compare the usage patterns of the smartphone and tablet and suggest the optimal model between the devices. In this way, by comparing the user's smartphone usage with different devices, the optimal smartphone model between the devices can be suggested.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The status collection unit not only collects information about the user's smartphone usage, but also collects environmental data about the user's surroundings. For example, it can collect environmental data such as ambient volume, light intensity, and temperature, and suggest optimal settings based on the user's usage environment. The status collection unit can also collect performance data such as the user's smartphone's operating speed and app crash frequency, and suggest optimal maintenance and update timing. Furthermore, the status collection unit can monitor the security status of the user's smartphone and send an alert when a virus or malware is detected. This allows for a comprehensive understanding of the user's smartphone usage and the provision of optimal services.

[0050] The status collection unit not only collects the user's health data, but also collects the user's diet and nutritional intake status. For example, if the user uses an app to record their meals, that data can be collected and used to analyze nutritional balance. The status collection unit can also monitor the user's water intake and suggest appropriate times to hydrate. Furthermore, the status collection unit can monitor the user's stress level and suggest relaxation methods and activities for stress relief. This allows the user's health data to be collected more comprehensively and used to help manage their health.

[0051] The status collection unit can not only collect the user's location information, but also collect the user's means of transportation and traffic conditions. For example, if the user is driving a car, the status collection unit can collect driving patterns and traffic congestion information and suggest the optimal route. If the user is using public transportation, the status collection unit can collect operation status and delay information and provide optimal transfer guidance. Furthermore, the status collection unit can monitor the user's safety status during the user's trip and send an alert if a dangerous situation is detected. This allows for comprehensive collection of information about the user's trip and the provision of optimal services.

[0052] The status collection unit is also pre-installed on other devices, allowing for integrated collection of usage status across multiple devices. For example, an app can be pre-installed on a tablet, and usage status of a smartphone and tablet can be collected in an integrated manner. For example, the app usage time and battery consumption of both devices can be compared. The status collection unit can also pre-install an app on a smartwatch, integrating data from the smartphone and smartwatch. For example, it can link heart rate data from the smartwatch with smartphone usage status. The status collection unit can also pre-install an app on multiple devices, synchronizing data between devices in real time. For example, it can ensure that operations on a smartphone are reflected on tablets and smartwatches. This allows for integrated collection of usage status across multiple devices.

[0053] The status collection unit works in conjunction with smart home appliances in the home to collect comprehensive data, including the usage status of the appliances. For example, an app works in conjunction with smart home appliances to collect the usage status of the appliances. For example, it monitors the operation status of refrigerators and washing machines and integrates this with smartphone usage data. The status collection unit also analyzes the data from the smart home appliances through the app to understand the energy consumption patterns within the home. For example, it analyzes the usage status of air conditioners and lighting and makes suggestions to improve energy efficiency. The status collection unit also analyzes the lifestyle patterns within the home based on the data from the smart home appliances through the app. For example, it understands the amount of time spent watching TV and how often the oven is used and suggests optimal appliance settings. This makes it possible to work in conjunction with smart home appliances in the home to collect comprehensive data.

[0054] The analysis unit can perform a more detailed lifestyle analysis, including the user's social media activity data. For example, the generation AI analyzes the content of the user's social media posts to understand lifestyle trends. For example, it identifies hobbies and interests from the frequency and content of posts. The analysis unit also analyzes the user's social media friendships to evaluate social connections. For example, it evaluates the user's sociability based on the number of friends and frequency of interactions. The analysis unit also analyzes the user's social media activity time to understand their daily rhythm. For example, it identifies the user's activity patterns from the time of day and frequency of posts. This makes it possible to perform a more detailed lifestyle analysis by including the user's social media activity data.

[0055] The analysis unit can make service suggestions based on consumer behavior, including the user's purchasing history. For example, the generation AI analyzes the user's online shopping history to understand consumer behavior trends. For example, the user's consumption patterns are identified based on purchase frequency and categories of purchased items. The analysis unit also uses the generation AI to suggest optimal services and products based on the user's purchasing history. For example, related products and services are recommended based on past purchase history. The analysis unit also uses the generation AI to analyze the user's purchasing history to understand changes in consumer behavior. For example, purchasing patterns during specific seasons or events are identified, and suggestions are made at appropriate times. This makes it possible to make service suggestions based on consumer behavior by including the user's purchasing history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The status collection unit collects the smartphone's status, such as remaining battery power, network connection status, and app usage status. It can also collect health data such as the user's walking pattern and heart rate. Step 2: The information storage unit stores the information collected by the status collection unit in the server. For example, the information is stored taking into consideration the data format, storage period, security measures, etc. Step 3: The analysis unit analyzes the information stored by the information storage unit, taking into consideration, for example, the algorithm to be used, the purpose of the analysis, and how the analysis results will be used. Step 4: The notification unit notifies the user of the results of the analysis performed by the analysis unit, taking into consideration the timing of the notification, the means of notification (push notification, email, etc.), and the content of the notification, for example.

[0058] (Example 2) The smartphone status collection system according to an embodiment of the present invention collects smartphone status, analyzes it using a generation AI, and proposes optimal services and timing for upgrading the model to the user. This allows the smartphone status collection system to grasp the usage status of the user's smartphone and proposes upgrading the model or using services at the optimal timing.

[0059] A smartphone status collection system according to an embodiment includes a status collection unit, an information storage unit, an analysis unit, and a notification unit. The status collection unit collects smartphone status information, such as remaining battery power, network connection status, and app usage status. The status collection unit can also collect health data, such as a user's walking pattern and heart rate. The information storage unit stores the information collected by the status collection unit in a server. For example, the information is stored while taking into consideration the data format, storage period, security measures, and the like. The analysis unit analyzes the information stored by the information storage unit. For example, the analysis is performed while taking into consideration the algorithm to be used, the purpose of the analysis, and how the analysis results will be used. The notification unit notifies the user of the results of the analysis by the analysis unit. For example, the notification is performed while taking into consideration the timing of the notification, the notification method (push notification, email, etc.), the content of the notification, and the like. This allows the smartphone status collection system to improve user convenience.

[0060] The status collection unit can collect health data such as the user's walking pattern or heart rate. For example, the status collection unit allows an app to collect the user's walking pattern and analyze the amount of daily exercise and activity level. For example, the collected data includes the number of steps, distance traveled, and frequency of exercise, which can be used to understand the user's health. The status collection unit also allows the app to monitor the user's heart rate in real time and analyze the user's stress level and cardiac health. For example, the status collection unit records heart rate fluctuations and sends an alert if an abnormality is detected. The status collection unit also allows the app to collect the user's sleep pattern and analyze the quality and duration of sleep. For example, the status collection unit monitors movements and heart rate during sleep and records the depth of sleep and frequency of interruptions. By collecting the user's health data, a more comprehensive understanding of usage can be achieved.

[0061] The status collection unit collects the user's location information and can suggest optimal settings or services according to the usage environment. For example, the status collection unit allows an app to collect the user's location information and analyze usage patterns in specific locations. For example, the status collection unit grasps smartphone usage at work and at home and suggests optimal settings. The status collection unit also allows an app to analyze the user's travel route and understand usage patterns during commuting and traveling. For example, the status collection unit suggests optimal music or news to listen to during commuting time. The status collection unit also allows an app to suggest nearby stores and services based on the user's location information. For example, when a user is in a specific area, the status collection unit provides information on nearby restaurants and cafes. This makes it possible to suggest optimal settings and services based on the user's location information.

[0062] The state collection unit uses the emotion estimation function to collect emotions while the user is using the smartphone and can analyze the usage status based on the emotions. For example, the state collection unit uses an app to analyze the user's facial expressions and estimate the emotion while using the smartphone. For example, the state collection unit uses a camera to monitor the user's facial expressions in real time and calculate an emotion score. The state collection unit also uses an app to analyze the user's voice tone and estimate the emotional state. For example, the state collection unit uses a microphone to analyze the tone and pitch of the user's voice and detect changes in emotion. The state collection unit also uses an app to analyze the user's touch pattern and estimate the emotional state. For example, the state collection unit monitors the strength and speed of the touch and analyzes the degree of stress or excitement. This makes it possible to analyze the usage status based on the user's emotions.

[0063] The status collection unit is also pre-installed on other devices, allowing for integrated collection of usage status across multiple devices. For example, the status collection unit pre-installs an app on a tablet and collects integrated usage status of a smartphone and tablet. For example, it compares the app usage time and battery consumption of both devices. The status collection unit also pre-installs an app on a smartwatch and integrates data from the smartphone and smartwatch. For example, it links heart rate data from the smartwatch with smartphone usage status. The status collection unit also pre-installs the app on multiple devices and synchronizes data between devices in real time. For example, it ensures that operations on a smartphone are reflected on the tablet and smartwatch. This allows for integrated collection of usage status across multiple devices.

[0064] The status collection unit works in conjunction with smart home appliances in the home to collect comprehensive data, including the usage status of the appliances. For example, an app in the status collection unit works in conjunction with smart home appliances to collect the usage status of the appliances. For example, it monitors the operation status of refrigerators and washing machines and integrates this with smartphone usage data. The status collection unit also analyzes the data from smart home appliances through an app to understand energy consumption patterns within the home. For example, it analyzes the usage status of air conditioners and lighting and makes suggestions to improve energy efficiency. The status collection unit also analyzes lifestyle patterns within the home based on the data from smart home appliances through an app. For example, it understands the amount of time spent watching TV and how often the oven is used and suggests optimal appliance settings. This makes it possible to work in conjunction with smart home appliances in the home to collect comprehensive data.

[0065] The state collection unit is equipped with an emotion estimation function and can collect emotions in real time while the user is performing a specific activity and suggest the most appropriate service. For example, the state collection unit allows an app to collect the user's emotions while exercising in real time and suggest the most appropriate fitness plan. For example, the state collection unit analyzes the heart rate and facial expressions during exercise and adjusts the training intensity. The state collection unit also allows an app to collect the user's emotions while relaxing and suggest the most appropriate relaxation service. For example, the state collection unit analyzes breathing patterns and facial expressions during meditation and evaluates the level of relaxation. The state collection unit also allows an app to collect the user's emotions while working and suggest the most appropriate productivity improvement service. For example, if concentration is declining, the state collection unit sends a notification suggesting a break. This allows the app to collect the user's emotions in real time and suggest the most appropriate service.

[0066] The analysis unit can perform a more detailed lifestyle analysis, including the user's social media activity data. In the analysis unit, for example, the generation AI analyzes the content of the user's social media posts to understand lifestyle trends. For example, it identifies hobbies and interests from the frequency and content of posts. In addition, the analysis unit uses the generation AI to analyze the user's social media friendships and evaluate social connections. For example, it evaluates the user's sociability based on the number of friends and frequency of interactions. In addition, the analysis unit uses the generation AI to analyze the user's social media activity time and understand the daily rhythm of their life. For example, it identifies the user's activity patterns from the time of day and frequency of posts. This makes it possible to perform a more detailed lifestyle analysis by including the user's social media activity data.

[0067] The analysis unit can make service suggestions based on consumer behavior, including the user's purchasing history. In the analysis unit, for example, the generation AI analyzes the user's online shopping history to understand consumer behavior trends. For example, the user's consumption patterns are identified based on purchase frequency and categories of purchased items. The analysis unit also uses the generation AI to suggest optimal services and products based on the user's purchasing history. For example, related products and services are recommended based on past purchase history. The analysis unit also uses the generation AI to analyze the user's purchasing history to understand changes in consumer behavior. For example, purchasing patterns during specific seasons or events are identified and suggestions are made at appropriate times. This makes it possible to make service suggestions based on consumer behavior by including the user's purchasing history.

[0068] The analysis unit uses the emotion estimation function to analyze the user's emotional patterns and make lifestyle suggestions based on their emotions. In the analysis unit, for example, the generation AI analyzes the user's emotional data and identifies their emotional patterns. For example, it records daily emotional fluctuations and identifies trends in stress and happiness. The analysis unit also uses the generation AI to suggest lifestyle improvements based on the user's emotional patterns. For example, it suggests relaxation services during times of high stress. The analysis unit also uses the generation AI to analyze the user's emotional data and suggest personalized services based on their emotions. For example, it recommends activities and events during times when positive emotions are strong. This makes it possible to analyze the user's emotional patterns and make lifestyle suggestions based on their emotions.

[0069] The analysis unit can compare data from different regions or cultural spheres to identify region-specific usage patterns. For example, the generation AI analyzes user data from different regions to identify region-specific usage patterns. For example, it compares app usage time and battery consumption patterns in specific regions. The analysis unit also analyzes data from different cultural spheres to identify culture-specific usage patterns. For example, it understands smartphone usage methods and preferences in specific cultural spheres. The analysis unit also allows the generation AI to propose region-specific services based on the data for each region. For example, it recommends apps and services that are popular in a specific region. This makes it possible to identify region-specific usage patterns by comparing data from different regions or cultural spheres.

[0070] The analysis unit uses the emotion estimation function to compare the user's emotion data with other users and suggest services that are likely to resonate emotionally. For example, the analysis unit has the generation AI compare the user's emotion data with other users and identify services that are likely to resonate. For example, it prioritizes suggesting services with a high level of positive emotion. The analysis unit also has the generation AI evaluate the degree of empathy between users based on the emotion data. For example, it suggests services that are likely to resonate with users with the same emotion pattern. The analysis unit also has the generation AI analyze the emotion data and suggest services that are likely to resonate emotionally. For example, it prioritizes recommending services with a high emotion score. In this way, by comparing the user's emotion data with other users, it is possible to suggest services that are likely to resonate emotionally.

[0071] The analysis unit can analyze the frequency of use of the user's smartphone or app usage patterns and suggest the optimal timing for changing models. For example, the generation AI in the analysis unit analyzes the frequency of use of apps by the user and suggests the optimal timing for changing models. For example, if a specific app is used frequently, a higher performance model is suggested. The analysis unit also analyzes the usage pattern of the user's smartphone and suggests the optimal timing for changing models. For example, if the battery drains quickly, a model with a long battery life is suggested. The analysis unit also suggests the optimal timing for changing models based on the frequency of use of the user's smartphone. For example, if the usage frequency is high, the latest model is suggested. In this way, by analyzing the frequency of use of the user's smartphone and app usage patterns, the optimal timing for changing models can be suggested.

[0072] The analysis unit can analyze the deterioration status of the user's smartphone hardware and suggest the timing for changing models based on the lifespan of the hardware. In the analysis unit, for example, the generation AI analyzes the deterioration status of the smartphone's battery and suggests the optimal timing for changing models. For example, if the battery is deteriorating, the generation AI suggests a model with a long battery life. In addition, the analysis unit can analyze the smartphone's storage usage status and suggest the optimal timing for changing models. For example, if there is little free storage space, the generation AI suggests a model with a large storage capacity. In addition, the analysis unit can analyze the deterioration status of the smartphone's overall hardware and suggest the optimal timing for changing models. For example, if the hardware is deteriorating, the generation AI suggests the latest model. In this way, by analyzing the deterioration status of the user's smartphone hardware, it is possible to suggest the timing for changing models based on the hardware lifespan.

[0073] The analysis unit uses the emotion estimation function to analyze the user's emotional satisfaction with the smartphone and suggest the emotionally optimal timing for changing models. For example, the analysis unit uses a generation AI to analyze the user's emotional data regarding the smartphone and suggest the emotionally optimal timing for changing models. For example, if satisfaction is low, the analysis unit suggests a model change. The analysis unit also uses a generation AI to analyze the user's emotional patterns regarding the smartphone and suggest the emotionally optimal timing for changing models. For example, if stress is high, the analysis unit also uses a generation AI to suggest the emotionally optimal timing for changing models based on the user's emotional data regarding the smartphone. For example, if positive emotions are low, the analysis unit suggests the emotionally optimal timing for changing models. In this way, by analyzing the user's emotional satisfaction with the smartphone, the analysis unit can suggest the emotionally optimal timing for changing models.

[0074] The analysis unit can compare the user's smartphone usage with other users and suggest the timing for changing models based on common usage patterns. The analysis unit, for example, uses a generation AI to analyze usage data from multiple users and suggest the timing for changing models based on common usage patterns. For example, the analysis unit suggests the optimal timing for changing models for users with the same usage patterns. Furthermore, the analysis unit uses the generation AI to identify common usage patterns based on data from other users and suggest the timing for changing models. For example, the analysis unit suggests a model change for a user whose battery is deteriorating. Furthermore, the analysis unit uses the generation AI to compare data between users and suggest the timing for changing models based on common usage patterns. For example, the analysis unit suggests a model change for a user with low free storage space. In this way, by comparing the user's smartphone usage with other users, the analysis unit can suggest the timing for changing models based on common usage patterns.

[0075] The analysis unit can compare the user's smartphone usage with different devices and suggest the optimal timing for changing models between devices. In the analysis unit, for example, the generation AI compares the usage of a smartphone and a tablet and suggests the optimal timing for changing models. For example, if the tablet is used frequently, it suggests changing the smartphone model. In addition, the analysis unit can compare the usage of a smartphone and a smartwatch and suggest the optimal timing for changing models. For example, it suggests changing the smartphone model based on the data from the smartwatch. In addition, the analysis unit can analyze the usage data between different devices and suggest the optimal timing for changing models. For example, it compares the battery consumption patterns of a smartphone and a tablet and suggests a model change. In this way, by comparing the user's smartphone usage with different devices, it is possible to suggest the optimal timing for changing models between devices.

[0076] The analysis unit can use the emotion estimation function to compare the user's emotion data with other users and suggest the timing for changing models that is likely to be emotionally relatable. For example, the analysis unit has a generation AI compare the user's emotion data with other users and suggest a timing for changing models that is likely to be emotionally relatable. For example, the analysis unit suggests a model change to users who have the same emotion pattern. The analysis unit also has a generation AI evaluate the degree of empathy between users based on the emotion data and suggest a timing for changing models that is likely to be emotionally relatable. For example, the analysis unit suggests a model change when there are few positive emotions. The analysis unit also has a generation AI analyze the emotion data and suggest a timing for changing models that is likely to be emotionally relatable. For example, the analysis unit suggests a model change when stress is high. In this way, by comparing the user's emotion data with other users, it is possible to suggest a timing for changing models that is likely to be emotionally relatable.

[0077] The analysis unit can analyze the user's app usage patterns and suggest the optimal subscription service. In the analysis unit, for example, the generation AI analyzes the user's app usage patterns and suggests the optimal subscription service. For example, if a music app is used frequently, a music streaming service is suggested. In addition, the analysis unit uses the generation AI to suggest the optimal subscription service based on the user's app usage data. For example, if a video viewing app is used frequently, a video streaming service is suggested. In addition, the analysis unit uses the generation AI to analyze the user's app usage patterns and suggest the optimal subscription service. For example, if a news app is used frequently, a news subscription service is suggested. In this way, the optimal subscription service can be suggested by analyzing the user's app usage patterns.

[0078] The analysis unit can analyze the user's smartphone usage and propose the optimal data plan or communication plan. In the analysis unit, for example, the generation AI analyzes the user's data usage and proposes the optimal data plan. For example, if data usage is high, an unlimited data plan is proposed. In addition, the analysis unit can analyze the user's communication patterns and propose the optimal communication plan. For example, if call time is long, an unlimited call plan is proposed. In addition, the analysis unit can propose the optimal data plan based on the user's data usage. For example, if data usage is low, a low-cost data plan is proposed. In this way, by analyzing the user's smartphone usage, the optimal data plan or communication plan can be proposed.

[0079] The analysis unit can use the emotion estimation function to analyze the user's emotional patterns and suggest the services that provide high emotional satisfaction. For example, the generation AI in the analysis unit analyzes the user's emotional data and suggests services that provide high emotional satisfaction. For example, if there are a lot of positive emotions, it suggests a relaxation service. The analysis unit also uses the generation AI to suggest services that provide high emotional satisfaction based on the user's emotional patterns. For example, if stress is high, it suggests a stress relief service. The analysis unit also uses the generation AI to analyze the user's emotional data and suggest services that provide high emotional satisfaction. For example, if happiness is high, it suggests an entertainment service. In this way, by analyzing the user's emotional patterns, it is possible to suggest services that provide high emotional satisfaction.

[0080] The analysis unit can compare the user's smartphone usage with different devices and suggest the optimal service between devices. In the analysis unit, for example, the generation AI compares smartphone and tablet usage and suggests the optimal service. For example, if the tablet is used frequently, a service for the tablet is suggested. In addition, the analysis unit can compare the smartphone and smartwatch usage and suggest the optimal service. For example, a service for the smartphone is suggested based on smartwatch data. In addition, the analysis unit can analyze the usage data between different devices and suggest the optimal service. For example, the generation AI can compare the usage patterns of the smartphone and tablet and suggest the optimal service between devices. In this way, by comparing the user's smartphone usage with different devices, the optimal service between devices can be suggested.

[0081] The analysis unit can use the emotion estimation function to compare the user's emotion data with other users and suggest services that are likely to be emotionally relatable. For example, the analysis unit has the generation AI compare the user's emotion data with other users and suggest services that are likely to be emotionally relatable. For example, it suggests the optimal service for users with the same emotion pattern. The analysis unit also has the generation AI evaluate the degree of empathy between users based on the emotion data and suggest services that are likely to be emotionally relatable. For example, if there are a lot of positive emotions, it suggests a relaxation service. The analysis unit also has the generation AI analyze the emotion data and suggest services that are likely to be emotionally relatable. For example, if stress is high, it suggests a stress relief service. In this way, by comparing the user's emotion data with other users, it is possible to suggest services that are likely to be emotionally relatable.

[0082] The analysis unit can analyze the user's app usage patterns and suggest the optimal smartphone model. In the analysis unit, for example, the generation AI analyzes the user's app usage patterns and suggests the optimal smartphone model. For example, if a game app is used frequently, a model with high gaming performance is suggested. In addition, the analysis unit uses the generation AI to suggest the optimal smartphone model based on the user's app usage data. For example, if a photo-taking app is used frequently, a model with excellent camera performance is suggested. In addition, the analysis unit uses the generation AI to analyze the user's app usage patterns and suggest the optimal smartphone model. For example, if a business app is used frequently, a model with extensive business features is suggested. In this way, the optimal smartphone model can be suggested by analyzing the user's app usage patterns.

[0083] The analysis unit can analyze the user's smartphone usage and suggest the optimal smartphone model. For example, the generation AI in the analysis unit analyzes the user's smartphone usage and suggests the optimal smartphone model. For example, if the battery drains quickly, the generation AI suggests a model with a long battery life. The analysis unit also suggests the optimal smartphone model based on the user's smartphone usage data. For example, if there is little free storage space, the generation AI suggests a model with a large storage capacity. The analysis unit also suggests the user's smartphone usage and suggests the optimal smartphone model. For example, if the screen is used for a long time, the generation AI suggests a model with a display that is easy on the eyes. In this way, the optimal smartphone model can be suggested by analyzing the user's smartphone usage.

[0084] The analysis unit can use the emotion estimation function to analyze the user's emotion patterns and suggest the smartphone model that provides high emotional satisfaction. For example, the analysis unit uses a generation AI to analyze the user's emotion data and suggest a smartphone model that provides high emotional satisfaction. For example, if there are a lot of positive emotions, the analysis unit suggests a model with a wide range of entertainment functions. The analysis unit also uses the generation AI to suggest a smartphone model that provides high emotional satisfaction based on the user's emotion patterns. For example, if stress is high, the analysis unit suggests a model with a wide range of relaxation functions. The analysis unit also uses the generation AI to analyze the user's emotion data and suggest a smartphone model that provides high emotional satisfaction. For example, if happiness is high, the analysis unit suggests a model with excellent camera performance. In this way, by analyzing the user's emotion patterns, it is possible to suggest a smartphone model that provides high emotional satisfaction.

[0085] The analysis unit can compare the user's smartphone usage with other users and suggest a smartphone model based on the common usage pattern. For example, the analysis unit uses a generation AI to analyze usage data from multiple users and suggest a smartphone model based on the common usage pattern. For example, the analysis unit suggests the optimal model for users with the same usage pattern. Furthermore, the analysis unit uses the generation AI to identify common usage patterns based on the data of other users and suggest a smartphone model. For example, the analysis unit suggests a model with a long battery life for a user whose battery is deteriorating. Furthermore, the analysis unit uses the generation AI to compare data between users and suggest a smartphone model based on the common usage pattern. For example, the analysis unit suggests a model with a large storage capacity for a user with little free storage space. This allows the analysis unit to suggest a smartphone model based on the common usage pattern by comparing the user's smartphone usage with other users.

[0086] The analysis unit can compare the user's smartphone usage with different devices and suggest the optimal smartphone model between the devices. In the analysis unit, for example, the generation AI compares the usage of the smartphone and tablet and suggests the optimal smartphone model. For example, if the tablet is used frequently, a model that is easy to link with the tablet is suggested. In addition, the analysis unit can compare the usage of the smartphone and smartwatch and suggest the optimal smartphone model. For example, a smartphone model is suggested based on smartwatch data. In addition, the analysis unit can analyze the usage data between different devices and suggest the optimal smartphone model. For example, the generation AI can compare the usage patterns of the smartphone and tablet and suggest the optimal model between the devices. In this way, by comparing the user's smartphone usage with different devices, the optimal smartphone model between the devices can be suggested.

[0087] The analysis unit can use the emotion estimation function to compare the user's emotion data with other users and suggest a smartphone model that is likely to be emotionally relatable. For example, the analysis unit uses a generation AI to compare the user's emotion data with other users and suggest a smartphone model that is likely to be emotionally relatable. For example, the analysis unit suggests the optimal model for users with the same emotion pattern. The analysis unit also uses the generation AI to evaluate the degree of empathy between users based on the emotion data and suggest a smartphone model that is likely to be emotionally relatable. For example, if there are many positive emotions, the analysis unit suggests a model with a wide range of entertainment functions. The analysis unit also uses the generation AI to analyze the emotion data and suggest a smartphone model that is likely to be emotionally relatable. For example, if stress is high, the analysis unit suggests a model with a wide range of relaxation functions. In this way, by comparing the user's emotion data with other users, it is possible to suggest a smartphone model that is likely to be emotionally relatable.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The status collection unit not only collects information about the user's smartphone usage, but also collects environmental data about the user's surroundings. For example, it can collect environmental data such as ambient volume, light intensity, and temperature, and suggest optimal settings based on the user's usage environment. The status collection unit can also collect performance data such as the user's smartphone's operating speed and app crash frequency, and suggest optimal maintenance and update timing. Furthermore, the status collection unit can monitor the security status of the user's smartphone and send an alert when a virus or malware is detected. This allows for a comprehensive understanding of the user's smartphone usage and the provision of optimal services.

[0090] The status collection unit not only collects the user's health data, but also collects the user's diet and nutritional intake status. For example, if the user uses an app to record their meals, that data can be collected and used to analyze nutritional balance. The status collection unit can also monitor the user's water intake and suggest appropriate times to hydrate. Furthermore, the status collection unit can monitor the user's stress level and suggest relaxation methods and activities for stress relief. This allows the user's health data to be collected more comprehensively and used to help manage their health.

[0091] The status collection unit can not only collect the user's location information, but also collect the user's means of transportation and traffic conditions. For example, if the user is driving a car, the status collection unit can collect driving patterns and traffic congestion information and suggest the optimal route. If the user is using public transportation, the status collection unit can collect operation status and delay information and provide optimal transfer guidance. Furthermore, the status collection unit can monitor the user's safety status during the user's trip and send an alert if a dangerous situation is detected. This allows for comprehensive collection of information about the user's trip and the provision of optimal services.

[0092] The state collection unit uses the emotion estimation function to collect emotions while the user is using the smartphone and can analyze the usage status based on the emotions. For example, the app analyzes the user's facial expressions and estimates the emotion while using the smartphone. For example, the camera is used to monitor the user's facial expressions in real time and calculate an emotion score. The state collection unit also analyzes the user's voice tone and estimates the emotional state through the app. For example, the microphone is used to analyze the tone and pitch of the user's voice and detect changes in emotion. The state collection unit also analyzes the user's touch pattern and estimates the emotional state through the app. For example, the strength and speed of the touch are monitored and the level of stress or excitement is analyzed. This makes it possible to analyze the usage status based on the user's emotions.

[0093] The status collection unit is also pre-installed on other devices, allowing for integrated collection of usage status across multiple devices. For example, an app can be pre-installed on a tablet, and usage status of a smartphone and tablet can be collected in an integrated manner. For example, the app usage time and battery consumption of both devices can be compared. The status collection unit can also pre-install an app on a smartwatch, integrating data from the smartphone and smartwatch. For example, it can link heart rate data from the smartwatch with smartphone usage status. The status collection unit can also pre-install an app on multiple devices, synchronizing data between devices in real time. For example, it can ensure that operations on a smartphone are reflected on tablets and smartwatches. This allows for integrated collection of usage status across multiple devices.

[0094] The status collection unit works in conjunction with smart home appliances in the home to collect comprehensive data, including the usage status of the appliances. For example, an app works in conjunction with smart home appliances to collect the usage status of the appliances. For example, it monitors the operation status of refrigerators and washing machines and integrates this with smartphone usage data. The status collection unit also analyzes the data from the smart home appliances through the app to understand the energy consumption patterns within the home. For example, it analyzes the usage status of air conditioners and lighting and makes suggestions to improve energy efficiency. The status collection unit also analyzes the lifestyle patterns within the home based on the data from the smart home appliances through the app. For example, it understands the amount of time spent watching TV and how often the oven is used and suggests optimal appliance settings. This makes it possible to work in conjunction with smart home appliances in the home to collect comprehensive data.

[0095] The state collection unit is equipped with an emotion estimation function and can collect emotions in real time while the user is performing a specific activity and suggest the most appropriate service. For example, an app can collect a user's emotions while exercising in real time and suggest the most appropriate fitness plan. For example, it can analyze the user's heart rate and facial expressions while exercising and adjust the intensity of the training. The state collection unit can also collect a user's emotions while relaxing and suggest the most appropriate relaxation service. For example, it can analyze breathing patterns and facial expressions while meditating and evaluate the level of relaxation. The state collection unit can also collect a user's emotions while working and suggest the most appropriate productivity improvement service. For example, if the user's concentration is declining, it can send a notification suggesting a break. This allows the app to collect a user's emotions in real time and suggest the most appropriate service.

[0096] The analysis unit can perform a more detailed lifestyle analysis, including the user's social media activity data. For example, the generation AI analyzes the content of the user's social media posts to understand lifestyle trends. For example, it identifies hobbies and interests from the frequency and content of posts. The analysis unit also analyzes the user's social media friendships to evaluate social connections. For example, it evaluates the user's sociability based on the number of friends and frequency of interactions. The analysis unit also analyzes the user's social media activity time to understand their daily rhythm. For example, it identifies the user's activity patterns from the time of day and frequency of posts. This makes it possible to perform a more detailed lifestyle analysis by including the user's social media activity data.

[0097] The analysis unit can make service suggestions based on consumer behavior, including the user's purchasing history. For example, the generation AI analyzes the user's online shopping history to understand consumer behavior trends. For example, the user's consumption patterns are identified based on purchase frequency and categories of purchased items. The analysis unit also uses the generation AI to suggest optimal services and products based on the user's purchasing history. For example, related products and services are recommended based on past purchase history. The analysis unit also uses the generation AI to analyze the user's purchasing history to understand changes in consumer behavior. For example, purchasing patterns during specific seasons or events are identified, and suggestions are made at appropriate times. This makes it possible to make service suggestions based on consumer behavior by including the user's purchasing history.

[0098] The analysis unit uses the emotion estimation function to analyze the user's emotional patterns and make lifestyle suggestions based on their emotions. For example, the generation AI analyzes the user's emotional data and identifies their emotional patterns. For example, it records daily emotional fluctuations and identifies trends in stress and happiness. The analysis unit then uses the generation AI to make lifestyle improvement suggestions based on the user's emotional patterns. For example, it might suggest relaxation services during times of high stress. The analysis unit also uses the generation AI to analyze the user's emotional data and suggest personalized services based on their emotions. For example, it might recommend activities or events during times when positive emotions are strong. This makes it possible to analyze the user's emotional patterns and make lifestyle suggestions based on their emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The status collection unit collects the smartphone's status, such as remaining battery power, network connection status, and app usage status. It can also collect health data such as the user's walking pattern and heart rate. Step 2: The information storage unit stores the information collected by the status collection unit in the server. For example, the information is stored taking into consideration the data format, storage period, security measures, etc. Step 3: The analysis unit analyzes the information stored by the information storage unit, taking into consideration, for example, the algorithm to be used, the purpose of the analysis, and how the analysis results will be used. Step 4: The notification unit notifies the user of the results of the analysis performed by the analysis unit, taking into consideration the timing of the notification, the means of notification (push notification, email, etc.), and the content of the notification, for example.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0127] 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.

[0128] 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.

[0129] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, to avoid confusion and 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.

[0167] 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. [Explanation of symbols]

[0168] 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 state collection unit that collects the state of the smartphone; an information storage unit that stores the information collected by the status collection unit in a server; an analysis unit that analyzes the information stored by the information storage unit; a notification unit that notifies a user of the results of the analysis performed by the analysis unit. A system characterized by:

2. The status collection unit Collecting health data such as walking patterns or heart rate of said user.

2. The system of claim 1.

3. The status collection unit Collecting the user's location information and proposing optimal settings or services according to the usage environment 2. The system of claim 1.

4. The status collection unit The system according to claim 1, further comprising: collecting emotions of the user while using the smartphone; and analyzing usage status based on the emotions.

5. The status collection unit The system according to claim 1, further comprising a pre-installed software for other devices, which collects information on usage across multiple devices in an integrated manner.

6. The status collection unit The system according to claim 1, characterized in that it works in conjunction with smart home appliances in the home to collect comprehensive data, including the usage status of the appliances.

Images