system

Abstract

An object of a system according to an embodiment is to manage a plurality of services in an integrated manner and to provide optimal support based on the behavior of a user.SOLUTION: A system according to an embodiment includes an API cooperation unit, an action analysis unit, and a support providing unit. The API linkage unit performs API linkage with various services such as a calendar, health management, a financial management tool, and a payment application. The behavior analysis unit analyzes a behavior of the user based on the data acquired by the API cooperation unit. The support providing unit provides optimal support to the user on the basis of the result analyzed by the behavior 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 have had the problem of not being able to centrally manage multiple services and provide optimal support based on user behavior.

[0005] The system according to the embodiment aims to centrally manage a plurality of services and provide optimal support based on the user's actions. [Means for solving the problem]

[0006] The system according to the embodiment includes an API integration unit, a behavior analysis unit, and a support providing unit. The API integration unit performs API integration with various services such as calendars, health management, financial management tools, and payment apps. The behavior analysis unit analyzes user behavior based on data acquired by the API integration unit. The support providing unit provides optimal support to the user based on the results of the analysis by the behavior analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage multiple services and provide optimal support based on user behavior. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A support system according to an embodiment of the present invention utilizes AI to act as a personal assistant to the user, helping them live a more efficient and fulfilling life. This support system integrates APIs with various services, such as calendars, health management tools, financial management tools, and payment apps, enabling centralized management and automatic adjustments. Furthermore, the AI ​​analyzes the user's behavior and provides optimal support tailored to their preferences and lifestyle. This allows the support system to streamline and enrich the user's life.

[0029] The support system according to the embodiment includes an API integration unit, a behavior analysis unit, and a support providing unit. The API integration unit performs API integration with various services, such as calendars, health management tools, financial management tools, and payment apps. For example, the API integration unit exchanges data with each service using a REST API. The API integration unit can securely acquire data using OAuth authentication. The API integration unit can also integrate with older systems using a SOAP API. The behavior analysis unit analyzes user behavior based on the data acquired by the API integration unit. For example, the behavior analysis unit collects user behavior logs and analyzes behavioral patterns using a machine learning algorithm. The behavior analysis unit can predict future behavior based on the user's past behavioral data. The behavior analysis unit can cluster user behavioral data and identify user groups with similar behavioral patterns. The support providing unit provides optimal support to the user based on the results of the analysis by the behavior analysis unit. For example, the support providing unit automatically adjusts the user's schedule and suggests optimal plans. The support providing unit can also suggest appropriate exercise plans based on the user's health data. The support providing unit can also provide advice to reduce wasteful spending based on the user's financial data. This allows the support system according to the embodiment to streamline and enrich the user's life. For example, the support system can link the user's calendar with a health management app and automatically update the calendar with exercise plans and health checkup schedules. The support system can also provide optimal support based on the user's behavioral data, improving the user's quality of life.

[0030] The API integration unit can link a calendar app with a health management app, automatically updating the calendar with exercise plans and health checkup schedules. For example, by linking a calendar app with a health management app, the API integration unit can automatically update the calendar with exercise plans and health checkup schedules. For example, the API integration unit can link Google Calendar with Fitbit to automatically add exercise plans to the calendar. The API integration unit can also link Apple Health with Outlook Calendar to automatically update health checkup schedules to the calendar. The API integration unit can also integrate data from the calendar app and the health management app to make health management more efficient for users. This allows exercise plans and health checkup schedules to be automatically updated on the calendar, making health management more efficient for users.

[0031] If the user has a habit of jogging every morning, the behavior analysis unit can suggest the optimal time for jogging based on the weather forecast. For example, if the user has a habit of jogging every morning, the behavior analysis unit can suggest the optimal time for jogging based on the weather forecast. For example, the behavior analysis unit uses a weather forecast API to obtain the temperature and probability of precipitation and calculate the optimal time for jogging. The behavior analysis unit can also suggest the optimal jogging time based on the user's past jogging data. The behavior analysis unit can also suggest the optimal jogging time based on the user's health data. In this way, suggesting the optimal jogging time can support the user in maintaining their health.

[0032] When a scheduled meeting is changed, the support providing unit can adjust it with other schedules and propose an optimal schedule. For example, when a scheduled meeting is changed, the support providing unit can adjust it with other schedules and propose an optimal schedule. For example, the support providing unit can automatically adjust it with other schedules when a scheduled meeting is changed based on data from a calendar app. The support providing unit can also propose an optimal schedule based on the user's priorities. The support providing unit can also propose an optimal schedule based on the user's past schedule data. This can reduce the burden on the user by automating schedule adjustments when a scheduled meeting is changed.

[0033] The behavior analysis unit can provide an appropriate exercise plan and advice on improving sleep based on the user's exercise data and sleep data. The behavior analysis unit can provide an appropriate exercise plan and advice on improving sleep based on the user's exercise data and sleep data, for example. For example, the behavior analysis unit can suggest an appropriate exercise plan based on the user's step count data and calorie consumption data. The behavior analysis unit can also provide advice on improving sleep based on the user's sleep time data and sleep quality data. The behavior analysis unit can also comprehensively analyze the user's health data and suggest an optimal health management plan. This can support the user's health management by providing an exercise plan and advice on improving sleep.

[0034] The behavior analysis unit can analyze the user's income and expenses and provide advice for reducing wasteful spending. The behavior analysis unit can, for example, analyze the user's income and expenses and provide advice for reducing wasteful spending. For example, the behavior analysis unit can identify wasteful spending based on the user's income data and expense data and provide advice for reducing it. The behavior analysis unit can also provide advice for reducing wasteful spending based on the user's past expense data. The behavior analysis unit can also comprehensively analyze the user's financial data and propose an optimal financial management plan. This can support the user's financial management by providing advice for reducing wasteful spending.

[0035] The support providing unit can suggest the optimal payment method when shopping online, thereby maximizing points and cash back. The support providing unit can suggest the optimal payment method when shopping online, thereby maximizing points and cash back. For example, the support providing unit can suggest the optimal payment method based on the user's credit card point program. The support providing unit can also suggest the optimal payment method based on the user's past payment data. The support providing unit can also suggest the optimal payment method based on the user's cash back program. By suggesting the optimal payment method, the user's payments can be made more efficient and points and cash back can be maximized.

[0036] In the API integration unit, the generation AI monitors the data of each service in real time, detects anomalies, and automatically corrects any problems that occur. In the API integration unit, for example, the generation AI monitors the data of each service in real time, and automatically corrects any anomalies that it detects. For example, if two appointments in a calendar app overlap, the generation AI automatically adjusts them. The API integration unit can also detect anomalies based on user behavior data and automatically correct them. The API integration unit can also predict anomalies based on the user's past data and correct them in advance. This allows for real-time detection of anomalies and automatic correction, improving system stability.

[0037] When integrating data from each service, the API integration unit allows the generation AI to automatically detect duplication and inconsistencies in the data and perform optimal data cleansing. For example, when integrating data from each service, the API integration unit automatically detects duplication and inconsistencies in the data and performs optimal data cleansing. For example, the API integration unit automatically integrates data when the same event is registered on multiple calendars. The API integration unit can also detect duplication and inconsistencies in the data based on user behavior data and perform optimal data cleansing. The API integration unit can also predict duplication and inconsistencies in the data based on the user's past data and perform cleansing in advance. This allows data consistency to be maintained by automatically detecting duplication and inconsistencies in the data and performing optimal data cleansing.

[0038] The API integration unit enables data sharing between different users and allows for centralized management with family and team members. The API integration unit, for example, enables data sharing between different users and allows for centralized management with family and team members. For example, the API integration unit uses cloud storage to integrate and share calendars for all family members. The API integration unit can also share project data with team members using shared folders. The API integration unit can also centrally manage data for family and team members using an integrated dashboard. This allows for data sharing between different users and allows for centralized management with family and team members, thereby achieving efficient data management.

[0039] The behavior analysis unit uses the generation AI to predict long-term behavioral patterns based on the user's behavioral data, allowing it to plan future support. The behavior analysis unit, for example, analyzes the user's behavioral data, allowing the generation AI to predict long-term behavioral patterns. For example, the behavior analysis unit can suggest a future exercise plan based on the user's regular exercise habits. The behavior analysis unit can also suggest a long-term nutritional management plan based on the user's dietary data. The behavior analysis unit can also suggest a long-term sleep improvement plan based on the user's sleep data. This makes it possible to predict long-term behavioral patterns and plan future support, thereby more efficiently supporting the user's life.

[0040] The behavior analysis unit allows the generation AI to compare the user's behavioral data with other users, learn best practices, and provide optimal support. For example, the behavior analysis unit provides advice based on success stories of users with the same goals. The behavior analysis unit can also provide optimal support based on the behavioral data of users in the same area. The behavior analysis unit can also cluster the user's behavioral data, identify user groups with similar behavioral patterns, and learn best practices. This allows the generation AI to compare the user's behavioral data with other users, learn best practices, and provide optimal support, thereby more efficiently supporting the user's life.

[0041] The behavior analysis unit can expand the scope of behavior analysis to include the user's pets and family members, thereby optimizing the overall lifestyle. The behavior analysis unit can, for example, expand the scope of behavior analysis to include the user's pets and family members, thereby optimizing the overall lifestyle. For example, the behavior analysis unit can propose an optimal exercise plan based on health management data for the pet. The behavior analysis unit can also propose an overall health management plan based on health data for the family members. The behavior analysis unit can also integrate the schedules of all family members and propose an optimal schedule. In this way, by expanding the scope of behavior analysis to include pets and family members, the overall lifestyle can be optimized.

[0042] The behavior analysis unit allows the generation AI to suggest hobbies and new activities based on the user's behavioral data, thereby improving the user's quality of life. For example, the behavior analysis unit may suggest new sports based on the user's exercise data. The behavior analysis unit may also suggest new books based on the user's reading data. The behavior analysis unit may also suggest new music based on the user's music data. This allows the generation AI to suggest hobbies and new activities, thereby improving the user's quality of life.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The support system can also suggest events and activities based on the user's hobbies and interests. For example, the support provider can provide concert information based on the user's favorite music genre. It can also provide ticket information for sporting events that the user is interested in. It can also recommend new books based on the user's reading history. This can improve the user's quality of life by suggesting events and activities that match the user's hobbies and interests.

[0045] The support system can further include a meal recording function to support the user's dietary management. For example, the support providing unit can record the meals the user has eaten and analyze the nutritional balance. It can also propose an appropriate meal plan based on the user's health goals. It can also provide safe ingredients and recipes taking into account the user's allergy information. This can support the user's dietary management and promote a healthy lifestyle.

[0046] The support system can further include a travel planning function to support the user's travel plans. For example, the support provider can suggest travel destinations based on the user's preferences. It can also create an optimal travel itinerary that matches the user's schedule. It can also suggest optimal accommodations and transportation options based on the user's budget. This allows the system to efficiently support the user's travel plans and provide a fulfilling travel experience.

[0047] The support system can also be equipped with a learning management function to further support the user's learning. For example, the support provider can record the user's learning progress and propose an appropriate learning plan based on the user's learning goals. It can also recommend new learning resources and courses based on the user's interests. It can also suggest optimal learning methods that match the user's learning style. This allows the system to efficiently support the user's learning and improve their learning outcomes.

[0048] The support system can further include a pet management function to support the user's pet's health management. For example, the support providing unit can record the pet's health data and propose an appropriate health management plan. It can also record the pet's diet and exercise and propose optimal diet and exercise plans. It can also manage the pet's regular health check and vaccination schedule. This supports the user's pet's health management and allows the pet to remain healthy.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The API integration unit performs API integration with various services such as calendars, health management tools, financial management tools, and payment apps. For example, the API integration unit exchanges data with each service using a REST API. The API integration unit can also securely obtain data using OAuth authentication. The API integration unit can also integrate with older systems using a SOAP API. Step 2: The behavior analysis unit analyzes user behavior based on the data acquired by the API integration unit. For example, the behavior analysis unit collects user behavior logs and analyzes behavioral patterns using machine learning algorithms. The behavior analysis unit can also predict future behavior based on the user's past behavior data. The behavior analysis unit can also cluster user behavior data and identify user groups with similar behavioral patterns. Step 3: The support providing unit provides optimal support to the user based on the results of the analysis by the behavior analysis unit. For example, the support providing unit automatically adjusts the user's schedule and suggests optimal plans. The support providing unit can also suggest an appropriate exercise plan based on the user's health data. The support providing unit can also provide advice on reducing wasteful spending based on the user's financial data.

[0051] (Example 2) A support system according to an embodiment of the present invention utilizes AI to act as a personal assistant to the user, helping them live a more efficient and fulfilling life. This support system integrates APIs with various services, such as calendars, health management tools, financial management tools, and payment apps, enabling centralized management and automatic adjustments. Furthermore, the AI ​​analyzes the user's behavior and provides optimal support tailored to their preferences and lifestyle. This allows the support system to streamline and enrich the user's life.

[0052] The support system according to the embodiment includes an API integration unit, a behavior analysis unit, and a support providing unit. The API integration unit performs API integration with various services, such as calendars, health management tools, financial management tools, and payment apps. For example, the API integration unit exchanges data with each service using a REST API. The API integration unit can securely acquire data using OAuth authentication. The API integration unit can also integrate with older systems using a SOAP API. The behavior analysis unit analyzes user behavior based on the data acquired by the API integration unit. For example, the behavior analysis unit collects user behavior logs and analyzes behavioral patterns using a machine learning algorithm. The behavior analysis unit can predict future behavior based on the user's past behavioral data. The behavior analysis unit can cluster user behavioral data and identify user groups with similar behavioral patterns. The support providing unit provides optimal support to the user based on the results of the analysis by the behavior analysis unit. For example, the support providing unit automatically adjusts the user's schedule and suggests optimal plans. The support providing unit can also suggest appropriate exercise plans based on the user's health data. The support providing unit can also provide advice to reduce wasteful spending based on the user's financial data. This allows the support system according to the embodiment to streamline and enrich the user's life. For example, the support system can link the user's calendar with a health management app and automatically update the calendar with exercise plans and health checkup schedules. The support system can also provide optimal support based on the user's behavioral data, improving the user's quality of life.

[0053] The API integration unit can link a calendar app with a health management app, automatically updating the calendar with exercise plans and health checkup schedules. For example, by linking a calendar app with a health management app, the API integration unit can automatically update the calendar with exercise plans and health checkup schedules. For example, the API integration unit can link Google Calendar with Fitbit to automatically add exercise plans to the calendar. The API integration unit can also link Apple Health with Outlook Calendar to automatically update health checkup schedules to the calendar. The API integration unit can also integrate data from the calendar app and the health management app to make health management more efficient for users. This allows exercise plans and health checkup schedules to be automatically updated on the calendar, making health management more efficient for users.

[0054] If the user has a habit of jogging every morning, the behavior analysis unit can suggest the optimal time for jogging based on the weather forecast. For example, if the user has a habit of jogging every morning, the behavior analysis unit can suggest the optimal time for jogging based on the weather forecast. For example, the behavior analysis unit uses a weather forecast API to obtain the temperature and probability of precipitation and calculate the optimal time for jogging. The behavior analysis unit can also suggest the optimal jogging time based on the user's past jogging data. The behavior analysis unit can also suggest the optimal jogging time based on the user's health data. In this way, suggesting the optimal jogging time can support the user in maintaining their health.

[0055] When a scheduled meeting is changed, the support providing unit can adjust it with other schedules and propose an optimal schedule. For example, when a scheduled meeting is changed, the support providing unit can adjust it with other schedules and propose an optimal schedule. For example, the support providing unit can automatically adjust it with other schedules when a scheduled meeting is changed based on data from a calendar app. The support providing unit can also propose an optimal schedule based on the user's priorities. The support providing unit can also propose an optimal schedule based on the user's past schedule data. This can reduce the burden on the user by automating schedule adjustments when a scheduled meeting is changed.

[0056] The behavior analysis unit can provide an appropriate exercise plan and advice on improving sleep based on the user's exercise data and sleep data. The behavior analysis unit can provide an appropriate exercise plan and advice on improving sleep based on the user's exercise data and sleep data, for example. For example, the behavior analysis unit can suggest an appropriate exercise plan based on the user's step count data and calorie consumption data. The behavior analysis unit can also provide advice on improving sleep based on the user's sleep time data and sleep quality data. The behavior analysis unit can also comprehensively analyze the user's health data and suggest an optimal health management plan. This can support the user's health management by providing an exercise plan and advice on improving sleep.

[0057] The behavior analysis unit can analyze the user's income and expenses and provide advice for reducing wasteful spending. The behavior analysis unit can, for example, analyze the user's income and expenses and provide advice for reducing wasteful spending. For example, the behavior analysis unit can identify wasteful spending based on the user's income data and expense data and provide advice for reducing it. The behavior analysis unit can also provide advice for reducing wasteful spending based on the user's past expense data. The behavior analysis unit can also comprehensively analyze the user's financial data and propose an optimal financial management plan. This can support the user's financial management by providing advice for reducing wasteful spending.

[0058] The support providing unit can suggest the optimal payment method when shopping online, thereby maximizing points and cash back. The support providing unit can suggest the optimal payment method when shopping online, thereby maximizing points and cash back. For example, the support providing unit can suggest the optimal payment method based on the user's credit card point program. The support providing unit can also suggest the optimal payment method based on the user's past payment data. The support providing unit can also suggest the optimal payment method based on the user's cash back program. By suggesting the optimal payment method, the user's payments can be made more efficient and points and cash back can be maximized.

[0059] When performing API integration with each service, the API integration unit allows the generation AI to estimate the user's emotions and propose the optimal integration method to elicit positive emotions. For example, when performing API integration with each service, the API integration unit allows the generation AI to analyze the user's emotions in real time and propose the optimal integration method to elicit positive emotions. For example, the API integration unit elicits positive emotions by automating tasks that cause the user stress. The API integration unit can also propose the optimal integration method based on the user's emotional data. The API integration unit can also propose the optimal integration method based on the user's past emotional data. In this way, by proposing the optimal integration method that takes the user's emotions into consideration, user satisfaction can be improved.

[0060] In the API integration unit, the generation AI monitors the data of each service in real time, detects anomalies, and automatically corrects any problems that occur. In the API integration unit, for example, the generation AI monitors the data of each service in real time, and automatically corrects any anomalies that it detects. For example, if two appointments in a calendar app overlap, the generation AI automatically adjusts them. The API integration unit can also detect anomalies based on user behavior data and automatically correct them. The API integration unit can also predict anomalies based on the user's past data and correct them in advance. This allows for real-time detection of anomalies and automatic correction, improving system stability.

[0061] When integrating data from each service, the API integration unit allows the generation AI to automatically detect duplication and inconsistencies in the data and perform optimal data cleansing. For example, when integrating data from each service, the API integration unit automatically detects duplication and inconsistencies in the data and performs optimal data cleansing. For example, the API integration unit automatically integrates data when the same event is registered on multiple calendars. The API integration unit can also detect duplication and inconsistencies in the data based on user behavior data and perform optimal data cleansing. The API integration unit can also predict duplication and inconsistencies in the data based on the user's past data and perform cleansing in advance. This allows data consistency to be maintained by automatically detecting duplication and inconsistencies in the data and performing optimal data cleansing.

[0062] The API integration unit enables data sharing between different users and allows for centralized management with family and team members. The API integration unit, for example, enables data sharing between different users and allows for centralized management with family and team members. For example, the API integration unit uses cloud storage to integrate and share calendars for all family members. The API integration unit can also share project data with team members using shared folders. The API integration unit can also centrally manage data for family and team members using an integrated dashboard. This allows for data sharing between different users and allows for centralized management with family and team members, thereby achieving efficient data management.

[0063] The API integration unit can use the emotion estimation function to identify the task that the user finds most stressful and make a suggestion to automate that task. The API integration unit can, for example, use the emotion estimation function to identify the task that the user finds most stressful and make a suggestion to automate that task. For example, the API integration unit can use facial expression recognition technology to estimate stress from the user's facial expression and identify the most stressful task. The API integration unit can also use voice analysis technology to estimate stress from the tone and speed of the user's voice and identify the most stressful task. The API integration unit can also estimate stress based on the user's biometric data and identify the most stressful task. As a result, the user's stress can be reduced by identifying the task that the user finds most stressful and making a suggestion to automate that task.

[0064] The behavior analysis unit allows the generative AI to estimate the user's emotions and provide optimal support based on those emotions. For example, when stress is high, it suggests relaxation methods. The behavior analysis unit allows the generative AI to estimate the user's emotions in real time and suggest relaxation methods when stress is high. For example, the behavior analysis unit may recommend using a meditation app. The behavior analysis unit may also suggest deep breathing methods. The behavior analysis unit may also suggest listening to music. In this way, by providing optimal support based on the user's emotions, it is possible to reduce the user's stress and improve their quality of life.

[0065] The behavior analysis unit uses the generation AI to predict long-term behavioral patterns based on the user's behavioral data, allowing it to plan future support. The behavior analysis unit, for example, analyzes the user's behavioral data, allowing the generation AI to predict long-term behavioral patterns. For example, the behavior analysis unit can suggest a future exercise plan based on the user's regular exercise habits. The behavior analysis unit can also suggest a long-term nutritional management plan based on the user's dietary data. The behavior analysis unit can also suggest a long-term sleep improvement plan based on the user's sleep data. This makes it possible to predict long-term behavioral patterns and plan future support, thereby more efficiently supporting the user's life.

[0066] The behavior analysis unit allows the generation AI to compare the user's behavioral data with other users, learn best practices, and provide optimal support. For example, the behavior analysis unit provides advice based on success stories of users with the same goals. The behavior analysis unit can also provide optimal support based on the behavioral data of users in the same area. The behavior analysis unit can also cluster the user's behavioral data, identify user groups with similar behavioral patterns, and learn best practices. This allows the generation AI to compare the user's behavioral data with other users, learn best practices, and provide optimal support, thereby more efficiently supporting the user's life.

[0067] The behavior analysis unit can expand the scope of behavior analysis to include the user's pets and family members, thereby optimizing the overall lifestyle. The behavior analysis unit can, for example, expand the scope of behavior analysis to include the user's pets and family members, thereby optimizing the overall lifestyle. For example, the behavior analysis unit can propose an optimal exercise plan based on health management data for the pet. The behavior analysis unit can also propose an overall health management plan based on health data for the family members. The behavior analysis unit can also integrate the schedules of all family members and propose an optimal schedule. In this way, by expanding the scope of behavior analysis to include pets and family members, the overall lifestyle can be optimized.

[0068] The behavior analysis unit allows the generation AI to suggest hobbies and new activities based on the user's behavioral data, thereby improving the user's quality of life. For example, the behavior analysis unit may suggest new sports based on the user's exercise data. The behavior analysis unit may also suggest new books based on the user's reading data. The behavior analysis unit may also suggest new music based on the user's music data. This allows the generation AI to suggest hobbies and new activities, thereby improving the user's quality of life.

[0069] The behavior analysis unit can use the emotion estimation function to identify the activity the user enjoys most and propose a schedule for increasing that activity. The behavior analysis unit can, for example, use the emotion estimation function to identify the activity the user enjoys most and propose a schedule for increasing that activity. For example, the behavior analysis unit can identify the activity the user enjoys based on the user's facial expression data. The behavior analysis unit can also identify the activity the user enjoys based on the user's voice data. The behavior analysis unit can also identify the activity the user enjoys based on the user's biometric data. This can improve the user's quality of life by proposing a schedule for increasing the activity the user enjoys.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The support system can also suggest events and activities based on the user's hobbies and interests. For example, the support provider can provide concert information based on the user's favorite music genre. It can also provide ticket information for sporting events that the user is interested in. It can also recommend new books based on the user's reading history. This can improve the user's quality of life by suggesting events and activities that match the user's hobbies and interests.

[0072] The support system can further include a meal recording function to support the user's dietary management. For example, the support providing unit can record the meals the user has eaten and analyze the nutritional balance. It can also propose an appropriate meal plan based on the user's health goals. It can also provide safe ingredients and recipes taking into account the user's allergy information. This can support the user's dietary management and promote a healthy lifestyle.

[0073] The support system can further include a travel planning function to support the user's travel plans. For example, the support provider can suggest travel destinations based on the user's preferences. It can also create an optimal travel itinerary that matches the user's schedule. It can also suggest optimal accommodations and transportation options based on the user's budget. This allows the system to efficiently support the user's travel plans and provide a fulfilling travel experience.

[0074] The support system can also be equipped with a learning management function to further support the user's learning. For example, the support provider can record the user's learning progress and propose an appropriate learning plan based on the user's learning goals. It can also recommend new learning resources and courses based on the user's interests. It can also suggest optimal learning methods that match the user's learning style. This allows the system to efficiently support the user's learning and improve their learning outcomes.

[0075] The support system can further include a pet management function to support the user's pet's health management. For example, the support providing unit can record the pet's health data and propose an appropriate health management plan. It can also record the pet's diet and exercise and propose optimal diet and exercise plans. It can also manage the pet's regular health check and vaccination schedule. This supports the user's pet's health management and allows the pet to remain healthy.

[0076] The behavior analysis unit can estimate the user's emotions and suggest relaxation methods when stress levels are high. For example, the behavior analysis unit can recommend using a meditation app. The behavior analysis unit can also suggest deep breathing techniques. The behavior analysis unit can also suggest listening to music. This allows optimal support to be provided based on the user's emotions, thereby reducing the user's stress and improving their quality of life.

[0077] The behavioral analysis unit can estimate the user's emotions and suggest an optimal exercise plan based on the emotions. For example, if the user is feeling stressed, the behavioral analysis unit can suggest relaxing yoga or stretching. If the user is feeling energetic, the behavioral analysis unit can suggest running or high-intensity training. Furthermore, the timing and frequency of exercise can be adjusted based on the user's emotional data. This makes it possible to support health management by providing an exercise plan based on the user's emotions.

[0078] The behavioral analysis unit can estimate the user's emotions and suggest an optimal meal plan based on the emotions. For example, if the user is feeling stressed, the behavioral analysis unit can suggest recipes using ingredients with a relaxing effect. Alternatively, if the user is feeling energetic, the behavioral analysis unit can suggest recipes using nutritious ingredients. Furthermore, the timing and amount of meals can be adjusted based on the user's emotional data. This makes it possible to support health management by providing a meal plan based on the user's emotions.

[0079] The behavioral analysis unit can estimate the user's emotions and propose an optimal sleep improvement plan based on the emotions. For example, if the user is feeling stressed, the behavioral analysis unit can propose a sleep environment using relaxing music or aromas. Also, if the user is feeling energetic, the behavioral analysis unit can propose a schedule to ensure appropriate sleep time. Furthermore, based on the user's emotional data, the system can provide advice on improving sleep quality. This makes it possible to support health management by providing a sleep improvement plan based on the user's emotions.

[0080] The behavioral analysis unit can estimate the user's emotions and suggest optimal ways to refresh based on the emotions. For example, if the user is feeling stressed, the behavioral analysis unit can suggest a nature walk or a trip to a hot spring. If the user is feeling energetic, the behavioral analysis unit can suggest active outdoor activities. Furthermore, the timing and frequency of refreshment can be adjusted based on the user's emotional data. This can improve the quality of life by providing ways to refresh based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The API integration unit performs API integration with various services such as calendars, health management tools, financial management tools, and payment apps. For example, the API integration unit exchanges data with each service using a REST API. The API integration unit can also securely obtain data using OAuth authentication. The API integration unit can also integrate with older systems using a SOAP API. Step 2: The behavior analysis unit analyzes user behavior based on the data acquired by the API integration unit. For example, the behavior analysis unit collects user behavior logs and analyzes behavioral patterns using machine learning algorithms. The behavior analysis unit can also predict future behavior based on the user's past behavior data. The behavior analysis unit can also cluster user behavior data and identify user groups with similar behavioral patterns. Step 3: The support providing unit provides optimal support to the user based on the results of the analysis by the behavior analysis unit. For example, the support providing unit automatically adjusts the user's schedule and suggests optimal plans. The support providing unit can also suggest an appropriate exercise plan based on the user's health data. The support providing unit can also provide advice on reducing wasteful spending based on the user's financial data.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. The API Integration Department handles API integration with various services such as calendars, health management tools, financial management tools, and payment apps. a behavior analysis unit that analyzes user behavior based on the data acquired by the API collaboration unit; a support providing unit that provides optimal support to the user based on the results of the analysis by the behavior analysis unit. A system characterized by:

2. The API collaboration unit Link your calendar app with your health management app to automatically update your exercise schedule and health checkup schedule on your calendar.

2. The system of claim 1.

3. The behavior analysis unit If the user has the habit of jogging every morning, the system suggests the best time to jog based on the weather forecast.

2. The system of claim 1.

4. The support providing unit If the meeting schedule is changed, coordinate with the other schedules and propose the optimal schedule.

2. The system of claim 1.

5. The behavior analysis unit Providing appropriate exercise plans and advice on improving sleep based on the user's exercise and sleep data 2. The system of claim 1.

6. The support providing unit When shopping online, we suggest the most suitable payment method to maximize points and cashback.

2. The system of claim 1.

7. The Behavioral Analysis Department The generative AI estimates the user's emotions and provides optimal support based on those emotions. For example, when the stress level is high, it suggests relaxation methods.

2. The system of claim 1.

8. The behavior analysis unit Using emotion estimation capabilities, identify the activities the user enjoys most and suggest a schedule for increasing those activities.

2. The system of claim 1.

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