system

Abstract

The system according to this embodiment aims to allow users to view past, present, and future events at a glance and to provide dialogue scenarios tailored to specific events. [Solution] The system according to the embodiment comprises a timeline view unit, a diary unit, a future prediction scenario unit, a special event scenario unit, and an emotion analysis and feedback unit. The timeline view unit allows users to view past, present, and future events at a glance. The diary unit records daily events. The future prediction scenario unit generates future scenarios based on the events recorded by the diary unit. The special event scenario unit provides special dialogue scenarios tailored to specific events. The emotion analysis and feedback unit analyzes the interaction with the user, understands their emotions, and provides appropriate feedback.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully achieved to view past, present, and future events at a glance and provide a dialogue scenario tailored to a special event, and there is room for improvement.

[0005] The system according to the embodiment aims to view past, present, and future events at a glance and provide a dialogue scenario tailored to a special event.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a timeline view unit, a diary unit, a future prediction scenario unit, a special event scenario unit, and an emotion analysis and feedback unit. The timeline view unit allows users to view past, present, and future events at a glance. The diary unit records daily events. The future prediction scenario unit generates future scenarios based on the events recorded by the diary unit. The special event scenario unit provides special dialogue scenarios tailored to specific events. The emotion analysis and feedback unit analyzes interactions with the user, understands their emotions, and provides appropriate feedback. [Effects of the Invention]

[0007] The system according to this embodiment can provide a quick overview of past, present, and future events and offer dialogue scenarios tailored to specific events. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Time Travel Diary System according to an embodiment of the present invention is an application that allows users to record daily events while interacting with their past and future selves. This Time Travel Diary System includes a timeline view function that allows users to see past, present, and future events at a glance, an avatar customization function, a diary function for recording daily events, a future prediction scenario function that generates future scenarios based on interactions with the user, a special dialogue scenario function tailored to special events such as birthdays and anniversaries, an emotion analysis and feedback function in which a generating AI analyzes interactions with the user, understands emotions, and provides appropriate feedback, and a self-growth visualization function that allows users to visually confirm their self-growth by viewing past, present, and future events in a timeline view. As a result, users can feel their own growth, have hope for the future, and organize their daily emotions. For example, the Time Travel Diary System allows users to visually confirm their own growth by looking back on past events and predicting future events. The Time Travel Diary System also allows users to enjoy special dialogue scenarios tailored to special events and anniversaries. Furthermore, the Time Travel Diary System can understand the user's emotions and support them in recording daily events by using a generating AI to analyze the user's emotions and provide appropriate feedback. This allows users to experience personal growth, have hope for the future, and organize their daily emotions. The time travel diary system enables users to see past, present, and future events at a glance, record daily events, generate future scenarios, provide dialogue scenarios tailored to specific events, and understand and provide feedback on their emotions.

[0029] The time travel diary system according to this embodiment comprises a timeline view unit, a diary unit, a future prediction scenario unit, a special event scenario unit, and an emotion analysis and feedback unit. The timeline view unit allows users to view past, present, and future events at a glance. The timeline view unit displays events in, for example, a graphical timeline or list format. The timeline view unit also provides a visual interface for users to review past events and predict future events. For example, the timeline view unit can display past events in different colors and highlight important events. The diary unit allows users to record daily events. The diary unit records events by methods such as text input, voice input, and image attachment. The diary unit also provides a tagging function to make it easier for users to search for recorded events. For example, the diary unit can automatically tag events recorded by the user. The future prediction scenario unit can generate future scenarios based on events recorded by the diary unit. The future prediction scenario unit generates future scenarios based on interaction with the user using a generation AI. For example, the future prediction scenario unit generates scenarios that predict future events based on goals and plans entered by the user. The special event scenario unit can provide special dialogue scenarios tailored to special events. The special event scenario unit generates dialogue scenarios with the user tailored to special events such as birthdays and anniversaries. For example, the special event scenario unit provides a scenario that displays a special message on the user's birthday. The sentiment analysis and feedback unit can analyze the interaction with the user, understand their emotions, and provide appropriate feedback. The sentiment analysis and feedback unit uses generative AI to analyze the user's emotions and provide appropriate feedback. For example, the sentiment analysis and feedback unit analyzes the text and voice entered by the user to estimate the user's emotions.As a result, the time travel diary system according to this embodiment allows the user to see past, present, and future events at a glance, record daily events, generate future scenarios, provide dialogue scenarios tailored to special events, and understand and provide feedback on emotions.

[0030] The Timeline View section allows users to see past, present, and future events at a glance. It displays events in various formats, such as a graphical timeline or list. Specifically, it provides a visual interface for users to review past events and predict future events. For example, it can color-code past events to highlight important ones. This allows users to easily review past events and use them as a reference when planning for the future. Furthermore, the Timeline View section allows users to select specific periods or events to view details. For example, clicking on a specific date displays details of events recorded on that day. The Timeline View also allows users to add comments and notes to past events. This allows users to record their thoughts and feelings about past events for later reflection. Additionally, the Timeline View section functions as a tool for users to predict future events. For example, if a user enters future plans or goals, predicted events based on those plans are displayed on the timeline. This allows users to visually review their future plans and adjust them as needed.

[0031] The diary section allows users to record daily events. The diary section allows users to record events using methods such as text input, voice input, and image attachment. Specifically, users can easily record daily events using their smartphones or computers. For example, if a user enters the day's events as text, the diary section saves the text for later searching. When using voice input, users can record events simply by speaking into the microphone. Furthermore, the image attachment function allows users to add photos and images related to the day's events. This allows users to create visual records that can be helpful when looking back later. The diary section provides a tagging function to make it easier to search for recorded events. For example, the diary section can automatically tag events recorded by the user. This allows users to easily search for records related to specific events or themes. Additionally, the diary section allows users to manually add tags, enabling more detailed classification. This allows users to efficiently record daily events and easily search and review them later.

[0032] The Future Prediction Scenario Unit can generate future scenarios based on events recorded by the Diary Unit. The Future Prediction Scenario Unit uses a generation AI to generate future scenarios based on interaction with the user. Specifically, it generates scenarios that predict future events based on the goals and plans entered by the user. For example, if the user enters "My goal for next year is to complete a marathon," the Future Prediction Scenario Unit will generate a scenario that predicts training schedules and progress based on that goal. The generation AI utilizes past data and statistical information to propose the optimal scenario for achieving the user's goal. Furthermore, the Future Prediction Scenario Unit can generate multiple scenarios based on the information entered by the user, providing the user with choices. For example, if the user enters "I want to travel next summer," the Future Prediction Scenario Unit will generate and propose multiple travel plans. This allows the user to select the optimal future scenario based on their goals and plans. Additionally, the Future Prediction Scenario Unit can modify scenarios based on user feedback to provide more accurate predictions. This allows users to predict future events and use them to make plans.

[0033] The Special Event Scenario Unit can provide special dialogue scenarios tailored to specific events. For example, it generates dialogue scenarios with users to coincide with special events such as birthdays and anniversaries. Specifically, it provides a scenario that displays a special message on the user's birthday. For example, when a user celebrates their birthday, the Special Event Scenario Unit uses its AI generation capabilities to display a message such as, "Happy Birthday! I hope you have a wonderful year!" The Special Event Scenario Unit can also suggest special messages and surprise events when a user celebrates an anniversary. For example, when a user celebrates their wedding anniversary, the Special Event Scenario Unit displays a message such as, "Happy Wedding Anniversary! How about planning a special dinner tonight?" Furthermore, the Special Event Scenario Unit can provide helpful information and advice as users prepare for specific events. For example, when a user is graduating, the Special Event Scenario Unit displays a message such as, "Congratulations on your graduation! How are your graduation preparations going? Let's make a list of anything you need." In this way, the Special Event Scenario Unit provides users with dialogue scenarios tailored to special events, supporting them in enjoying their special day even more.

[0034] The sentiment analysis and feedback unit can analyze user interactions, understand emotions, and provide appropriate feedback. Using generative AI, the unit analyzes user emotions and provides appropriate feedback. Specifically, it analyzes text and audio input from the user to estimate their emotions. For example, if a user inputs "I'm very tired today," the sentiment analysis and feedback unit analyzes the text and understands that the user is tired. The generative AI then provides feedback such as, "You must be tired today. Please get some rest." Furthermore, the sentiment analysis and feedback unit can track changes in the user's emotions and analyze long-term emotional trends. This allows it to provide appropriate support in response to changes in the user's emotions. For example, if a user has recently been feeling stressed, the sentiment analysis and feedback unit might provide feedback such as, "It seems you've been feeling stressed lately. Why not try some ways to relax?" In addition, the sentiment analysis and feedback unit can suggest appropriate actions based on the user's emotions. For example, if a user inputs "I'm very happy today," the sentiment analysis and feedback unit might provide feedback such as, "That's wonderful! Cherish that feeling." This allows the emotion analysis and feedback unit to understand the user's emotions and provide appropriate feedback, thereby supporting the user's mental health.

[0035] The avatar customization section allows users to customize their avatars. It provides customization options such as changing appearance and selecting clothing. For example, users can change their avatar's hairstyle and clothing. The avatar customization section also allows users to customize their avatar's facial expressions and actions. For example, users can change their avatar's facial expression to a smile. This allows users to customize their avatars to their liking.

[0036] The self-growth visualization section allows users to visually confirm their personal growth. This section provides visualization methods such as graphs and progress bars. For example, the self-growth visualization section allows users to review past events and see their personal growth in a graph. It can also visually display the progress towards user-set goals. For example, it displays the progress towards user-set goals using a progress bar. This allows users to visually confirm their personal growth and maintain motivation.

[0037] The timeline view can dynamically change the display order of past events based on their importance. For example, it can prioritize important events and anniversaries so that users can quickly find them. It can also display events that users frequently refer to at the top for easier access. Furthermore, it can prioritize events that are highly relevant to the user's interests. This allows users to quickly find important events by prioritizing their display.

[0038] The timeline view can analyze the user's activity history and highlight the most relevant events. For example, it can highlight events related to places the user has frequently visited in the past. It can also highlight events related to activities the user performed during specific time periods. Furthermore, the timeline view can analyze the user's past behavior patterns and automatically highlight relevant events. This ensures that important information is not missed by highlighting relevant events based on the user's activity history.

[0039] The timeline view can highlight events in specific locations, taking into account the user's geographical location. For example, it can highlight past events related to the user's current location. It can also prioritize displaying events related to places the user frequently visits. Furthermore, if the user is traveling, it can highlight events related to places they have visited. This allows for the provision of highly relevant information by highlighting events related to the user's current location and frequently visited places.

[0040] The Timeline View section can analyze a user's social media activity and add relevant events to the timeline. For example, it can automatically add events that a user has shared on social media to the timeline. It can also analyze a user's social media posts and display relevant events on the timeline. Furthermore, it can add relevant events to the timeline based on the user's interactions with their social media friends. This allows for the provision of richer information by adding relevant events to the timeline based on social media activity.

[0041] The diary section can analyze the user's past diary entries and suggest the optimal recording method. For example, the diary section can prioritize suggesting recording methods that the user has frequently used in the past. It can also analyze the content of the user's past entries and suggest relevant recording methods. Furthermore, the diary section can analyze the user's recording patterns and automatically suggest the optimal recording method. This allows the system to suggest the most suitable recording method for the user by analyzing past entries.

[0042] The diary function can automatically tag entries based on the user's current activities. For example, it can automatically tag entries based on the user's current activities. It can also automatically tag relevant entries based on the user's current location. Furthermore, it can analyze the user's current emotional state and automatically tag appropriate entries. This automatic tagging based on current activities makes organizing entries easier.

[0043] The diary function can automatically record events at specific locations, taking into account the user's geographical location. For example, when the user arrives at a particular location, the diary function can automatically record events at that location. It can also automatically record events at places the user frequently visits. Furthermore, if the user is traveling, the diary function can automatically record events at places they visit. This improves the accuracy of the records by automatically recording events at specific locations.

[0044] The diary function can analyze a user's social media activity and automatically add relevant events to the diary. For example, it can automatically add events that a user shares on social media. It can also analyze a user's social media posts and add relevant events. Furthermore, it can add relevant events based on the user's interactions with their social media friends. This allows for the provision of richer information by adding relevant events based on social media activity.

[0045] The future prediction scenario unit can analyze the user's past behavior patterns and generate the most realistic future scenarios. For example, the future prediction scenario unit generates realistic future scenarios based on the user's past behavior patterns. Furthermore, the future prediction scenario unit can analyze the user's past choices and propose the most realistic future scenarios. In addition, the future prediction scenario unit can generate realistic future scenarios based on the user's past behavior history. This allows for the provision of highly reliable scenarios for the user by generating realistic future scenarios based on past behavior patterns.

[0046] The future prediction scenario unit can consider the user's current goals and plans when generating future prediction scenarios. For example, the future prediction scenario unit can generate future scenarios based on the user's current goals. It can also generate future scenarios considering the user's current plans. Furthermore, the future prediction scenario unit can generate realistic future scenarios based on the user's current goals and plans. This allows for the provision of more realistic and useful future scenarios for the user by considering current goals and plans.

[0047] The future prediction scenario unit can generate future scenarios for specific locations, taking into account the user's geographical location information. For example, it can generate future scenarios related to the user's current location. It can also generate future scenarios related to places the user frequently visits. Furthermore, if the user is traveling, it can generate future scenarios related to places they have visited. This allows the system to provide users with highly relevant scenarios by generating future scenarios for specific locations.

[0048] The future prediction scenario unit can analyze users' social media activity and generate relevant future scenarios. For example, it can generate future scenarios based on content shared by users on social media. It can also analyze the content of users' social media posts and generate relevant future scenarios. Furthermore, it can generate future scenarios based on users' interactions with their social media friends. This allows for the provision of richer information by generating relevant future scenarios based on social media activity.

[0049] The Special Event Scenario Unit can analyze a user's past special event history and propose the optimal scenario. For example, it can suggest similar event scenarios based on events the user has enjoyed in the past. It can also analyze a user's past special event history and propose related scenarios. Furthermore, it can propose the optimal event scenario based on a user's past event participation history. This allows the unit to propose the most suitable scenario for the user by analyzing their past special event history.

[0050] The special event scenario section can consider the user's current interests when generating special event scenarios. For example, the special event scenario section can generate special event scenarios based on the user's current interests. It can also generate special event scenarios considering the user's current hobbies and interests. Furthermore, the special event scenario section can analyze the user's current interests and generate the most optimal special event scenario. This allows for the provision of more interesting scenarios for the user by considering their current interests.

[0051] The special event scenario section can generate special event scenarios for specific locations, taking into account the user's geographical location. For example, it can generate special event scenarios related to the user's current location. It can also generate special event scenarios related to places the user frequently visits. Furthermore, if the user is traveling, it can generate special event scenarios related to places they have visited. This allows the system to provide users with highly relevant scenarios by generating special event scenarios for specific locations.

[0052] The Special Event Scenario Unit can analyze users' social media activity and generate relevant special event scenarios. For example, it can generate special event scenarios based on content shared by users on social media. It can also analyze users' social media posts and generate relevant special event scenarios. Furthermore, it can generate special event scenarios based on users' interactions with their social media friends. This allows for the provision of richer information by generating relevant special event scenarios based on social media activity.

[0053] The sentiment analysis and feedback unit can analyze the user's past conversation history and provide optimal feedback. For example, it can provide relevant feedback based on the user's past conversation history. Furthermore, it can analyze the user's past emotional states and provide optimal feedback. In addition, it can provide appropriate feedback based on the content of the user's past conversations. This allows the system to provide the user with the most appropriate feedback by analyzing their past conversation history.

[0054] The sentiment analysis and feedback unit can consider the user's current situation when providing feedback. For example, it can provide appropriate feedback based on the user's current emotional state. It can also provide feedback considering the user's current activities. Furthermore, it can provide optimal feedback based on the user's current environment. This allows for more appropriate feedback to be provided to the user by considering their current situation.

[0055] The sentiment analysis and feedback unit can provide feedback relevant to a specific location, taking into account the user's geographical location. For example, it can provide feedback related to the user's current location. It can also provide feedback related to places the user frequently visits. Furthermore, if the user is traveling, it can provide feedback related to places they have visited. This allows for the provision of highly relevant feedback to the user by offering location-specific feedback.

[0056] The sentiment analysis and feedback unit can analyze a user's social media activity and provide relevant feedback. For example, it can provide feedback based on what a user shares on social media. It can also analyze a user's social media posts and provide relevant feedback. Furthermore, it can provide feedback based on the user's interactions with their social media friends. This allows for the provision of richer information by providing relevant feedback based on social media activity.

[0057] The avatar customization section can analyze a user's past avatar customization history and suggest the optimal customization options. For example, it can suggest the optimal customization options based on the avatar styles the user has previously selected. Furthermore, the avatar customization section can analyze the user's past customization history and suggest related options. It can also suggest the optimal customization options based on the user's past choices. In this way, by analyzing past customization history, it can suggest the most suitable customization options to the user.

[0058] The avatar customization section can change the appearance of the avatar in specific locations, taking into account the user's geographical location. For example, it can change the appearance of the avatar in relation to the user's current location. It can also change the appearance of the avatar in relation to places the user frequently visits. Furthermore, if the user is traveling, it can change the appearance of the avatar in relation to places they have visited. This allows the system to provide users with avatars that are highly relevant to their needs by changing the appearance of the avatar in specific locations.

[0059] The self-growth visualization unit can analyze the user's past growth data and propose the optimal visualization method. For example, the self-growth visualization unit can propose the optimal visualization method based on the user's past growth data. Furthermore, the self-growth visualization unit can analyze the user's past growth patterns and propose relevant visualization methods. In addition, the self-growth visualization unit can propose the optimal visualization method based on the user's past growth history. This allows the system to propose the most suitable visualization method for the user by analyzing past growth data.

[0060] The self-growth visualization unit can visualize self-growth at specific locations, taking into account the user's geographical location. For example, it can visualize growth data related to the user's current location. It can also visualize growth data related to places the user frequently visits. Furthermore, if the user is traveling, it can visualize growth data related to places they have visited. This allows for highly relevant visualizations for the user by providing visualizations of self-growth at specific locations.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The Time Travel Diary system can also include a health management unit that acquires the user's health data and records it in association with daily events. The health management unit can, for example, acquire data such as the user's steps, heart rate, and sleep duration, and automatically add it to the diary. Furthermore, the health management unit can analyze the user's health data and visualize changes in their health status. In addition, the health management unit can provide appropriate advice and reminders based on the user's health data. This allows the user to see daily events and their health status at a glance and manage their health effectively.

[0063] The time travel diary system can also include a hobby suggestion section that proposes relevant events and activities based on the user's hobbies and interests. For example, the hobby suggestion section can suggest relevant events and activities based on the user's previously recorded hobbies and interests. It can also suggest new hobbies and activities based on the user's current interests. Furthermore, the hobby suggestion section can suggest events held nearby, taking into account the user's geographical location. This allows users to discover new hobbies and activities, enriching their daily lives.

[0064] The time travel diary system can also include a routine suggestion unit that analyzes the user's past behavioral patterns and proposes an optimal daily routine. For example, the routine suggestion unit could suggest an optimal daily routine based on the user's past successful routines. Furthermore, the routine suggestion unit could analyze the user's past behavioral patterns and suggest relevant routines. In addition, the routine suggestion unit could propose an optimal routine considering the user's current goals and plans. This allows the user to optimize their daily routine and efficiently achieve their goals.

[0065] The time travel diary system may also include a location information recording unit that automatically records events at specific locations, taking into account the user's geographical location. For example, the location information recording unit automatically records events at a specific location when the user arrives there. It can also automatically record events at places the user frequently visits. Furthermore, if the user is traveling, the location information recording unit can automatically record events at places they visit. This improves the accuracy of the recording by automatically recording events at specific locations.

[0066] The Time Travel Diary system can also include a social media integration unit that analyzes the user's social media activity and automatically adds relevant events to the diary. For example, the social media integration unit automatically adds events shared by the user on social media to the diary. It can also analyze the user's social media posts and display relevant events in the diary. Furthermore, the social media integration unit can add relevant events to the diary based on the user's interactions with their social media friends. This allows for the provision of richer information by adding relevant events to the diary based on social media activity.

[0067] The time travel diary system can also include a recording suggestion unit that analyzes the user's past diary entries and proposes the optimal recording method. For example, the recording suggestion unit might prioritize suggesting recording methods the user has frequently used in the past. It can also analyze the content of the user's past entries and suggest relevant recording methods. Furthermore, the recording suggestion unit can analyze the user's recording patterns and automatically suggest the optimal recording method. This allows the system to suggest the most suitable recording method to the user by analyzing past entries.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The timeline view section allows users to see past, present, and future events at a glance. The timeline view section displays events in a graphical timeline or list format, for example. The timeline view section also provides a visual interface for users to review past events and predict future events. For example, the timeline view section can display past events in different colors and highlight important events. Step 2: The diary section allows users to record daily events. The diary section allows users to record events using methods such as text input, voice input, and image attachments. The diary section also provides a tagging function to make it easier for users to search for recorded events. For example, the diary section can automatically tag events recorded by the user. Step 3: The future prediction scenario unit can generate future scenarios based on events recorded by the diary unit. The future prediction scenario unit uses a generation AI to generate future scenarios based on interaction with the user. For example, the future prediction scenario unit generates scenarios that predict future events based on goals and plans entered by the user. Step 4: The Special Event Scenario section can provide special dialogue scenarios tailored to special events. The Special Event Scenario section generates dialogue scenarios with the user to coincide with special events such as birthdays or anniversaries. For example, the Special Event Scenario section provides a scenario that displays a special message on the user's birthday. Step 5: The sentiment analysis and feedback unit can analyze the interaction with the user, understand their emotions, and provide appropriate feedback. The sentiment analysis and feedback unit uses generative AI to analyze the user's emotions and provide appropriate feedback. For example, the sentiment analysis and feedback unit analyzes the text or voice input by the user to estimate the user's emotions.

[0070] (Example of form 2) The Time Travel Diary System according to an embodiment of the present invention is an application that allows users to record daily events while interacting with their past and future selves. This Time Travel Diary System includes a timeline view function that allows users to see past, present, and future events at a glance, an avatar customization function, a diary function for recording daily events, a future prediction scenario function that generates future scenarios based on interactions with the user, a special dialogue scenario function tailored to special events such as birthdays and anniversaries, an emotion analysis and feedback function in which a generating AI analyzes interactions with the user, understands emotions, and provides appropriate feedback, and a self-growth visualization function that allows users to visually confirm their self-growth by viewing past, present, and future events in a timeline view. As a result, users can feel their own growth, have hope for the future, and organize their daily emotions. For example, the Time Travel Diary System allows users to visually confirm their own growth by looking back on past events and predicting future events. The Time Travel Diary System also allows users to enjoy special dialogue scenarios tailored to special events and anniversaries. Furthermore, the Time Travel Diary System can understand the user's emotions and support them in recording daily events by using a generating AI to analyze the user's emotions and provide appropriate feedback. This allows users to experience personal growth, have hope for the future, and organize their daily emotions. The time travel diary system enables users to see past, present, and future events at a glance, record daily events, generate future scenarios, provide dialogue scenarios tailored to specific events, and understand and provide feedback on their emotions.

[0071] The time travel diary system according to this embodiment comprises a timeline view unit, a diary unit, a future prediction scenario unit, a special event scenario unit, and an emotion analysis and feedback unit. The timeline view unit allows users to view past, present, and future events at a glance. The timeline view unit displays events in, for example, a graphical timeline or list format. The timeline view unit also provides a visual interface for users to review past events and predict future events. For example, the timeline view unit can display past events in different colors and highlight important events. The diary unit allows users to record daily events. The diary unit records events by methods such as text input, voice input, and image attachment. The diary unit also provides a tagging function to make it easier for users to search for recorded events. For example, the diary unit can automatically tag events recorded by the user. The future prediction scenario unit can generate future scenarios based on events recorded by the diary unit. The future prediction scenario unit generates future scenarios based on interaction with the user using a generation AI. For example, the future prediction scenario unit generates scenarios that predict future events based on goals and plans entered by the user. The special event scenario unit can provide special dialogue scenarios tailored to special events. The special event scenario unit generates dialogue scenarios with the user tailored to special events such as birthdays and anniversaries. For example, the special event scenario unit provides a scenario that displays a special message on the user's birthday. The sentiment analysis and feedback unit can analyze the interaction with the user, understand their emotions, and provide appropriate feedback. The sentiment analysis and feedback unit uses generative AI to analyze the user's emotions and provide appropriate feedback. For example, the sentiment analysis and feedback unit analyzes the text and voice entered by the user to estimate the user's emotions.As a result, the time travel diary system according to this embodiment allows the user to see past, present, and future events at a glance, record daily events, generate future scenarios, provide dialogue scenarios tailored to special events, and understand and provide feedback on emotions.

[0072] The Timeline View section allows users to see past, present, and future events at a glance. It displays events in various formats, such as a graphical timeline or list. Specifically, it provides a visual interface for users to review past events and predict future events. For example, it can color-code past events to highlight important ones. This allows users to easily review past events and use them as a reference when planning for the future. Furthermore, the Timeline View section allows users to select specific periods or events to view details. For example, clicking on a specific date displays details of events recorded on that day. The Timeline View also allows users to add comments and notes to past events. This allows users to record their thoughts and feelings about past events for later reflection. Additionally, the Timeline View section functions as a tool for users to predict future events. For example, if a user enters future plans or goals, predicted events based on those plans are displayed on the timeline. This allows users to visually review their future plans and adjust them as needed.

[0073] The diary section allows users to record daily events. The diary section allows users to record events using methods such as text input, voice input, and image attachment. Specifically, users can easily record daily events using their smartphones or computers. For example, if a user enters the day's events as text, the diary section saves the text for later searching. When using voice input, users can record events simply by speaking into the microphone. Furthermore, the image attachment function allows users to add photos and images related to the day's events. This allows users to create visual records that can be helpful when looking back later. The diary section provides a tagging function to make it easier to search for recorded events. For example, the diary section can automatically tag events recorded by the user. This allows users to easily search for records related to specific events or themes. Additionally, the diary section allows users to manually add tags, enabling more detailed classification. This allows users to efficiently record daily events and easily search and review them later.

[0074] The Future Prediction Scenario Unit can generate future scenarios based on events recorded by the Diary Unit. The Future Prediction Scenario Unit uses a generation AI to generate future scenarios based on interaction with the user. Specifically, it generates scenarios that predict future events based on the goals and plans entered by the user. For example, if the user enters "My goal for next year is to complete a marathon," the Future Prediction Scenario Unit will generate a scenario that predicts training schedules and progress based on that goal. The generation AI utilizes past data and statistical information to propose the optimal scenario for achieving the user's goal. Furthermore, the Future Prediction Scenario Unit can generate multiple scenarios based on the information entered by the user, providing the user with choices. For example, if the user enters "I want to travel next summer," the Future Prediction Scenario Unit will generate and propose multiple travel plans. This allows the user to select the optimal future scenario based on their goals and plans. Additionally, the Future Prediction Scenario Unit can modify scenarios based on user feedback to provide more accurate predictions. This allows users to predict future events and use them to make plans.

[0075] The Special Event Scenario Unit can provide special dialogue scenarios tailored to specific events. For example, it generates dialogue scenarios with users to coincide with special events such as birthdays and anniversaries. Specifically, it provides a scenario that displays a special message on the user's birthday. For example, when a user celebrates their birthday, the Special Event Scenario Unit uses its AI generation capabilities to display a message such as, "Happy Birthday! I hope you have a wonderful year!" The Special Event Scenario Unit can also suggest special messages and surprise events when a user celebrates an anniversary. For example, when a user celebrates their wedding anniversary, the Special Event Scenario Unit displays a message such as, "Happy Wedding Anniversary! How about planning a special dinner tonight?" Furthermore, the Special Event Scenario Unit can provide helpful information and advice as users prepare for specific events. For example, when a user is graduating, the Special Event Scenario Unit displays a message such as, "Congratulations on your graduation! How are your graduation preparations going? Let's make a list of anything you need." In this way, the Special Event Scenario Unit provides users with dialogue scenarios tailored to special events, supporting them in enjoying their special day even more.

[0076] The sentiment analysis and feedback unit can analyze user interactions, understand emotions, and provide appropriate feedback. Using generative AI, the unit analyzes user emotions and provides appropriate feedback. Specifically, it analyzes text and audio input from the user to estimate their emotions. For example, if a user inputs "I'm very tired today," the sentiment analysis and feedback unit analyzes the text and understands that the user is tired. The generative AI then provides feedback such as, "You must be tired today. Please get some rest." Furthermore, the sentiment analysis and feedback unit can track changes in the user's emotions and analyze long-term emotional trends. This allows it to provide appropriate support in response to changes in the user's emotions. For example, if a user has recently been feeling stressed, the sentiment analysis and feedback unit might provide feedback such as, "It seems you've been feeling stressed lately. Why not try some ways to relax?" In addition, the sentiment analysis and feedback unit can suggest appropriate actions based on the user's emotions. For example, if a user inputs "I'm very happy today," the sentiment analysis and feedback unit might provide feedback such as, "That's wonderful! Cherish that feeling." This allows the emotion analysis and feedback unit to understand the user's emotions and provide appropriate feedback, thereby supporting the user's mental health.

[0077] The avatar customization section allows users to customize their avatars. It provides customization options such as changing appearance and selecting clothing. For example, users can change their avatar's hairstyle and clothing. The avatar customization section also allows users to customize their avatar's facial expressions and actions. For example, users can change their avatar's facial expression to a smile. This allows users to customize their avatars to their liking.

[0078] The self-growth visualization section allows users to visually confirm their personal growth. This section provides visualization methods such as graphs and progress bars. For example, the self-growth visualization section allows users to review past events and see their personal growth in a graph. It can also visually display the progress towards user-set goals. For example, it displays the progress towards user-set goals using a progress bar. This allows users to visually confirm their personal growth and maintain motivation.

[0079] The timeline view section can estimate the user's emotions and adjust how the timeline is displayed based on those emotions. For example, if the user is stressed, the timeline view section can highlight important events and hide unnecessary information. If the user is relaxed, the timeline view section can display detailed information and make it easier to review past events. Furthermore, if the user is in a hurry, the timeline view section can display only the essentials to allow for quick information acquisition. In this way, by adjusting how the timeline is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The timeline view can dynamically change the display order of past events based on their importance. For example, it can prioritize important events and anniversaries so that users can quickly find them. It can also display events that users frequently refer to at the top for easier access. Furthermore, it can prioritize events that are highly relevant to the user's interests. This allows users to quickly find important events by prioritizing their display.

[0081] The timeline view can analyze the user's activity history and highlight the most relevant events. For example, it can highlight events related to places the user has frequently visited in the past. It can also highlight events related to activities the user performed during specific time periods. Furthermore, the timeline view can analyze the user's past behavior patterns and automatically highlight relevant events. This ensures that important information is not missed by highlighting relevant events based on the user's activity history.

[0082] The timeline view section can estimate the user's emotions and change the timeline's color scheme and design based on the estimated emotions. For example, if the user is sad, the timeline view section can change to a calm color scheme and design. If the user is happy, it can change to a bright color scheme and design. Furthermore, if the user is tired, it can change to a visually relaxing design. In this way, by changing the color scheme and design of the timeline according to the user's emotions, a visually comfortable display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The timeline view can highlight events in specific locations, taking into account the user's geographical location. For example, it can highlight past events related to the user's current location. It can also prioritize displaying events related to places the user frequently visits. Furthermore, if the user is traveling, it can highlight events related to places they have visited. This allows for the provision of highly relevant information by highlighting events related to the user's current location and frequently visited places.

[0084] The Timeline View section can analyze a user's social media activity and add relevant events to the timeline. For example, it can automatically add events that a user has shared on social media to the timeline. It can also analyze a user's social media posts and display relevant events on the timeline. Furthermore, it can add relevant events to the timeline based on the user's interactions with their social media friends. This allows for the provision of richer information by adding relevant events to the timeline based on social media activity.

[0085] The diary section can estimate the user's emotions and adjust the diary's input interface based on the estimated emotions. For example, if the user is stressed, the diary section can provide a simple input interface. Conversely, if the user is relaxed, it can provide more detailed input options. Furthermore, if the user is in a hurry, the diary section can provide an interface that prioritizes voice input. This allows for a more comfortable input environment by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The diary section can analyze the user's past diary entries and suggest the optimal recording method. For example, the diary section can prioritize suggesting recording methods that the user has frequently used in the past. It can also analyze the content of the user's past entries and suggest relevant recording methods. Furthermore, the diary section can analyze the user's recording patterns and automatically suggest the optimal recording method. This allows the system to suggest the most suitable recording method for the user by analyzing past entries.

[0087] The diary function can automatically tag entries based on the user's current activities. For example, it can automatically tag entries based on the user's current activities. It can also automatically tag relevant entries based on the user's current location. Furthermore, it can analyze the user's current emotional state and automatically tag appropriate entries. This automatic tagging based on current activities makes organizing entries easier.

[0088] The diary function can estimate the user's emotions and adjust the frequency of diary entries based on those emotions. For example, if the user is stressed, the diary function can reduce the frequency of entries to alleviate the burden. Conversely, if the user is relaxed, the diary function can increase the frequency of entries to encourage more detailed recording. Furthermore, if the user is busy, the diary function can adjust the frequency of entries to encourage more efficient recording. In this way, by adjusting the frequency of entries according to the user's emotions, the burden can be reduced and detailed recording can be encouraged. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The diary function can automatically record events at specific locations, taking into account the user's geographical location. For example, when the user arrives at a particular location, the diary function can automatically record events at that location. It can also automatically record events at places the user frequently visits. Furthermore, if the user is traveling, the diary function can automatically record events at places they visit. This improves the accuracy of the records by automatically recording events at specific locations.

[0090] The diary function can analyze a user's social media activity and automatically add relevant events to the diary. For example, it can automatically add events that a user shares on social media. It can also analyze a user's social media posts and add relevant events. Furthermore, it can add relevant events based on the user's interactions with their social media friends. This allows for the provision of richer information by adding relevant events based on social media activity.

[0091] The future prediction scenario unit can estimate the user's emotions and adjust the content of the future scenario based on those emotions. For example, if the user is optimistic, the future prediction scenario unit can generate a positive future scenario. It can also generate a realistic future scenario if the user is pessimistic. Furthermore, if the user is excited, the future prediction scenario unit can generate an exciting future scenario. This allows for the provision of more realistic and appropriate scenarios by adjusting the content of the future scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The future prediction scenario unit can analyze the user's past behavior patterns and generate the most realistic future scenarios. For example, the future prediction scenario unit generates realistic future scenarios based on the user's past behavior patterns. Furthermore, the future prediction scenario unit can analyze the user's past choices and propose the most realistic future scenarios. In addition, the future prediction scenario unit can generate realistic future scenarios based on the user's past behavior history. This allows for the provision of highly reliable scenarios for the user by generating realistic future scenarios based on past behavior patterns.

[0093] The future prediction scenario unit can consider the user's current goals and plans when generating future prediction scenarios. For example, the future prediction scenario unit can generate future scenarios based on the user's current goals. It can also generate future scenarios considering the user's current plans. Furthermore, the future prediction scenario unit can generate realistic future scenarios based on the user's current goals and plans. This allows for the provision of more realistic and useful future scenarios for the user by considering current goals and plans.

[0094] The future prediction scenario unit can estimate the user's emotions and determine the priority of future scenarios based on those estimated emotions. For example, if the user is optimistic, the future prediction scenario unit will prioritize positive future scenarios. It can also prioritize realistic future scenarios if the user is pessimistic. Furthermore, if the user is excited, it can prioritize exciting future scenarios. This allows for the provision of more appropriate scenarios by prioritizing future scenarios according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The future prediction scenario unit can generate future scenarios for specific locations, taking into account the user's geographical location information. For example, it can generate future scenarios related to the user's current location. It can also generate future scenarios related to places the user frequently visits. Furthermore, if the user is traveling, it can generate future scenarios related to places they have visited. This allows the system to provide users with highly relevant scenarios by generating future scenarios for specific locations.

[0096] The future prediction scenario unit can analyze users' social media activity and generate relevant future scenarios. For example, it can generate future scenarios based on content shared by users on social media. It can also analyze the content of users' social media posts and generate relevant future scenarios. Furthermore, it can generate future scenarios based on users' interactions with their social media friends. This allows for the provision of richer information by generating relevant future scenarios based on social media activity.

[0097] The special event scenario unit can estimate the user's emotions and adjust the content of the special event scenario based on those emotions. For example, if the user is happy, the special event scenario unit can provide a positive event scenario. It can also provide an encouraging event scenario if the user is sad. Furthermore, it can provide an exciting event scenario if the user is excited. By adjusting the content of the special event scenario according to the user's emotions, a more appropriate scenario can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The Special Event Scenario Unit can analyze a user's past special event history and propose the optimal scenario. For example, it can suggest similar event scenarios based on events the user has enjoyed in the past. It can also analyze a user's past special event history and propose related scenarios. Furthermore, it can propose the optimal event scenario based on a user's past event participation history. This allows the unit to propose the most suitable scenario for the user by analyzing their past special event history.

[0099] The special event scenario section can consider the user's current interests when generating special event scenarios. For example, the special event scenario section can generate special event scenarios based on the user's current interests. It can also generate special event scenarios considering the user's current hobbies and interests. Furthermore, the special event scenario section can analyze the user's current interests and generate the most optimal special event scenario. This allows for the provision of more interesting scenarios for the user by considering their current interests.

[0100] The special event scenario unit can estimate the user's emotions and determine the priority of special event scenarios based on those emotions. For example, if the user is happy, the special event scenario unit will prioritize positive event scenarios. It can also prioritize encouraging event scenarios if the user is sad. Furthermore, it can prioritize exciting event scenarios if the user is excited. This allows for the provision of more appropriate scenarios by prioritizing special event scenarios according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The special event scenario section can generate special event scenarios for specific locations, taking into account the user's geographical location. For example, it can generate special event scenarios related to the user's current location. It can also generate special event scenarios related to places the user frequently visits. Furthermore, if the user is traveling, it can generate special event scenarios related to places they have visited. This allows the system to provide users with highly relevant scenarios by generating special event scenarios for specific locations.

[0102] The Special Event Scenario Unit can analyze users' social media activity and generate relevant special event scenarios. For example, it can generate special event scenarios based on content shared by users on social media. It can also analyze users' social media posts and generate relevant special event scenarios. Furthermore, it can generate special event scenarios based on users' interactions with their social media friends. This allows for the provision of richer information by generating relevant special event scenarios based on social media activity.

[0103] The sentiment analysis and feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is sad, the sentiment analysis and feedback unit can provide encouraging feedback. It can also provide empathetic feedback if the user is happy. Furthermore, if the user is stressed, the sentiment analysis and feedback unit can provide relaxing feedback. In this way, by adjusting the content of the feedback according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The sentiment analysis and feedback unit can analyze the user's past conversation history and provide optimal feedback. For example, it can provide relevant feedback based on the user's past conversation history. Furthermore, it can analyze the user's past emotional states and provide optimal feedback. In addition, it can provide appropriate feedback based on the content of the user's past conversations. This allows the system to provide the user with the most appropriate feedback by analyzing their past conversation history.

[0105] The sentiment analysis and feedback unit can consider the user's current situation when providing feedback. For example, it can provide appropriate feedback based on the user's current emotional state. It can also provide feedback considering the user's current activities. Furthermore, it can provide optimal feedback based on the user's current environment. This allows for more appropriate feedback to be provided to the user by considering their current situation.

[0106] The sentiment analysis and feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is sad, the sentiment analysis and feedback unit will prioritize encouraging feedback. It can also prioritize empathetic feedback if the user is happy. Furthermore, if the user is stressed, it can prioritize relaxing feedback. This allows for more appropriate feedback to be provided by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The sentiment analysis and feedback unit can provide feedback relevant to a specific location, taking into account the user's geographical location. For example, it can provide feedback related to the user's current location. It can also provide feedback related to places the user frequently visits. Furthermore, if the user is traveling, it can provide feedback related to places they have visited. This allows for the provision of highly relevant feedback to the user by offering location-specific feedback.

[0108] The sentiment analysis and feedback unit can analyze a user's social media activity and provide relevant feedback. For example, it can provide feedback based on what a user shares on social media. It can also analyze a user's social media posts and provide relevant feedback. Furthermore, it can provide feedback based on the user's interactions with their social media friends. This allows for the provision of richer information by providing relevant feedback based on social media activity.

[0109] The avatar customization unit can estimate the user's emotions and automatically adjust the avatar's appearance based on those emotions. For example, if the user is happy, the avatar customization unit can change to a brighter color scheme. It can also change to a calmer color scheme if the user is sad. Furthermore, if the user is excited, the avatar customization unit can change to a visually stimulating one. This allows for the provision of a more appropriate avatar by adjusting its appearance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The avatar customization section can analyze a user's past avatar customization history and suggest the optimal customization options. For example, it can suggest the optimal customization options based on the avatar styles the user has previously selected. Furthermore, the avatar customization section can analyze the user's past customization history and suggest related options. It can also suggest the optimal customization options based on the user's past choices. In this way, by analyzing past customization history, it can suggest the most suitable customization options to the user.

[0111] The avatar customization unit can estimate the user's emotions and adjust the avatar's movements and expressions based on the estimated emotions. For example, if the user is happy, the avatar's expression can be changed to a smile. It can also change the avatar's expression to a calmer one if the user is sad. Furthermore, if the user is excited, the avatar's movements can be made more lively. This allows for the provision of a more appropriate avatar by adjusting the avatar's movements and expressions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The avatar customization section can change the appearance of the avatar in specific locations, taking into account the user's geographical location. For example, it can change the appearance of the avatar in relation to the user's current location. It can also change the appearance of the avatar in relation to places the user frequently visits. Furthermore, if the user is traveling, it can change the appearance of the avatar in relation to places they have visited. This allows the system to provide users with avatars that are highly relevant to their needs by changing the appearance of the avatar in specific locations.

[0113] The self-growth visualization unit can estimate the user's emotions and adjust the self-growth visualization method based on the estimated user emotions. For example, if the user is happy, the self-growth visualization unit can provide a positive visualization method. It can also provide an encouraging visualization method if the user is sad. Furthermore, it can provide an exciting visualization method if the user is excited. This allows for more appropriate visualization by adjusting the self-growth visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The self-growth visualization unit can analyze the user's past growth data and propose the optimal visualization method. For example, the self-growth visualization unit can propose the optimal visualization method based on the user's past growth data. Furthermore, the self-growth visualization unit can analyze the user's past growth patterns and propose relevant visualization methods. In addition, the self-growth visualization unit can propose the optimal visualization method based on the user's past growth history. This allows the system to propose the most suitable visualization method for the user by analyzing past growth data.

[0115] The self-growth visualization unit can estimate the user's emotions and determine the priority of self-growth visualizations based on the estimated user emotions. For example, if the user is happy, the self-growth visualization unit will prioritize displaying positive growth data. Similarly, if the user is sad, the self-growth visualization unit can prioritize displaying encouraging growth data. Furthermore, if the user is excited, the self-growth visualization unit can prioritize displaying stimulating growth data. This allows for more appropriate visualizations by prioritizing self-growth visualizations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The self-growth visualization unit can visualize self-growth at specific locations, taking into account the user's geographical location. For example, it can visualize growth data related to the user's current location. It can also visualize growth data related to places the user frequently visits. Furthermore, if the user is traveling, it can visualize growth data related to places they have visited. This allows for highly relevant visualizations for the user by providing visualizations of self-growth at specific locations.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The Time Travel Diary system can also include a health management unit that acquires the user's health data and records it in association with daily events. The health management unit can, for example, acquire data such as the user's steps, heart rate, and sleep duration, and automatically add it to the diary. Furthermore, the health management unit can analyze the user's health data and visualize changes in their health status. In addition, the health management unit can provide appropriate advice and reminders based on the user's health data. This allows the user to see daily events and their health status at a glance and manage their health effectively.

[0119] The time travel diary system can also include a hobby suggestion section that proposes relevant events and activities based on the user's hobbies and interests. For example, the hobby suggestion section can suggest relevant events and activities based on the user's previously recorded hobbies and interests. It can also suggest new hobbies and activities based on the user's current interests. Furthermore, the hobby suggestion section can suggest events held nearby, taking into account the user's geographical location. This allows users to discover new hobbies and activities, enriching their daily lives.

[0120] The Time Travel Diary system can also include a relaxation section that estimates the user's emotions and provides content for relaxation and stress relief based on those emotions. For example, if the user is feeling stressed, the relaxation section can provide relaxation music or meditation guides. If the user is relaxed, the relaxation section can also suggest refreshing activities. Furthermore, the relaxation section can automatically select appropriate relaxation content based on the user's emotional state. This allows the user to utilize relaxation content tailored to their emotions and reduce stress.

[0121] The time travel diary system can also include an emotional feedback unit that estimates the user's emotions and provides appropriate feedback based on those emotions. For example, if the user is sad, the emotional feedback unit can provide an encouraging message. It can also provide an empathetic message if the user is happy. Furthermore, if the user is stressed, it can provide relaxing advice. This allows the user to receive emotionally appropriate feedback and view daily events more positively.

[0122] The time travel diary system can also include a goal-setting unit that estimates the user's emotions and supports appropriate goal setting based on those emotions. For example, if the user is optimistic, the goal-setting unit may suggest challenging goals. If the user is pessimistic, the goal-setting unit may suggest realistic goals. Furthermore, if the user is excited, the goal-setting unit may suggest stimulating goals. This allows the user to set goals that match their emotions and gain a sense of accomplishment.

[0123] The time travel diary system can also include a routine suggestion unit that analyzes the user's past behavioral patterns and proposes an optimal daily routine. For example, the routine suggestion unit could suggest an optimal daily routine based on the user's past successful routines. Furthermore, the routine suggestion unit could analyze the user's past behavioral patterns and suggest relevant routines. In addition, the routine suggestion unit could propose an optimal routine considering the user's current goals and plans. This allows the user to optimize their daily routine and efficiently achieve their goals.

[0124] The time travel diary system may also include a location information recording unit that automatically records events at specific locations, taking into account the user's geographical location. For example, the location information recording unit automatically records events at a specific location when the user arrives there. It can also automatically record events at places the user frequently visits. Furthermore, if the user is traveling, the location information recording unit can automatically record events at places they visit. This improves the accuracy of the recording by automatically recording events at specific locations.

[0125] The Time Travel Diary system can also include a social media integration unit that analyzes the user's social media activity and automatically adds relevant events to the diary. For example, the social media integration unit automatically adds events shared by the user on social media to the diary. It can also analyze the user's social media posts and display relevant events in the diary. Furthermore, the social media integration unit can add relevant events to the diary based on the user's interactions with their social media friends. This allows for the provision of richer information by adding relevant events to the diary based on social media activity.

[0126] The time travel diary system can also include a reminder unit that estimates the user's emotions and provides appropriate reminders based on those emotions. For example, if the user is feeling stressed, the reminder unit can provide a reminder to help them relax. If the user is relaxed, the reminder unit can also provide a reminder to guide them through their next task. Furthermore, if the user is busy, the reminder unit can provide reminders to help them complete tasks efficiently. This allows the user to receive emotionally appropriate reminders and manage their daily tasks efficiently.

[0127] The time travel diary system can also include a recording suggestion unit that analyzes the user's past diary entries and proposes the optimal recording method. For example, the recording suggestion unit might prioritize suggesting recording methods the user has frequently used in the past. It can also analyze the content of the user's past entries and suggest relevant recording methods. Furthermore, the recording suggestion unit can analyze the user's recording patterns and automatically suggest the optimal recording method. This allows the system to suggest the most suitable recording method to the user by analyzing past entries.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The timeline view section allows users to see past, present, and future events at a glance. The timeline view section displays events in a graphical timeline or list format, for example. The timeline view section also provides a visual interface for users to review past events and predict future events. For example, the timeline view section can display past events in different colors and highlight important events. Step 2: The diary section allows users to record daily events. The diary section allows users to record events using methods such as text input, voice input, and image attachments. The diary section also provides a tagging function to make it easier for users to search for recorded events. For example, the diary section can automatically tag events recorded by the user. Step 3: The future prediction scenario unit can generate future scenarios based on events recorded by the diary unit. The future prediction scenario unit uses a generation AI to generate future scenarios based on interaction with the user. For example, the future prediction scenario unit generates scenarios that predict future events based on goals and plans entered by the user. Step 4: The Special Event Scenario section can provide special dialogue scenarios tailored to special events. The Special Event Scenario section generates dialogue scenarios with the user to coincide with special events such as birthdays or anniversaries. For example, the Special Event Scenario section provides a scenario that displays a special message on the user's birthday. Step 5: The sentiment analysis and feedback unit can analyze the interaction with the user, understand their emotions, and provide appropriate feedback. The sentiment analysis and feedback unit uses generative AI to analyze the user's emotions and provide appropriate feedback. For example, the sentiment analysis and feedback unit analyzes the text or voice input by the user to estimate the user's emotions.

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0133] Each of the multiple elements described above, including the timeline view unit, diary unit, future prediction scenario unit, special event scenario unit, sentiment analysis and feedback unit, avatar customization unit, and self-growth visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the timeline view unit displays past, present, and future events on the display 40A of the smart device 14. The diary unit records daily events using the touch panel 38A of the smart device 14. The future prediction scenario unit generates future scenarios using the specific processing unit 290 of the data processing unit 12. The special event scenario unit generates special dialogue scenarios using the specific processing unit 290 of the data processing unit 12. The sentiment analysis and feedback unit analyzes the user's emotions and provides feedback using the specific processing unit 290 of the data processing unit 12. The avatar customization unit customizes the avatar using the control unit 46A of the smart device 14. The self-growth visualization unit visually displays self-growth using the display 40A of the smart device 14. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the timeline view unit, diary unit, future prediction scenario unit, special event scenario unit, sentiment analysis and feedback unit, avatar customization unit, and self-growth visualization unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the timeline view unit displays past, present, and future events on the display of the smart glasses 214. The diary unit uses the microphone 238 of the smart glasses 214 to input daily events by voice. The future prediction scenario unit generates future scenarios using the specific processing unit 290 of the data processing unit 12. The special event scenario unit generates special dialogue scenarios using the specific processing unit 290 of the data processing unit 12. The sentiment analysis and feedback unit analyzes the user's emotions and provides feedback using the specific processing unit 290 of the data processing unit 12. The avatar customization unit customizes the avatar using the control unit 46A of the smart glasses 214. The self-growth visualization unit visually displays self-growth on the display of the smart glasses 214. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the timeline view unit, diary unit, future prediction scenario unit, special event scenario unit, sentiment analysis and feedback unit, avatar customization unit, and self-growth visualization unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the timeline view unit displays past, present, and future events on the display 343 of the headset terminal 314. The diary unit uses the microphone 238 of the headset terminal 314 to input daily events by voice. The future prediction scenario unit generates future scenarios using the specific processing unit 290 of the data processing unit 12. The special event scenario unit generates special dialogue scenarios using the specific processing unit 290 of the data processing unit 12. The sentiment analysis and feedback unit analyzes the user's emotions and provides feedback using the specific processing unit 290 of the data processing unit 12. The avatar customization unit customizes the avatar using the control unit 46A of the headset terminal 314. The self-growth visualization unit visually displays self-growth using the display 343 of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the timeline view unit, diary unit, future prediction scenario unit, special event scenario unit, sentiment analysis and feedback unit, avatar customization unit, and self-growth visualization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the timeline view unit displays past, present, and future events on the robot 414's display. The diary unit uses the robot 414's microphone 238 to input daily events by voice. The future prediction scenario unit generates future scenarios using the specific processing unit 290 of the data processing unit 12. The special event scenario unit generates special dialogue scenarios using the specific processing unit 290 of the data processing unit 12. The sentiment analysis and feedback unit analyzes the user's emotions and provides feedback using the specific processing unit 290 of the data processing unit 12. The avatar customization unit customizes the avatar using the control unit 46A of the robot 414. The self-growth visualization unit visually displays self-growth using the robot 414's display. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) The timeline view section allows you to see past, present, and future events at a glance, The diary section, where daily events are recorded, A future prediction scenario unit generates future scenarios based on events recorded by the diary unit, The Special Event Scenario Department provides special dialogue scenarios tailored to special events, It includes an emotion analysis and feedback unit that analyzes user interactions, understands emotions, and provides appropriate feedback. A system characterized by the following features. (Note 2) Features an avatar customization section. The system described in Appendix 1, characterized by the features described herein. (Note 3) Features a visualization section for self-growth. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned timeline view section is It estimates the user's emotions and adjusts how the timeline is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned timeline view section is Dynamically change the display order of past events based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned timeline view section is Analyze the user's behavior history and highlight the most relevant events. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned timeline view section is It estimates the user's emotions and changes the timeline's color scheme and design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned timeline view section is The system highlights events in specific locations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned timeline view section is Analyze users' social media activity and add relevant events to their timeline. The system described in Appendix 1, characterized by the features described herein. (Note 10) The diary section is, It estimates the user's emotions and adjusts the diary's input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The diary section is, It analyzes the user's past diary entries and suggests the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The diary section is, When recording entries in the diary, tags are automatically added based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 13) The diary section is, It estimates the user's emotions and adjusts the frequency of diary entries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The diary section is, The system automatically records events at specific locations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The diary section is, Analyze users' social media activity and automatically add relevant events to their diary. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned future prediction scenario section is: It estimates the user's emotions and adjusts the content of future scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned future prediction scenario section is: It analyzes users' past behavior patterns and generates the most realistic future scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned future prediction scenario section is: When generating future prediction scenarios, the user's current goals and plans are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned future prediction scenario section is: It estimates user emotions and prioritizes future scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned future prediction scenario section is: It generates future scenarios for a specific location, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned future prediction scenario section is: Analyze users' social media activity and generate relevant future scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned special event scenario department, The system estimates the user's emotions and adjusts the content of special event scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned special event scenario department, We analyze the user's past special event history and propose the optimal scenario. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned special event scenario department, When generating special event scenarios, consider the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned special event scenario department, The system estimates user emotions and prioritizes special event scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned special event scenario department, Considering the user's geographical location, generate special event scenarios for specific locations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned special event scenario department, Analyze users' social media activity and generate relevant special event scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned emotion analysis and feedback unit, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned emotion analysis and feedback unit, Analyze the user's past conversation history to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned emotion analysis and feedback unit, When providing feedback, we take the user's current situation into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned emotion analysis and feedback unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned emotion analysis and feedback unit, Providing feedback tailored to specific locations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emotion analysis and feedback unit, Analyze users' social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned avatar customization unit is It estimates the user's emotions and automatically adjusts the avatar's appearance based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned avatar customization unit is We analyze the user's past avatar customization history and suggest the optimal customization options. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned avatar customization unit is It estimates the user's emotions and adjusts the avatar's movements and facial expressions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned avatar customization unit is The avatar's appearance will change depending on the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned self-growth visualization unit is, We estimate the user's emotions and adjust the visualization method of self-growth based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned self-growth visualization unit is, We analyze users' past growth data and propose the optimal visualization method. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned self-growth visualization unit is, It estimates the user's emotions and prioritizes visualizations of self-growth based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned self-growth visualization unit is, Visualize self-growth in specific locations, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The timeline view section allows you to see past, present, and future events at a glance, The diary section, where daily events are recorded, A future prediction scenario unit generates future scenarios based on events recorded by the diary unit, The Special Event Scenario Department provides special dialogue scenarios tailored to special events, It includes an emotion analysis and feedback unit that analyzes user interactions, understands emotions, and provides appropriate feedback. A system characterized by the following features.

2. Features an avatar customization section. The system according to feature 1.

3. Features a visualization section for self-growth. The system according to feature 1.

4. The aforementioned timeline view section is It estimates the user's emotions and adjusts how the timeline is displayed based on those estimated emotions. The system according to feature 1.

5. The aforementioned timeline view section is Dynamically change the display order of past events based on their importance. The system according to feature 1.

6. The aforementioned timeline view section is Analyze the user's behavior history and highlight the most relevant events. The system according to feature 1.

7. The aforementioned timeline view section is It estimates the user's emotions and changes the timeline's color scheme and design based on those estimated emotions. The system according to feature 1.

8. The aforementioned timeline view section is The system highlights events in specific locations, taking into account the user's geographical location. The system according to feature 1.

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