System

Abstract

An object of a system according to an embodiment is to set a small daily goal on the basis of a lifestyle and a behavior pattern of a user.SOLUTION: A system includes a lifestyle analysis unit and a target setting unit. The lifestyle analysis unit analyzes a lifestyle and a behavior pattern of the user. The goal setting unit sets a small daily goal based on the behavior pattern of the user analyzed by the lifestyle habit 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 techniques do not adequately set daily goals based on a user's lifestyle and behavioral patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to set small daily goals based on the user's lifestyle habits and behavior patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle analysis unit and a goal setting unit. The lifestyle analysis unit analyzes the lifestyle and behavioral patterns of a user. The goal setting unit sets small daily goals based on the behavioral patterns of the user analyzed by the lifestyle analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can set small daily goals based on the user's lifestyle and behavioral patterns. [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) CherishTime AI, an embodiment of the present invention, is an AI support app that learns from memories of deceased loved ones and enriches current relationships. This AI support app uses past regrets as lessons to suggest activities to deepen current relationships with important people. In this way, CherishTime AI allows users to learn from past regrets, cherish the present moment, and build more fulfilling relationships for the future.

[0029] CherishTime AI according to an embodiment includes a lifestyle analysis unit and a goal setting unit. The lifestyle analysis unit analyzes a user's lifestyle and behavioral patterns. For example, the lifestyle analysis unit analyzes the user's activity times. The lifestyle analysis unit can also analyze the user's hobbies and interests. The lifestyle analysis unit performs analysis using data provided by the user and smartphone sensor information. For example, the lifestyle analysis unit analyzes the user's activity times and identifies the time when the user is most active. The lifestyle analysis unit analyzes the user's hobbies and interests and identifies the user's preferred activities. The goal setting unit sets small daily goals based on the user's behavioral patterns analyzed by the lifestyle analysis unit. For example, the goal setting unit sets a goal such as "expressing gratitude to family once a day." The goal setting unit can also set a goal such as "making time to spend with friends on the weekend." The goal setting unit also sets goals that are easy for the user to achieve based on the user's lifestyle and behavioral patterns. For example, the goal setting unit sets goals based on the user's activity times, allowing the user to achieve them without difficulty. The goal setting unit also sets goals based on the user's hobbies and interests, allowing the user to achieve the goals while having fun. In this way, CherishTime AI according to the embodiment analyzes the user's lifestyle and behavioral patterns and sets small daily goals, thereby strengthening bonds with family and friends. For example, when a user expresses gratitude to family, the bond with family deepens. Furthermore, when a user spends time with friends, the relationship with friends is strengthened. Furthermore, by setting goals that are easy for the user to achieve, the user can feel the joy of achieving the goal.

[0030] The lifestyle habit analysis unit can analyze the impact of seasonal and weather changes on behavioral patterns based on the user's lifestyle habit data. The lifestyle habit analysis unit, for example, integrates the user's lifestyle habit data with weather data and analyzes the impact of seasonal and weather changes on behavioral patterns. For example, the lifestyle habit analysis unit analyzes a tendency to go out less on rainy days and suggests indoor activities. The lifestyle habit analysis unit can also analyze changes in behavioral patterns with seasonal changes and set goals according to the season. The lifestyle habit analysis unit can also analyze changes in behavioral patterns with weather changes and set goals according to the weather. For example, the lifestyle habit analysis unit analyzes a tendency to go out less in winter and sets indoor exercise goals. The lifestyle habit analysis unit analyzes a tendency to go out more in summer and sets outdoor activity goals. In this way, analyzing the impact of seasonal and weather changes on behavioral patterns enables more appropriate goal setting. For example, setting indoor activity goals taking into account a tendency to go out less on rainy days makes it easier for the user to achieve their goals. Setting goals according to seasonal changes allows the user to enjoy activities that match the season.

[0031] The lifestyle habit analysis unit can compare the user's past and current behavioral patterns to detect changes in behavior and identify the causes. The lifestyle habit analysis unit, for example, compares the user's past and current behavioral patterns to detect changes in behavior. For example, the lifestyle habit analysis unit can analyze changes in exercise habits based on data from the past year and identify the causes. The lifestyle habit analysis unit can also compare the user's past and current behavioral patterns to detect changes in eating habits. The lifestyle habit analysis unit can also detect changes in behavior and identify the causes, thereby making suggestions for the user to improve their behavior. For example, the lifestyle habit analysis unit can detect changes in exercise habits and identify the causes as stress. The lifestyle habit analysis unit can detect changes in eating habits and identify the causes as being busy at work. By comparing past and current behavioral patterns and detecting changes in behavior, more appropriate goal setting becomes possible. For example, if a change in exercise habits is detected and the cause is stress, a goal to reduce stress can be set. It can also detect changes in eating habits and, if the cause is a busy work schedule, set goals for eating healthy meals during your free time.

[0032] When analyzing the user's lifestyle habits and behavioral patterns, the lifestyle habit analysis unit can also integrate data on family and friends and analyze their mutual influences. For example, the lifestyle habit analysis unit integrates the user's lifestyle habit data with data on family and friends and analyzes their mutual influences. For example, the lifestyle habit analysis unit analyzes the eating patterns of all family members and suggests healthy eating habits. The lifestyle habit analysis unit can also analyze interaction data with friends and set goals to strengthen relationships with friends. The lifestyle habit analysis unit can also integrate data on family and friends and analyze the influence on the user's behavioral patterns. For example, the lifestyle habit analysis unit analyzes the exercise habits of all family members and sets exercise goals for the whole family to work on. The lifestyle habit analysis unit analyzes interaction data with friends and suggests activities to strengthen relationships with friends. In this way, integrating data on family and friends and analyzing their mutual influences enables more appropriate goal setting. For example, analyzing the eating patterns of all family members and suggesting healthy eating habits allows the whole family to live a healthy lifestyle. Analyzing interaction data with friends and setting goals to strengthen relationships with friends allows the user to deepen their relationships with friends.

[0033] The lifestyle habit analysis unit can also utilize data from smart home devices and wearable devices to analyze the user's behavioral patterns. For example, the lifestyle habit analysis unit utilizes data from smart home devices to analyze the user's behavioral patterns. For example, the lifestyle habit analysis unit analyzes the user's sleep patterns based on data from smart lighting and temperature sensors. The lifestyle habit analysis unit can also analyze the user's exercise patterns using data from wearable devices. The lifestyle habit analysis unit can also integrate data from smart home devices and wearable devices to analyze the user's behavioral patterns in detail. For example, the lifestyle habit analysis unit analyzes the user's indoor activity patterns based on data from smart home devices. The lifestyle habit analysis unit also analyzes the user's exercise volume based on data from wearable devices. This enables more accurate behavioral pattern analysis by utilizing data from smart home devices and wearable devices. For example, analyzing the user's sleep patterns based on data from smart lighting and temperature sensors can set goals for the user to get better sleep. In addition, by analyzing the user's exercise patterns based on data from the wearable device, the user can set goals for living a healthy lifestyle.

[0034] The goal setting unit can learn the most effective goal setting pattern from the user's past behavioral history and propose individually optimized goals. The goal setting unit, for example, analyzes the user's past behavioral history to learn the most effective goal setting pattern. For example, the goal setting unit proposes individually optimized goals based on patterns of goal setting that were successful in the past. The goal setting unit can also analyze patterns of goal setting that were unsuccessful in the past and propose goals to avoid the same failures. The goal setting unit can also set goals that are easy for the user to achieve based on the user's behavioral history. For example, the goal setting unit proposes goals that are easy for the user to achieve based on patterns of goal setting that were successful in the past. The goal setting unit sets goals that are easy for the user to achieve based on patterns of goal setting that were unsuccessful in the past. In this way, by learning from the past behavioral history, it is possible to propose optimal goals for the user. For example, by proposing goals that are easy for the user to achieve based on patterns of goal setting that were successful in the past, the probability that the user will achieve their goal is increased. Furthermore, by setting goals that are easy for the user to achieve based on patterns of goal setting that were unsuccessful in the past, the risk that the user will not achieve their goal can be reduced.

[0035] The goal setting unit can send reminders for achieving goals at optimal timings in accordance with the user's lifestyle rhythm. The goal setting unit, for example, analyzes the user's lifestyle rhythm and builds a system that sends reminders for achieving goals at optimal timings. For example, the goal setting unit sends reminders during times when the user is most active. The goal setting unit can also send reminders during times when the user is relaxing. The goal setting unit can also customize the content of reminders in accordance with the user's lifestyle rhythm. For example, the goal setting unit sends a reminder for an exercise goal during times when the user is most active. The goal setting unit also sends a reminder for a relaxation goal during times when the user is relaxing. In this way, sending reminders in accordance with the user's lifestyle rhythm increases the probability of goal achievement. For example, sending a reminder during times when the user is most active makes it easier for the user to achieve their goal. Also, sending a reminder during times when the user is relaxing allows the user to achieve their goal without feeling stressed.

[0036] The goal setting unit can monitor the degree of goal achievement in real time and dynamically adjust the goal according to the degree of achievement. The goal setting unit, for example, builds a system that monitors the degree of goal achievement in real time and dynamically adjusts the goal according to the degree of achievement. For example, the goal setting unit makes the goal easier when the degree of goal achievement is low. Furthermore, the goal setting unit can make the goal more difficult when the degree of goal achievement is high. Furthermore, the goal setting unit can dynamically adjust the goal based on user feedback. For example, the goal setting unit makes the goal more difficult when the user feels that the goal is easy to achieve. Furthermore, the goal setting unit makes the goal easier when the user feels that the goal is difficult to achieve. In this way, by monitoring the degree of goal achievement in real time and dynamically adjusting the goal, it becomes easier to maintain the user's motivation. For example, when the degree of goal achievement is low, making the goal easier makes it easier for the user to achieve the goal. Furthermore, when the degree of goal achievement is high, making the goal more difficult can motivate the user to take on challenges.

[0037] The goal setting unit can set a joint goal with family and friends and share the degree of achievement, thereby increasing mutual motivation. The goal setting unit, for example, builds a system for setting a joint goal with family and friends and sharing the degree of achievement. For example, the goal setting unit sets a fitness challenge for the whole family to work on together and shares the degree of achievement. The goal setting unit can also set a joint goal with friends and share the degree of achievement. The goal setting unit can also strengthen bonds with family and friends by sharing the degree of achievement. For example, the goal setting unit can set a fitness challenge for the whole family to work on together and share the degree of achievement, thereby allowing the whole family to live a healthy lifestyle. The goal setting unit can also strengthen relationships with friends by setting a joint goal with friends and sharing the degree of achievement. In this way, by setting a joint goal and sharing the degree of achievement, bonds with family and friends can be strengthened and mutual motivation can be increased. For example, by setting a fitness challenge for the whole family to work on together and sharing the degree of achievement, the whole family can live a healthy lifestyle. The goal setting unit can also strengthen relationships with friends by setting a joint goal with friends and sharing the degree of achievement.

[0038] The goal setting unit can provide guides and tutorials for performing the activity suggested by the generation AI as support for achieving the goal. The goal setting unit, for example, builds a system that provides guides and tutorials for performing the activity suggested by the generation AI. For example, the goal setting unit provides a video on how to perform a fitness activity. The goal setting unit can also provide a text guide for performing the activity suggested by the generation AI. The goal setting unit can also provide a tutorial for performing the activity suggested by the generation AI. For example, the goal setting unit can provide a video on how to perform a fitness activity to enable the user to perform the activity correctly. The goal setting unit can also provide a text guide for performing the activity suggested by the generation AI to make it easier for the user to understand the activity. In this way, by providing guides and tutorials, the user can effectively perform the activity to achieve the goal. For example, by providing a video on how to perform a fitness activity, the user can exercise with the correct form. Furthermore, by providing a text guide for performing the activity suggested by the generation AI, it is easier for the user to understand the purpose and method of the activity.

[0039] The goal setting unit can ensure consistency with the user's current living situation and goals when making activity suggestions based on past regrets. The goal setting unit, for example, analyzes the user's current living situation and goals and makes activity suggestions based on past regrets. For example, the goal setting unit makes activity suggestions that match the user's current lifestyle rhythm, learning from regrets. The goal setting unit can also make activity suggestions that match the user's current goals. The goal setting unit can also make suggestions that are easy for the user to carry out by ensuring consistency with the user's current living situation and goals. For example, the goal setting unit makes activity suggestions that match the user's current lifestyle rhythm, allowing the user to carry out the suggestions without difficulty. The goal setting unit also makes activity suggestions that match the user's current goals, making it easier for the user to achieve the goals. In this way, by ensuring consistency between the activity suggestions based on past regrets and the current living situation and goals, it is possible to make suggestions that are easy for the user to carry out. For example, by making activity suggestions that match the user's current lifestyle rhythm, the user can carry out the suggestions without difficulty. Furthermore, by making activity suggestions that match the user's current goals, the user can easily achieve the goals.

[0040] The goal setting unit can provide a step-by-step guide for the user to overcome past regrets and monitor progress. The goal setting unit, for example, builds a system that provides a step-by-step guide for the user to overcome past regrets. For example, the goal setting unit presents a specific action plan and monitors progress. The goal setting unit can also support the user in overcoming regrets by following the step-by-step guide. The goal setting unit can also monitor progress and check whether the user is progressing as planned. For example, the goal setting unit monitors whether the user is acting according to the specific action plan. The goal setting unit can also check whether the user is progressing as planned and provide feedback as needed. In this way, providing a step-by-step guide and monitoring progress makes it easier for the user to overcome past regrets. For example, presenting a specific action plan allows the user to clearly understand what they should do. Monitoring progress can also check whether the user is progressing as planned and provide support as needed.

[0041] The goal setting unit can anonymize data related to a user's past regrets and integrate it with data from other users to extract common lessons. The goal setting unit, for example, builds a system that anonymizes data related to a user's past regrets and integrates it with data from other users to extract common lessons. For example, the goal setting unit analyzes data from multiple users and suggests common lessons. By anonymizing the user's data, the goal setting unit can utilize the data while protecting privacy. By integrating the data with data from other users, the goal setting unit can extract lessons based on a larger amount of data. For example, the goal setting unit analyzes data from multiple users and suggests common lessons, thereby supporting the user in preventing the user from repeating the same mistake. By anonymizing the user's data, the goal setting unit can utilize the data while protecting privacy. This makes it easier to extract common lessons by anonymizing the data and integrating it with data from other users. For example, by analyzing data from multiple users and suggesting common lessons, the user is prevented from repeating the same mistake. By anonymizing the user's data, the data can be utilized while protecting privacy.

[0042] The goal setting unit can customize activity suggestions based on past regrets to match the user's hobbies and interests. The goal setting unit, for example, analyzes the user's hobbies and interests and builds a system that customizes activity suggestions based on past regrets. For example, the goal setting unit suggests activities related to hobbies and learns from regrets. The goal setting unit can also make activity suggestions based on the user's interests. The goal setting unit can also make suggestions that are easy for the user to carry out by customizing the activity suggestions to match the user's hobbies and interests. For example, the goal setting unit suggests activities related to hobbies and learns from regrets while enjoying the activity. The goal setting unit also makes activity suggestions based on the user's interests, allowing the user to carry out the activity with interest. In this way, customizing the activity suggestions to match the hobbies and interests enables suggestions that are easy for the user to carry out. For example, by suggesting activities related to hobbies, the user can learn from regrets while enjoying the activity. The activity suggestions based on interests allow the user to carry out the activity with interest.

[0043] The goal setting unit can monitor the user's current living situation in real time and suggest specific actions to cherish the moments. The goal setting unit, for example, builds a system that monitors the user's current living situation in real time and suggests specific actions to cherish the moments. For example, the goal setting unit suggests meditation during a time when the user can relax. The goal setting unit can also suggest actions to help the user feel grateful. The goal setting unit can monitor the user's living situation in real time and suggest actions to help the user cherish the moments. For example, the goal setting unit suggests meditation during a time when the user can relax. The goal setting unit can also suggest actions to help the user feel grateful. In this way, by monitoring the living situation in real time and suggesting specific actions, the user can be supported in cherishing the present moment. For example, by suggesting meditation during a time when the user can relax, the user can have time to relax. Furthermore, by suggesting actions to help the user feel grateful, the user can feel grateful.

[0044] The goal setting unit enables the generation AI to provide feedback in real time when the user performs an activity to cherish the present moment. The goal setting unit, for example, builds a system in which the generation AI provides feedback in real time when the user performs an activity to cherish the present moment. For example, the goal setting unit monitors the level of relaxation during meditation and provides feedback. The goal setting unit can also provide feedback to increase gratitude when the user keeps a gratitude diary. The goal setting unit can also provide feedback in real time when the user performs an activity to cherish the present moment. For example, the goal setting unit monitors the level of relaxation during meditation and provides feedback. The goal setting unit can also provide feedback to increase gratitude when the user keeps a gratitude diary. As a result, by providing feedback in real time, the user can effectively perform an activity to cherish the present moment. For example, monitoring the level of relaxation during meditation and providing feedback allows the user to have time to relax. Furthermore, providing feedback to increase gratitude when the user keeps a gratitude diary allows the user to feel grateful.

[0045] The goal setting unit enables the user to share with family and friends an activity for cherishing the present moment and perform it together. The goal setting unit, for example, builds a system for sharing with family and friends an activity for cherishing the present moment and performing it together. For example, the goal setting unit keeps a gratitude diary with the whole family. The goal setting unit can also meditate with friends. The goal setting unit can also support the user in cherishing the present moment by sharing an activity with family and friends and performing it together. For example, the goal setting unit enables the whole family to feel grateful by keeping a gratitude diary with the whole family. The goal setting unit can also support the user in cherishing the present moment by meditating with friends. In this way, the goal setting unit can support the user in cherishing the present moment by sharing and performing an activity with family and friends. For example, the goal setting unit enables the whole family to feel grateful by keeping a gratitude diary with the whole family. In addition, the goal setting unit can support the user in cherishing the present moment by sharing and performing an activity with family and friends.

[0046] The goal setting unit can customize activities for cherishing the present moment to match the user's lifestyle rhythm. The goal setting unit, for example, analyzes the user's lifestyle rhythm and builds a system that customizes activities for cherishing the present moment. For example, the goal setting unit suggests meditation for a time when the user can relax most. The goal setting unit can also suggest activities that will help the user feel grateful. The goal setting unit can also make suggestions that are easy for the user to carry out by customizing activities to match the user's lifestyle rhythm. For example, the goal setting unit can suggest meditation for a time when the user can relax most, so that the user has time to relax. The goal setting unit can also suggest activities that will help the user feel grateful, so that the user has gratitude. In this way, customizing activities to match the user's lifestyle rhythm can support the user in cherishing the present moment. For example, suggesting meditation for a time when the user can relax most allows the user to have time to relax. Also, suggesting activities that will help the user feel grateful allows the user to feel grateful.

[0047] The goal setting unit can analyze in detail the user's hopes and goals for the future and, based on the analysis, suggest specific steps for building relationships. The goal setting unit, for example, builds a system that analyzes in detail the user's hopes and goals for the future and, based on the analysis, suggests specific steps for building relationships. For example, the goal setting unit can suggest keeping in touch with friends on a regular basis. The goal setting unit can also suggest building new relationships through a new hobby. The goal setting unit can also suggest specific steps for the user to build fulfilling relationships based on the user's hopes and goals. For example, the goal setting unit can suggest keeping in touch with friends on a regular basis. The goal setting unit can also suggest building new relationships through a new hobby. In this way, by analyzing in detail the hopes and goals for the future and, based on the analysis, suggesting specific steps for building relationships, the system can support the user in building fulfilling relationships. For example, by suggesting keeping in touch with friends on a regular basis, the user can maintain their relationships with friends. By suggesting building new relationships through a new hobby, the system can support the user in building fulfilling relationships.

[0048] The goal setting unit allows the generation AI to provide real-time support when the user performs an activity to build new relationships. The goal setting unit, for example, builds a system in which the generation AI provides real-time support when the user performs an activity to build new relationships. For example, the goal setting unit provides information necessary when starting a new hobby. The goal setting unit can also cause the generation AI to provide real-time advice when the user performs an activity to build new relationships. The goal setting unit can also cause the generation AI to provide real-time support when the user performs an activity to build new relationships, thereby enabling the user to effectively perform the activity. For example, the goal setting unit provides information necessary when starting a new hobby. The goal setting unit can also cause the generation AI to provide real-time advice when the user performs an activity to build new relationships. In this way, by providing real-time support, the user can effectively perform the activity to build new relationships. For example, by providing information necessary when starting a new hobby, it becomes easier for the user to start a new hobby. The generation AI can also provide advice in real time, allowing the user to effectively perform the activity to build new relationships.

[0049] The goal setting unit can analyze the user's past interpersonal data, extract successful patterns, and utilize them in future suggestions. The goal setting unit, for example, builds a system that analyzes the user's past interpersonal data, extracts successful patterns, and utilizes them in future suggestions. For example, the goal setting unit suggests methods for building interpersonal relationships that were successful in the past. The goal setting unit can also analyze interpersonal relationship building methods that failed in the past and make suggestions to avoid the same failures. The goal setting unit can also make suggestions for the user to build fulfilling interpersonal relationships based on the user's past interpersonal data. For example, the goal setting unit suggests methods for building interpersonal relationships that were successful in the past. The goal setting unit can also analyze interpersonal relationship building methods that failed in the past and make suggestions to avoid the same failures. In this way, by analyzing the past interpersonal relationship data, extracting successful patterns, and utilizing them in future suggestions, the user can be supported in building fulfilling interpersonal relationships. For example, by suggesting interpersonal relationship building methods that were successful in the past, the user's chances of success using the same methods increase. Furthermore, by analyzing interpersonal relationship building methods that failed in the past and making suggestions to avoid the same failures, the user's risk of failure can be reduced.

[0050] The goal setting unit can suggest activities from the perspective of different cultures or regions that will enable the user to build fulfilling human relationships for the future. The goal setting unit, for example, constructs a system that suggests activities from the perspective of different cultures or regions that will enable the user to build fulfilling human relationships for the future. For example, the goal setting unit suggests participation in an intercultural exchange event. The goal setting unit can also suggest activities that incorporate local customs. The goal setting unit can also support the user in building diverse human relationships by performing activities from the perspective of different cultures or regions. For example, by suggesting participation in an intercultural exchange event, the user can have an opportunity to interact with people from different cultures. The goal setting unit can also support the user in building diverse human relationships by suggesting activities from the perspective of different cultures or regions. For example, by suggesting participation in an intercultural exchange event, the user can have an opportunity to interact with people from different cultures. Furthermore, by suggesting activities that incorporate local customs, the user can have an opportunity to interact with people in the local area. In this way, by suggesting activities from the perspective of different cultures or regions, the system can support the user in building diverse human relationships. For example, by suggesting participation in an intercultural exchange event, the user can have an opportunity to interact with people from different cultures. Furthermore, by suggesting activities that incorporate local customs, the user can have an opportunity to interact with people in the local area.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] CherishTime AI can also be equipped with a health management unit that monitors the user's health. The health management unit collects the user's health data and analyzes their health condition. For example, the health management unit monitors the user's heart rate and blood pressure and sends an alert if an abnormality is detected. The health management unit can also analyze the user's diet and exercise habits and provide advice on living a healthy lifestyle. This allows the user to constantly monitor their health condition and take appropriate measures. For example, if the user's heart rate is high, the unit can suggest relaxing activities. Also, if the user's diet is unbalanced, the unit can suggest nutritionally balanced meals.

[0053] CherishTime AI can also be equipped with a hobby analysis unit that delves deeper into the user's hobbies and interests. The hobby analysis unit collects and analyzes data related to the user's hobbies and interests. For example, the hobby analysis unit can identify the user's hobbies and suggest activities related to those hobbies. The hobby analysis unit can also support the user in finding new hobbies. This allows the user to deepen their hobbies and interests and spend their time more fulfillingly. For example, if the user is interested in music, music events and instrument lessons can be suggested. Also, if the user is interested in outdoor activities, hiking and camping activities can be suggested.

[0054] CherishTime AI can also be equipped with a learning support unit to increase the user's motivation to learn. The learning support unit collects the user's learning data and provides advice to increase their motivation to learn. For example, the learning support unit can analyze the user's learning style and suggest a learning method that suits that style. The learning support unit can also support the user in achieving their learning goals. This allows the user to study effectively. For example, if the user has a visual learning style, it can suggest a learning method that uses visual aids. Also, if the user has an auditory learning style, it can suggest a learning method that uses audio materials.

[0055] CherishTime AI can also be equipped with a creativity support unit to bring out the user's creativity. The creativity support unit supports the user's creative activities and provides advice to enhance their creativity. For example, the creativity support unit can analyze the creative activities the user is engaged in and make suggestions for further developing those activities. The creativity support unit can also support the user in finding new creative ideas, allowing the user to maximize their creativity. For example, if the user is interested in painting, the unit can suggest new techniques and styles. Or, if the user is interested in writing, the unit can provide creative hints and inspiration.

[0056] CherishTime AI can further include a social skills support unit to improve the user's social skills. The social skills support unit analyzes the user's social skills and provides advice to improve them. For example, the social skills support unit can analyze the user's communication style and suggest a communication method that suits that style. The social skills support unit can also support the user in building relationships. This allows the user to effectively improve their social skills. For example, if the user has an introverted communication style, the social skills support unit can suggest ways to express themselves. Also, if the user has an extroverted communication style, the social skills support unit can suggest ways to deepen relationships with others.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The lifestyle analysis unit analyzes the user's lifestyle and behavioral patterns. For example, it analyzes what time of day the user is active, and what hobbies and interests the user has. The lifestyle analysis unit performs its analysis using data provided by the user and sensor information from the smartphone. This makes it possible to identify the user's active time periods, hobbies, and interests. Step 2: The goal setting unit sets small daily goals based on the user's behavioral patterns analyzed by the lifestyle analysis unit. For example, goals such as "expressing gratitude to family once a day" or "making time to spend with friends on the weekend" are set. The goal setting unit sets goals that are easy for the user to achieve based on the user's activity times, hobbies, and interests.

[0059] (Example 2) CherishTime AI, an embodiment of the present invention, is an AI support app that learns from memories of deceased loved ones and enriches current relationships. This AI support app uses past regrets as lessons to suggest activities to deepen current relationships with important people. In this way, CherishTime AI allows users to learn from past regrets, cherish the present moment, and build more fulfilling relationships for the future.

[0060] CherishTime AI according to an embodiment includes a lifestyle analysis unit and a goal setting unit. The lifestyle analysis unit analyzes a user's lifestyle and behavioral patterns. For example, the lifestyle analysis unit analyzes the user's activity times. The lifestyle analysis unit can also analyze the user's hobbies and interests. The lifestyle analysis unit performs analysis using data provided by the user and smartphone sensor information. For example, the lifestyle analysis unit analyzes the user's activity times and identifies the time when the user is most active. The lifestyle analysis unit analyzes the user's hobbies and interests and identifies the user's preferred activities. The goal setting unit sets small daily goals based on the user's behavioral patterns analyzed by the lifestyle analysis unit. For example, the goal setting unit sets a goal such as "expressing gratitude to family once a day." The goal setting unit can also set a goal such as "making time to spend with friends on the weekend." The goal setting unit also sets goals that are easy for the user to achieve based on the user's lifestyle and behavioral patterns. For example, the goal setting unit sets goals based on the user's activity times, allowing the user to achieve them without difficulty. The goal setting unit also sets goals based on the user's hobbies and interests, allowing the user to achieve the goals while having fun. In this way, CherishTime AI according to the embodiment analyzes the user's lifestyle and behavioral patterns and sets small daily goals, thereby strengthening bonds with family and friends. For example, when a user expresses gratitude to family, the bond with family deepens. Furthermore, when a user spends time with friends, the relationship with friends is strengthened. Furthermore, by setting goals that are easy for the user to achieve, the user can feel the joy of achieving the goal.

[0061] The lifestyle habit analysis unit performs emotion analysis when analyzing a user's lifestyle habits and behavioral patterns, and can predict behavioral patterns based on emotional fluctuations. The lifestyle habit analysis unit, for example, collects the user's lifestyle data and performs emotion analysis using generative AI. For example, it analyzes the user's emotions during specific time periods and predicts behavioral patterns based on the emotional fluctuations. The lifestyle habit analysis unit can also analyze the user's emotional fluctuations and identify situations in which the user feels positive emotions. The lifestyle habit analysis unit can also predict what behavior the user will take based on the user's emotional fluctuations. For example, the lifestyle habit analysis unit predicts what behavior the user will take during times when the user feels positive emotions. This enables more accurate goal setting by predicting behavioral patterns based on emotional fluctuations. For example, setting goals during times when the user feels positive emotions makes the user more likely to achieve their goals. Furthermore, not setting goals during times when the user feels negative emotions reduces the risk of the user failing to achieve their goals.

[0062] The lifestyle habit analysis unit can analyze the impact of seasonal and weather changes on behavioral patterns based on the user's lifestyle habit data. The lifestyle habit analysis unit, for example, integrates the user's lifestyle habit data with weather data and analyzes the impact of seasonal and weather changes on behavioral patterns. For example, the lifestyle habit analysis unit analyzes a tendency to go out less on rainy days and suggests indoor activities. The lifestyle habit analysis unit can also analyze changes in behavioral patterns with seasonal changes and set goals according to the season. The lifestyle habit analysis unit can also analyze changes in behavioral patterns with weather changes and set goals according to the weather. For example, the lifestyle habit analysis unit analyzes a tendency to go out less in winter and sets indoor exercise goals. The lifestyle habit analysis unit analyzes a tendency to go out more in summer and sets outdoor activity goals. In this way, analyzing the impact of seasonal and weather changes on behavioral patterns enables more appropriate goal setting. For example, setting indoor activity goals taking into account a tendency to go out less on rainy days makes it easier for the user to achieve their goals. Setting goals according to seasonal changes allows the user to enjoy activities that match the season.

[0063] The lifestyle habit analysis unit can compare the user's past and current behavioral patterns to detect changes in behavior and identify the causes. The lifestyle habit analysis unit, for example, compares the user's past and current behavioral patterns to detect changes in behavior. For example, the lifestyle habit analysis unit can analyze changes in exercise habits based on data from the past year and identify the causes. The lifestyle habit analysis unit can also compare the user's past and current behavioral patterns to detect changes in eating habits. The lifestyle habit analysis unit can also detect changes in behavior and identify the causes, thereby making suggestions for the user to improve their behavior. For example, the lifestyle habit analysis unit can detect changes in exercise habits and identify the causes as stress. The lifestyle habit analysis unit can detect changes in eating habits and identify the causes as being busy at work. By comparing past and current behavioral patterns and detecting changes in behavior, more appropriate goal setting becomes possible. For example, if a change in exercise habits is detected and the cause is stress, a goal to reduce stress can be set. It can also detect changes in eating habits and, if the cause is a busy work schedule, set goals for eating healthy meals during your free time.

[0064] When analyzing the user's lifestyle habits and behavioral patterns, the lifestyle habit analysis unit can also integrate data on family and friends and analyze their mutual influences. For example, the lifestyle habit analysis unit integrates the user's lifestyle habit data with data on family and friends and analyzes their mutual influences. For example, the lifestyle habit analysis unit analyzes the eating patterns of all family members and suggests healthy eating habits. The lifestyle habit analysis unit can also analyze interaction data with friends and set goals to strengthen relationships with friends. The lifestyle habit analysis unit can also integrate data on family and friends and analyze the influence on the user's behavioral patterns. For example, the lifestyle habit analysis unit analyzes the exercise habits of all family members and sets exercise goals for the whole family to work on. The lifestyle habit analysis unit analyzes interaction data with friends and suggests activities to strengthen relationships with friends. In this way, integrating data on family and friends and analyzing their mutual influences enables more appropriate goal setting. For example, analyzing the eating patterns of all family members and suggesting healthy eating habits allows the whole family to live a healthy lifestyle. Analyzing interaction data with friends and setting goals to strengthen relationships with friends allows the user to deepen their relationships with friends.

[0065] The lifestyle habit analysis unit can also utilize data from smart home devices and wearable devices to analyze the user's behavioral patterns. For example, the lifestyle habit analysis unit utilizes data from smart home devices to analyze the user's behavioral patterns. For example, the lifestyle habit analysis unit analyzes the user's sleep patterns based on data from smart lighting and temperature sensors. The lifestyle habit analysis unit can also analyze the user's exercise patterns using data from wearable devices. The lifestyle habit analysis unit can also integrate data from smart home devices and wearable devices to analyze the user's behavioral patterns in detail. For example, the lifestyle habit analysis unit analyzes the user's indoor activity patterns based on data from smart home devices. The lifestyle habit analysis unit also analyzes the user's exercise volume based on data from wearable devices. This enables more accurate behavioral pattern analysis by utilizing data from smart home devices and wearable devices. For example, analyzing the user's sleep patterns based on data from smart lighting and temperature sensors can set goals for the user to get better sleep. In addition, by analyzing the user's exercise patterns based on data from the wearable device, the user can set goals for living a healthy lifestyle.

[0066] The goal setting unit can learn the most effective goal setting pattern from the user's past behavioral history and propose individually optimized goals. The goal setting unit, for example, analyzes the user's past behavioral history to learn the most effective goal setting pattern. For example, the goal setting unit proposes individually optimized goals based on patterns of goal setting that were successful in the past. The goal setting unit can also analyze patterns of goal setting that were unsuccessful in the past and propose goals to avoid the same failures. The goal setting unit can also set goals that are easy for the user to achieve based on the user's behavioral history. For example, the goal setting unit proposes goals that are easy for the user to achieve based on patterns of goal setting that were successful in the past. The goal setting unit sets goals that are easy for the user to achieve based on patterns of goal setting that were unsuccessful in the past. In this way, by learning from the past behavioral history, it is possible to propose optimal goals for the user. For example, by proposing goals that are easy for the user to achieve based on patterns of goal setting that were successful in the past, the probability that the user will achieve their goal is increased. Furthermore, by setting goals that are easy for the user to achieve based on patterns of goal setting that were unsuccessful in the past, the risk that the user will not achieve their goal can be reduced.

[0067] The goal setting unit can send reminders for achieving goals at optimal timings in accordance with the user's lifestyle rhythm. The goal setting unit, for example, analyzes the user's lifestyle rhythm and builds a system that sends reminders for achieving goals at optimal timings. For example, the goal setting unit sends reminders during times when the user is most active. The goal setting unit can also send reminders during times when the user is relaxing. The goal setting unit can also customize the content of reminders in accordance with the user's lifestyle rhythm. For example, the goal setting unit sends a reminder for an exercise goal during times when the user is most active. The goal setting unit also sends a reminder for a relaxation goal during times when the user is relaxing. In this way, sending reminders in accordance with the user's lifestyle rhythm increases the probability of goal achievement. For example, sending a reminder during times when the user is most active makes it easier for the user to achieve their goal. Also, sending a reminder during times when the user is relaxing allows the user to achieve their goal without feeling stressed.

[0068] The goal setting unit can monitor the degree of goal achievement in real time and dynamically adjust the goal according to the degree of achievement. The goal setting unit, for example, builds a system that monitors the degree of goal achievement in real time and dynamically adjusts the goal according to the degree of achievement. For example, the goal setting unit makes the goal easier when the degree of goal achievement is low. Furthermore, the goal setting unit can make the goal more difficult when the degree of goal achievement is high. Furthermore, the goal setting unit can dynamically adjust the goal based on user feedback. For example, the goal setting unit makes the goal more difficult when the user feels that the goal is easy to achieve. Furthermore, the goal setting unit makes the goal easier when the user feels that the goal is difficult to achieve. In this way, by monitoring the degree of goal achievement in real time and dynamically adjusting the goal, it becomes easier to maintain the user's motivation. For example, when the degree of goal achievement is low, making the goal easier makes it easier for the user to achieve the goal. Furthermore, when the degree of goal achievement is high, making the goal more difficult can motivate the user to take on challenges.

[0069] The goal setting unit can set a joint goal with family and friends and share the degree of achievement, thereby increasing mutual motivation. The goal setting unit, for example, builds a system for setting a joint goal with family and friends and sharing the degree of achievement. For example, the goal setting unit sets a fitness challenge for the whole family to work on together and shares the degree of achievement. The goal setting unit can also set a joint goal with friends and share the degree of achievement. The goal setting unit can also strengthen bonds with family and friends by sharing the degree of achievement. For example, the goal setting unit can set a fitness challenge for the whole family to work on together and share the degree of achievement, thereby allowing the whole family to live a healthy lifestyle. The goal setting unit can also strengthen relationships with friends by setting a joint goal with friends and sharing the degree of achievement. In this way, by setting a joint goal and sharing the degree of achievement, bonds with family and friends can be strengthened and mutual motivation can be increased. For example, by setting a fitness challenge for the whole family to work on together and sharing the degree of achievement, the whole family can live a healthy lifestyle. The goal setting unit can also strengthen relationships with friends by setting a joint goal with friends and sharing the degree of achievement.

[0070] The goal setting unit can provide guides and tutorials for performing the activity suggested by the generation AI as support for achieving the goal. The goal setting unit, for example, builds a system that provides guides and tutorials for performing the activity suggested by the generation AI. For example, the goal setting unit provides a video on how to perform a fitness activity. The goal setting unit can also provide a text guide for performing the activity suggested by the generation AI. The goal setting unit can also provide a tutorial for performing the activity suggested by the generation AI. For example, the goal setting unit can provide a video on how to perform a fitness activity to enable the user to perform the activity correctly. The goal setting unit can also provide a text guide for performing the activity suggested by the generation AI to make it easier for the user to understand the activity. In this way, by providing guides and tutorials, the user can effectively perform the activity to achieve the goal. For example, by providing a video on how to perform a fitness activity, the user can exercise with the correct form. Furthermore, by providing a text guide for performing the activity suggested by the generation AI, it is easier for the user to understand the purpose and method of the activity.

[0071] The goal setting unit can use the emotion estimation function to analyze the user's emotion when the goal is achieved and provide feedback to elicit positive emotions. The goal setting unit can, for example, use the emotion estimation function to analyze the user's emotion when the goal is achieved in real time and provide feedback to elicit positive emotions. For example, the goal setting unit can send a message that makes the user feel happy when the goal is achieved. The goal setting unit can also provide feedback that makes the user feel a sense of accomplishment when the goal is achieved. The goal setting unit can also analyze the user's emotion and provide feedback that makes the user feel positive emotions. For example, the goal setting unit can send a message that makes the user feel happy when the user achieves the goal. The goal setting unit can also provide feedback that makes the user feel a sense of accomplishment when the user achieves the goal. In this way, by using the emotion estimation function, positive emotions when the goal is achieved can be elicited and the user's motivation can be increased. For example, by sending a message that makes the user feel happy when the goal is achieved, the user can feel the joy of achieving the goal. Furthermore, by providing feedback that makes the user feel a sense of accomplishment when the goal is achieved, the user can be motivated to achieve the goal.

[0072] The goal setting unit can ensure consistency with the user's current living situation and goals when making activity suggestions based on past regrets. The goal setting unit, for example, analyzes the user's current living situation and goals and makes activity suggestions based on past regrets. For example, the goal setting unit makes activity suggestions that match the user's current lifestyle rhythm, learning from regrets. The goal setting unit can also make activity suggestions that match the user's current goals. The goal setting unit can also make suggestions that are easy for the user to carry out by ensuring consistency with the user's current living situation and goals. For example, the goal setting unit makes activity suggestions that match the user's current lifestyle rhythm, allowing the user to carry out the suggestions without difficulty. The goal setting unit also makes activity suggestions that match the user's current goals, making it easier for the user to achieve the goals. In this way, by ensuring consistency between the activity suggestions based on past regrets and the current living situation and goals, it is possible to make suggestions that are easy for the user to carry out. For example, by making activity suggestions that match the user's current lifestyle rhythm, the user can carry out the suggestions without difficulty. Furthermore, by making activity suggestions that match the user's current goals, the user can easily achieve the goals.

[0073] The goal setting unit can provide a step-by-step guide for the user to overcome past regrets and monitor progress. The goal setting unit, for example, builds a system that provides a step-by-step guide for the user to overcome past regrets. For example, the goal setting unit presents a specific action plan and monitors progress. The goal setting unit can also support the user in overcoming regrets by following the step-by-step guide. The goal setting unit can also monitor progress and check whether the user is progressing as planned. For example, the goal setting unit monitors whether the user is acting according to the specific action plan. The goal setting unit can also check whether the user is progressing as planned and provide feedback as needed. In this way, providing a step-by-step guide and monitoring progress makes it easier for the user to overcome past regrets. For example, presenting a specific action plan allows the user to clearly understand what they should do. Monitoring progress can also check whether the user is progressing as planned and provide support as needed.

[0074] The goal setting unit can anonymize data related to a user's past regrets and integrate it with data from other users to extract common lessons. The goal setting unit, for example, builds a system that anonymizes data related to a user's past regrets and integrates it with data from other users to extract common lessons. For example, the goal setting unit analyzes data from multiple users and suggests common lessons. By anonymizing the user's data, the goal setting unit can utilize the data while protecting privacy. By integrating the data with data from other users, the goal setting unit can extract lessons based on a larger amount of data. For example, the goal setting unit analyzes data from multiple users and suggests common lessons, thereby supporting the user in preventing the user from repeating the same mistake. By anonymizing the user's data, the goal setting unit can utilize the data while protecting privacy. This makes it easier to extract common lessons by anonymizing the data and integrating it with data from other users. For example, by analyzing data from multiple users and suggesting common lessons, the user is prevented from repeating the same mistake. By anonymizing the user's data, the data can be utilized while protecting privacy.

[0075] The goal setting unit can customize activity suggestions based on past regrets to match the user's hobbies and interests. The goal setting unit, for example, analyzes the user's hobbies and interests and builds a system that customizes activity suggestions based on past regrets. For example, the goal setting unit suggests activities related to hobbies and learns from regrets. The goal setting unit can also make activity suggestions based on the user's interests. The goal setting unit can also make suggestions that are easy for the user to carry out by customizing the activity suggestions to match the user's hobbies and interests. For example, the goal setting unit suggests activities related to hobbies and learns from regrets while enjoying the activity. The goal setting unit also makes activity suggestions based on the user's interests, allowing the user to carry out the activity with interest. In this way, customizing the activity suggestions to match the hobbies and interests enables suggestions that are easy for the user to carry out. For example, by suggesting activities related to hobbies, the user can learn from regrets while enjoying the activity. The activity suggestions based on interests allow the user to carry out the activity with interest.

[0076] The goal setting unit can use the emotion estimation function to analyze the user's emotions regarding past regrets and provide emotional support. The goal setting unit, for example, uses the emotion estimation function to analyze the user's emotions regarding past regrets in real time and build a system that provides emotional support. For example, the goal setting unit sends an encouraging message when the user feels regret. The goal setting unit can also provide support for the user to become emotionally stable. The goal setting unit can also analyze the user's emotions and provide feedback for the user to become emotionally stable. For example, the goal setting unit sends an encouraging message when the user feels regret. The goal setting unit also provides support for the user to become emotionally stable. In this way, by using the emotion estimation function, emotional support for past regrets is provided and the user is helped to become emotionally stable. For example, sending an encouraging message when the user feels regret helps the user to become emotionally stable. Furthermore, providing support for the user to become emotionally stable makes it easier for the user to overcome past regrets.

[0077] The goal setting unit can monitor the user's current living situation in real time and suggest specific actions to cherish the moments. The goal setting unit, for example, builds a system that monitors the user's current living situation in real time and suggests specific actions to cherish the moments. For example, the goal setting unit suggests meditation during a time when the user can relax. The goal setting unit can also suggest actions to help the user feel grateful. The goal setting unit can monitor the user's living situation in real time and suggest actions to help the user cherish the moments. For example, the goal setting unit suggests meditation during a time when the user can relax. The goal setting unit can also suggest actions to help the user feel grateful. In this way, by monitoring the living situation in real time and suggesting specific actions, the user can be supported in cherishing the present moment. For example, by suggesting meditation during a time when the user can relax, the user can have time to relax. Furthermore, by suggesting actions to help the user feel grateful, the user can feel grateful.

[0078] The goal setting unit enables the generation AI to provide feedback in real time when the user performs an activity to cherish the present moment. The goal setting unit, for example, builds a system in which the generation AI provides feedback in real time when the user performs an activity to cherish the present moment. For example, the goal setting unit monitors the level of relaxation during meditation and provides feedback. The goal setting unit can also provide feedback to increase gratitude when the user keeps a gratitude diary. The goal setting unit can also provide feedback in real time when the user performs an activity to cherish the present moment. For example, the goal setting unit monitors the level of relaxation during meditation and provides feedback. The goal setting unit can also provide feedback to increase gratitude when the user keeps a gratitude diary. As a result, by providing feedback in real time, the user can effectively perform an activity to cherish the present moment. For example, monitoring the level of relaxation during meditation and providing feedback allows the user to have time to relax. Furthermore, providing feedback to increase gratitude when the user keeps a gratitude diary allows the user to feel grateful.

[0079] The goal setting unit enables the user to share with family and friends an activity for cherishing the present moment and perform it together. The goal setting unit, for example, builds a system for sharing with family and friends an activity for cherishing the present moment and performing it together. For example, the goal setting unit keeps a gratitude diary with the whole family. The goal setting unit can also meditate with friends. The goal setting unit can also support the user in cherishing the present moment by sharing an activity with family and friends and performing it together. For example, the goal setting unit enables the whole family to feel grateful by keeping a gratitude diary with the whole family. The goal setting unit can also support the user in cherishing the present moment by meditating with friends. In this way, the goal setting unit can support the user in cherishing the present moment by sharing and performing an activity with family and friends. For example, the goal setting unit enables the whole family to feel grateful by keeping a gratitude diary with the whole family. In addition, the goal setting unit can support the user in cherishing the present moment by sharing and performing an activity with family and friends.

[0080] The goal setting unit can customize activities for cherishing the present moment to match the user's lifestyle rhythm. The goal setting unit, for example, analyzes the user's lifestyle rhythm and builds a system that customizes activities for cherishing the present moment. For example, the goal setting unit suggests meditation for a time when the user can relax most. The goal setting unit can also suggest activities that will help the user feel grateful. The goal setting unit can also make suggestions that are easy for the user to carry out by customizing activities to match the user's lifestyle rhythm. For example, the goal setting unit can suggest meditation for a time when the user can relax most, so that the user has time to relax. The goal setting unit can also suggest activities that will help the user feel grateful, so that the user has gratitude. In this way, customizing activities to match the user's lifestyle rhythm can support the user in cherishing the present moment. For example, suggesting meditation for a time when the user can relax most allows the user to have time to relax. Also, suggesting activities that will help the user feel grateful allows the user to feel grateful.

[0081] The goal setting unit can use the emotion estimation function to analyze the emotions the user feels when cherishing the present moment and make suggestions to elicit positive emotions. The goal setting unit, for example, uses the emotion estimation function to build a system that analyzes the emotions the user feels when cherishing the present moment in real time and makes suggestions to elicit positive emotions. For example, the goal setting unit can suggest activities that allow the user to relax. The goal setting unit can also suggest activities that will make the user feel grateful. The goal setting unit can also analyze the user's emotions and make suggestions to help the user feel positive emotions. For example, the goal setting unit can suggest activities that will allow the user to relax. The goal setting unit can also suggest activities that will make the user feel grateful. In this way, the emotion estimation function can elicit positive emotions when the user cherishes the present moment and support mental health. For example, suggesting activities that will allow the user to relax allows the user to have time to relax. Also, suggesting activities that will make the user feel grateful allows the user to feel grateful.

[0082] The goal setting unit can analyze in detail the user's hopes and goals for the future and, based on the analysis, suggest specific steps for building relationships. The goal setting unit, for example, builds a system that analyzes in detail the user's hopes and goals for the future and, based on the analysis, suggests specific steps for building relationships. For example, the goal setting unit can suggest keeping in touch with friends on a regular basis. The goal setting unit can also suggest building new relationships through a new hobby. The goal setting unit can also suggest specific steps for the user to build fulfilling relationships based on the user's hopes and goals. For example, the goal setting unit can suggest keeping in touch with friends on a regular basis. The goal setting unit can also suggest building new relationships through a new hobby. In this way, by analyzing in detail the hopes and goals for the future and, based on the analysis, suggesting specific steps for building relationships, the system can support the user in building fulfilling relationships. For example, by suggesting keeping in touch with friends on a regular basis, the user can maintain their relationships with friends. By suggesting building new relationships through a new hobby, the system can support the user in building fulfilling relationships.

[0083] The goal setting unit allows the generation AI to provide real-time support when the user performs an activity to build new relationships. The goal setting unit, for example, builds a system in which the generation AI provides real-time support when the user performs an activity to build new relationships. For example, the goal setting unit provides information necessary when starting a new hobby. The goal setting unit can also cause the generation AI to provide real-time advice when the user performs an activity to build new relationships. The goal setting unit can also cause the generation AI to provide real-time support when the user performs an activity to build new relationships, thereby enabling the user to effectively perform the activity. For example, the goal setting unit provides information necessary when starting a new hobby. The goal setting unit can also cause the generation AI to provide real-time advice when the user performs an activity to build new relationships. In this way, by providing real-time support, the user can effectively perform the activity to build new relationships. For example, by providing information necessary when starting a new hobby, it becomes easier for the user to start a new hobby. The generation AI can also provide advice in real time, allowing the user to effectively perform the activity to build new relationships.

[0084] The goal setting unit can analyze the user's past interpersonal data, extract successful patterns, and utilize them in future suggestions. The goal setting unit, for example, builds a system that analyzes the user's past interpersonal data, extracts successful patterns, and utilizes them in future suggestions. For example, the goal setting unit suggests methods for building interpersonal relationships that were successful in the past. The goal setting unit can also analyze interpersonal relationship building methods that failed in the past and make suggestions to avoid the same failures. The goal setting unit can also make suggestions for the user to build fulfilling interpersonal relationships based on the user's past interpersonal data. For example, the goal setting unit suggests methods for building interpersonal relationships that were successful in the past. The goal setting unit can also analyze interpersonal relationship building methods that failed in the past and make suggestions to avoid the same failures. In this way, by analyzing the past interpersonal relationship data, extracting successful patterns, and utilizing them in future suggestions, the user can be supported in building fulfilling interpersonal relationships. For example, by suggesting interpersonal relationship building methods that were successful in the past, the user's chances of success using the same methods increase. Furthermore, by analyzing interpersonal relationship building methods that failed in the past and making suggestions to avoid the same failures, the user's risk of failure can be reduced.

[0085] The goal setting unit can suggest activities from the perspective of different cultures or regions that will enable the user to build fulfilling human relationships for the future. The goal setting unit, for example, constructs a system that suggests activities from the perspective of different cultures or regions that will enable the user to build fulfilling human relationships for the future. For example, the goal setting unit suggests participation in an intercultural exchange event. The goal setting unit can also suggest activities that incorporate local customs. The goal setting unit can also support the user in building diverse human relationships by performing activities from the perspective of different cultures or regions. For example, by suggesting participation in an intercultural exchange event, the user can have an opportunity to interact with people from different cultures. The goal setting unit can also support the user in building diverse human relationships by suggesting activities from the perspective of different cultures or regions. For example, by suggesting participation in an intercultural exchange event, the user can have an opportunity to interact with people from different cultures. Furthermore, by suggesting activities that incorporate local customs, the user can have an opportunity to interact with people in the local area. In this way, by suggesting activities from the perspective of different cultures or regions, the system can support the user in building diverse human relationships. For example, by suggesting participation in an intercultural exchange event, the user can have an opportunity to interact with people from different cultures. Furthermore, by suggesting activities that incorporate local customs, the user can have an opportunity to interact with people in the local area.

[0086] The goal setting unit can use the emotion estimation function to analyze the emotions the user feels when building fulfilling relationships for the future and make suggestions to bring out positive emotions. The goal setting unit, for example, uses the emotion estimation function to build a system that analyzes the emotions the user feels when building fulfilling relationships for the future in real time and makes suggestions to bring out positive emotions. For example, the goal setting unit can suggest activities that allow the user to relax. The goal setting unit can also suggest activities that will make the user feel grateful. The goal setting unit can also analyze the user's emotions and make suggestions to help the user feel positive emotions. For example, the goal setting unit can suggest activities that will allow the user to relax. The goal setting unit can also suggest activities that will make the user feel grateful. In this way, the emotion estimation function can bring out positive emotions when building fulfilling relationships for the future and support mental health. For example, suggesting activities that will allow the user to relax allows the user to have time to relax. Also, suggesting activities that will make the user feel grateful allows the user to feel grateful.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] CherishTime AI can also be equipped with a health management unit that monitors the user's health. The health management unit collects the user's health data and analyzes their health condition. For example, the health management unit monitors the user's heart rate and blood pressure and sends an alert if an abnormality is detected. The health management unit can also analyze the user's diet and exercise habits and provide advice on living a healthy lifestyle. This allows the user to constantly monitor their health condition and take appropriate measures. For example, if the user's heart rate is high, the unit can suggest relaxing activities. Also, if the user's diet is unbalanced, the unit can suggest nutritionally balanced meals.

[0089] The goal setting unit can estimate the user's emotions and set a goal for the user to feel positive emotions based on the estimated emotions. For example, if the user is feeling stressed, a goal for relaxation can be set. Also, if the user is feeling joyful, a goal for maintaining that emotion can be set. This makes it easier for the user to maintain positive emotions by achieving goals according to their emotions. For example, if the user is feeling stressed, a goal for meditation or deep breathing can be set. Also, if the user is feeling joyful, a goal for spending time with friends or family to share those emotions can be set.

[0090] CherishTime AI can also be equipped with a hobby analysis unit that delves deeper into the user's hobbies and interests. The hobby analysis unit collects and analyzes data related to the user's hobbies and interests. For example, the hobby analysis unit can identify the user's hobbies and suggest activities related to those hobbies. The hobby analysis unit can also support the user in finding new hobbies. This allows the user to deepen their hobbies and interests and spend their time more fulfillingly. For example, if the user is interested in music, music events and instrument lessons can be suggested. Also, if the user is interested in outdoor activities, hiking and camping activities can be suggested.

[0091] The goal setting unit can estimate the user's emotions and set a goal for the user to overcome negative emotions based on the estimated emotions. For example, if the user is feeling sad, a goal for alleviating those emotions can be set. Also, if the user is feeling angry, a goal for controlling those emotions can be set. This makes it easier for the user to overcome negative emotions. For example, if the user is feeling sad, an activity to help the user feel grateful can be suggested. Also, if the user is feeling angry, a goal for deep breathing or meditation to relax can be set.

[0092] CherishTime AI can also be equipped with a learning support unit to increase the user's motivation to learn. The learning support unit collects the user's learning data and provides advice to increase their motivation to learn. For example, the learning support unit can analyze the user's learning style and suggest a learning method that suits that style. The learning support unit can also support the user in achieving their learning goals. This allows the user to study effectively. For example, if the user has a visual learning style, it can suggest a learning method that uses visual aids. Also, if the user has an auditory learning style, it can suggest a learning method that uses audio materials.

[0093] The goal setting unit can estimate the user's emotions and set a goal for the user to achieve emotional stability based on the estimated emotions. For example, if the user is feeling anxious, a goal can be set to alleviate the emotion. Also, if the user is feeling lonely, a goal can be set to reduce the emotion. This makes it easier for the user to achieve emotional stability. For example, if the user is feeling anxious, a goal can be set to meditate or yoga to relax. Also, if the user is feeling lonely, a goal can be set to contact friends and family.

[0094] CherishTime AI can also be equipped with a creativity support unit to bring out the user's creativity. The creativity support unit supports the user's creative activities and provides advice to enhance their creativity. For example, the creativity support unit can analyze the creative activities the user is engaged in and make suggestions for further developing those activities. The creativity support unit can also support the user in finding new creative ideas, allowing the user to maximize their creativity. For example, if the user is interested in painting, the unit can suggest new techniques and styles. Or, if the user is interested in writing, the unit can provide creative hints and inspiration.

[0095] The goal setting unit can estimate the user's emotions and set a goal for the user to be emotionally fulfilled based on the estimated emotions. For example, if the user feels satisfied, a goal can be set to maintain that emotion. Also, if the user feels grateful, a goal can be set to deepen that emotion. This makes it easier for the user to be emotionally fulfilled. For example, if the user feels satisfied, a goal can be set to keep a diary to share those emotions. Also, if the user feels grateful, a goal can be set to write a thank-you letter to express those emotions.

[0096] CherishTime AI can further include a social skills support unit to improve the user's social skills. The social skills support unit analyzes the user's social skills and provides advice to improve them. For example, the social skills support unit can analyze the user's communication style and suggest a communication method that suits that style. The social skills support unit can also support the user in building relationships. This allows the user to effectively improve their social skills. For example, if the user has an introverted communication style, the social skills support unit can suggest ways to express themselves. Also, if the user has an extroverted communication style, the social skills support unit can suggest ways to deepen relationships with others.

[0097] The goal setting unit can estimate the user's emotions and set goals for the user to grow emotionally based on the estimated emotions. For example, if the user wants to increase self-esteem, a goal can be set to foster that emotion. Also, if the user wants to increase empathy, a goal can be set to foster that emotion. This makes it easier for the user to grow emotionally. For example, if the user wants to increase self-esteem, a goal can be set to make affirmations to foster self-esteem. Also, if the user wants to increase empathy, a goal can be set to improve listening skills to be more empathetic to the emotions of others.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The lifestyle analysis unit analyzes the user's lifestyle and behavioral patterns. For example, it analyzes what time of day the user is active, and what hobbies and interests the user has. The lifestyle analysis unit performs its analysis using data provided by the user and sensor information from the smartphone. This makes it possible to identify the user's active time periods, hobbies, and interests. Step 2: The goal setting unit sets small daily goals based on the user's behavioral patterns analyzed by the lifestyle analysis unit. For example, goals such as "expressing gratitude to family once a day" or "making time to spend with friends on the weekend" are set. The goal setting unit sets goals that are easy for the user to achieve based on the user's activity times, hobbies, and interests.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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]

[0167] 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 lifestyle analysis unit that analyzes the lifestyle and behavior patterns of a user; a goal setting unit that sets small daily goals based on the behavioral patterns of the user analyzed by the lifestyle habit analysis unit. A system characterized by:

2. The lifestyle habit analysis unit When analyzing the user's lifestyle habits and behavioral patterns, emotion analysis is performed and the behavioral patterns are predicted based on emotional fluctuations.

2. The system of claim 1.

3. The lifestyle habit analysis unit When analyzing the user's lifestyle and behavioral patterns, data on family and friends is also integrated to analyze their mutual influence.

2. The system of claim 1.

4. The goal setting unit Learn the most effective goal setting patterns from the user's past behavioral history and propose individually optimized goals 2. The system of claim 1.

5. The goal setting unit Analyze data on the user's past regrets, extract common patterns, and organize lessons learned.

2. The system of claim 1.

6. The goal setting unit Monitor the user's current living situation in real time and suggest specific actions to cherish the moment.

2. The system of claim 1.

7. The goal setting unit The user's hopes and goals for the future are analyzed in detail, and specific steps for building relationships are proposed based on the analysis.

2. The system of claim 1.

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