System

Abstract

An object of a system according to an embodiment is to propose a shared experience with a family member or a friend on the basis of a lifestyle or an interest of a user and record the shared experience.SOLUTION: A system includes a lifestyle analysis unit, a proposal unit, and a recording tool providing unit. The lifestyle analysis unit analyzes a lifestyle or interest of a user. The proposal unit proposes a sharing experience with a family member or a friend on the basis of a result of the analysis by the lifestyle analysis unit. The recording tool providing unit provides a recording tool for recording the shared experience proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to effectively suggest and record valuable moments in everyday life and share experiences with family and friends.

[0005] The system according to the embodiment aims to suggest and record experiences to share with family and friends based on the user's lifestyle and interests. [Means for solving the problem]

[0006] A system according to an embodiment includes a lifestyle analysis unit, a suggestion unit, and a recording tool providing unit. The lifestyle analysis unit analyzes a user's lifestyle or interests. The suggestion unit suggests experiences to share with family or friends based on the results of the analysis by the lifestyle analysis unit. The recording tool providing unit provides a recording tool for recording the shared experiences suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest and record shared experiences with family and friends based on the user's lifestyle and interests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Memory Maker AI system according to an embodiment of the present invention is a system that suggests shared experiences with family and friends based on the user's lifestyle and interests, and provides tools for recording those experiences, allowing the user to enjoy special moments in their daily lives without missing them.

[0029] The memory maker AI system according to the embodiment includes a lifestyle analysis unit, a suggestion unit, and a recording tool providing unit. The lifestyle analysis unit analyzes a user's lifestyle or interests. For example, the lifestyle analysis unit analyzes the user's hobbies and activities to extract lifestyle patterns. The lifestyle analysis unit can also analyze the user's past behavioral history and track changes in the user's interests. The lifestyle analysis unit can also use an emotion estimation function to analyze changes in the user's emotions and make lifestyle suggestions based on the emotions. The suggestion unit suggests shared experiences with family or friends based on the results of the analysis by the lifestyle analysis unit. For example, the suggestion unit makes specific suggestions such as, "Why don't you go on a family picnic today?" or "Let's cook together with friends." The suggestion unit can also make suggestions at optimal times, taking into account the user's current mood and physical condition. The suggestion unit can also use the emotion estimation function to predict the user's emotional reaction to the proposed shared experiences and prioritize suggestions that are expected to elicit a positive reaction. The recording tool providing unit provides a recording tool for recording the shared experiences suggested by the suggestion unit. For example, the recording tool providing unit may provide functions such as taking photos and videos, keeping a diary, and recording shared experiences on a calendar. The recording tool providing unit may also provide a function that automatically adds captions and tags to recorded photos and videos using a generation AI. The recording tool providing unit may also provide a function that automatically organizes recorded memories and generates albums for specific themes or events. This allows the Memory Maker AI system according to the embodiment to ensure users do not miss out on special moments in their daily lives. For example, users can deepen bonds with family and friends through shared experiences. Furthermore, discovering hidden value in small everyday moments can make each day special.

[0030] The lifestyle analysis unit can analyze the user's life log data and extract detailed lifestyle patterns. The lifestyle analysis unit, for example, analyzes heart rate and step count data obtained from a smartwatch to understand the user's exercise habits and health condition. For example, the lifestyle analysis unit analyzes the user's lifestyle in detail based on the amount of daily exercise and sleep patterns. The lifestyle analysis unit can also analyze data from a fitness tracker to understand the user's activity level and calorie consumption. For example, the lifestyle analysis unit can analyze the user's exercise patterns and eating habits based on the fitness tracker data. The lifestyle analysis unit can also analyze the smartphone's location information data to understand the user's movement patterns and visited places. For example, the location information data can be used to analyze the user's commute route and how they spend their weekends. In this way, by analyzing the user's life log data, more detailed lifestyle patterns can be extracted.

[0031] The lifestyle analysis unit can analyze a user's past SNS posts or photos to track changes in their interests. For example, the lifestyle analysis unit analyzes a user's SNS posts and extracts their interests from the content of the posts and hashtags. For example, it identifies themes such as travel, cooking, and sports and tracks changes in the user's interests. The lifestyle analysis unit can also analyze a user's past photos and identify their interests using image analysis technology. For example, it can identify the user's hobbies and activities based on the content and location of the photos. The lifestyle analysis unit can also analyze a user's SNS posts and photos in combination to track changes in their interests in more detail. For example, it can analyze the relevance between the content of the posts and the photos to comprehensively understand changes in the user's interests. In this way, it is possible to track changes in the user's interests by analyzing a user's past SNS posts and photos.

[0032] The lifestyle analysis unit can incorporate dialogue data with the voice assistant and extract interests and concerns from everyday conversations. The lifestyle analysis unit, for example, analyzes dialogue data with the voice assistant and extracts interests and concerns from the user's everyday conversations. For example, interests can be identified based on themes frequently discussed by the user or the content of questions. The lifestyle analysis unit can also analyze dialogue data with the voice assistant and analyze the user's emotions and tone. For example, the user's interests and concerns can be identified based on positive or negative tones. The lifestyle analysis unit can also analyze dialogue data with the voice assistant and understand the user's behavioral patterns and habits. For example, the lifestyle analysis unit can analyze the user's lifestyle based on the content of conversations at specific times of day or in specific places. In this way, interests and concerns can be extracted from everyday conversations by analyzing dialogue data with the voice assistant.

[0033] The lifestyle analysis unit can analyze data of users of different age groups or cultural backgrounds and make suggestions that take into account the diversity of lifestyles. The lifestyle analysis unit, for example, analyzes data of users of different age groups and makes suggestions that take into account the diversity of lifestyles. For example, it understands the differences in lifestyles between young people and elderly people and makes suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users of different cultural backgrounds and make suggestions that take into account the diversity of lifestyles. For example, it understands the lifestyles of users of different nationalities or religions and makes suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users in different regions and make suggestions that take into account the diversity of lifestyles. For example, it understands the differences in lifestyles between urban and rural areas and makes suggestions that are appropriate for each. In this way, by analyzing data of different age groups and cultural backgrounds, it becomes possible to make suggestions that take into account the diversity of lifestyles.

[0034] The suggestion unit can analyze the user's history of past shared experiences, extract patterns of successful experiences, and reflect them in suggestions. The suggestion unit, for example, analyzes the user's history of past shared experiences and extracts patterns of successful experiences. For example, suggestions are made based on positive experiences such as family trips or events with friends. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of unsuccessful experiences. For example, similar suggestions are avoided based on experiences that the user did not enjoy. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of success under specific conditions. For example, suggestions are made based on successful experiences in specific seasons or weather conditions. In this way, by analyzing the history of past shared experiences, patterns of successful experiences can be extracted and reflected in suggestions.

[0035] The suggestion unit can make suggestions at the optimal timing, taking into account the user's current mood or physical condition. The suggestion unit, for example, analyzes the user's current mood or physical condition, and suggests a shared experience at the optimal timing based on that. For example, it suggests a relaxing experience when the user is relaxed. The suggestion unit can also analyze the user's current mood or physical condition, and suggest an experience that reduces stress. For example, it suggests an experience that helps the user relax when the user is feeling stressed. The suggestion unit can also analyze the user's current mood or physical condition, and suggest an experience that increases energy. For example, it suggests an experience that will refresh the user when the user is tired. This makes it possible to make suggestions at the optimal timing by taking into account the user's current mood and physical condition.

[0036] The suggestion unit can incorporate the user's geographical location information to suggest nearby events or activities. The suggestion unit, for example, can suggest nearby events or activities based on the user's geographical location information. For example, it can suggest events held near the user's current location. The suggestion unit can also suggest activities in specific locations based on the user's geographical location information. For example, it can suggest tourist spots and leisure facilities near the user's current location. The suggestion unit can also suggest events and activities within a range of movement based on the user's geographical location information. For example, it can suggest events held within a certain distance from the user's current location. In this way, by incorporating the user's geographical location information, nearby events and activities can be suggested.

[0037] The suggestion unit can suggest shared experiences according to different seasons or weather conditions, and make suggestions for enjoying the sense of the seasons. The suggestion unit, for example, suggests shared experiences according to different seasons. For example, suggestions for enjoying the sense of the seasons, such as cherry blossom viewing in spring, swimming in the sea in summer, leaf-viewing in autumn, and skiing in winter. The suggestion unit can also suggest shared experiences according to different weather conditions. For example, outdoor activities can be suggested on sunny days, and indoor activities on rainy days. The suggestion unit can also suggest special events or activities according to the seasons or weather conditions. For example, seasonal festivals or workshops according to the weather can be suggested. In this way, suggestions according to different seasons or weather conditions can be made, allowing the sense of the seasons to be enjoyed.

[0038] The recording tool providing unit can provide a function to automatically add captions or tags to recorded photos or videos using a generation AI. The recording tool providing unit, for example, automatically generates captions for recorded photos using a generation AI. For example, it analyzes the content of the photo and adds captions such as "family picnic" or "barbecue with friends." The recording tool providing unit can also automatically generate captions for recorded videos using a generation AI. For example, it analyzes the content of the video and adds captions such as "travel memories" or "event highlights." The recording tool providing unit can also automatically add tags to recorded photos and videos using a generation AI. For example, it analyzes the content of the photo or video and adds tags such as "travel," "family," and "friends." This allows captions and tags to be automatically added to recorded photos and videos using a generation AI.

[0039] The recording tool providing unit can provide a function for automatically organizing recorded memories and generating albums for specific themes or events. For example, the recording tool providing unit automatically organizes recorded photos and videos and generates albums for specific themes or events. For example, albums such as "family trips," "parties with friends," and "sports events" are automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific time periods. For example, albums such as "2023 memories" or "summer memories" are automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific locations. For example, albums such as "memories of travel destinations" or "memories at home" are automatically generated. This allows recorded memories to be automatically organized and albums to be generated for specific themes or events.

[0040] The recording tool providing unit can add a voice memo function to the recording tool, allowing the user to record by voice what they feel on the spot. The recording tool providing unit, for example, adds a voice memo function to the recording tool, allowing the user to record by voice what they feel on the spot. For example, impressions at a travel destination or emotions at an event are recorded by voice. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot in real time. For example, emotions and memories felt at a specific moment are recorded in real time as voice memos. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot as a memo to review later. For example, impressions about a specific event or activity are recorded as voice memos to review later. In this way, by adding the voice memo function, the user can record by voice what they feel on the spot.

[0041] The recording tool providing unit can provide a private shared album function for sharing recorded memories with family or friends. The recording tool providing unit, for example, provides a private shared album function for sharing recorded memories with family and friends. For example, an album that can only be accessed by specific members can be created. The recording tool providing unit can also use the private shared album function to share memories of specific events or activities. For example, memories of a family trip or a party with friends can be shared. The recording tool providing unit can also use the private shared album function to share memories for specific periods of time. For example, "Memories of 2023" or "Summer Memories" can be shared with family and friends. In this way, by providing the private shared album function, recorded memories can be shared with family and friends.

[0042] The lifestyle analysis unit can analyze the user's daily behavioral patterns and find small moments that occur at specific times or places. The lifestyle analysis unit, for example, analyzes the user's daily behavioral patterns and finds small moments that occur at specific times or places. For example, it identifies special moments in daily life, such as a morning coffee break or an evening walk. The lifestyle analysis unit can also analyze the user's behavioral patterns and find small moments related to specific activities or events. For example, it identifies moments related to specific hobbies or activities. The lifestyle analysis unit can also analyze the user's behavioral patterns and find small moments related to specific emotions or moods. For example, it identifies moments when the user can relax or have fun. In this way, by analyzing the user's daily behavioral patterns, it is possible to find small moments that occur at specific times or places.

[0043] The lifestyle analysis unit can predict and suggest particularly valuable moments in daily life based on the user's past behavioral history. The lifestyle analysis unit, for example, analyzes the user's past behavioral history to predict particularly valuable moments in daily life. For example, based on moments in which positive emotions were expressed in the past, it predicts and suggests similar moments. The lifestyle analysis unit can also analyze the user's past behavioral history to predict valuable moments under specific conditions. For example, it predicts valuable moments in specific seasons or weather. The lifestyle analysis unit can also analyze the user's past behavioral history to predict valuable moments related to specific activities or events. For example, it predicts moments related to specific hobbies or activities. In this way, it is possible to predict and suggest particularly valuable moments in daily life based on the user's past behavioral history.

[0044] The lifestyle analysis unit can incorporate the growth records of the user's pets or plants to discover the value of small everyday moments. The lifestyle analysis unit, for example, incorporates the growth records of the user's pets to discover the value of small everyday moments. For example, it records the pet's growth process and specific behaviors and suggests valuable moments based on the data. The lifestyle analysis unit can also incorporate the growth records of the user's plants to discover the value of small everyday moments. For example, it records the plant's growth process and flowering moments and suggests valuable moments based on the data. The lifestyle analysis unit can also analyze the growth records of the user's pets or plants to discover valuable moments under specific conditions. For example, it suggests valuable moments based on growth records in specific seasons or weather conditions. In this way, the value of small everyday moments can be discovered by incorporating the growth records of the user's pets or plants.

[0045] The lifestyle analysis unit suggests small everyday moments related to the user's hobbies or interests, allowing the user to find value through hobby activities. The lifestyle analysis unit, for example, suggests small everyday moments related to the user's hobbies or interests. For example, for a user whose hobby is gardening, it suggests moments of observing plants growing. The lifestyle analysis unit can also suggest events or activities related to the user's hobbies or interests. For example, for a user whose hobby is music, it suggests concerts or live events. The lifestyle analysis unit can also suggest places or time periods related to the user's hobbies or interests. For example, for a user whose hobby is photography, it suggests taking photos at specific places or time periods. In this way, by suggesting small everyday moments related to the user's hobbies or interests, it is possible to find value through hobby activities.

[0046] The suggestion unit can analyze the user's behavior history and optimize reminders to increase the rate at which the proposed shared experience is performed. The suggestion unit, for example, analyzes the user's behavior history and optimizes reminders to increase the rate at which the proposed shared experience is performed. For example, the suggestion unit may send reminders at times when the user is likely to forget. The suggestion unit can also analyze the user's behavior history and optimize reminders under specific conditions. For example, the suggestion unit may optimize reminders for specific times of day or locations. The suggestion unit can also analyze the user's behavior history and optimize the content and format of reminders. For example, the suggestion unit may send reminders in a format that is optimal for the user, such as a text message or a push notification. In this way, by analyzing the user's behavior history, it is possible to optimize reminders to increase the rate at which the proposed shared experience is performed.

[0047] The suggestion unit can establish a feedback loop for collecting feedback on the user's behavior and reflecting it in the next suggestion. The suggestion unit, for example, establishes a feedback loop for collecting feedback on the user's behavior and reflecting it in the next suggestion. For example, the result of the user acting according to the suggestion is recorded and reflected in the next suggestion. The suggestion unit can also collect feedback on the user's behavior in real time and reflect it in the next suggestion. For example, the suggestion unit adjusts the suggestion based on the real-time feedback. The suggestion unit can also analyze feedback on the user's behavior and establish a feedback loop for improving the accuracy of the suggestion. For example, the suggestion unit improves the suggestion based on positive feedback. In this way, a feedback loop can be established for collecting feedback on the user's behavior and reflecting it in the next suggestion.

[0048] The suggestion unit can provide a function to automatically list preparations or procedures required for a proposed shared experience to support user actions. The suggestion unit, for example, provides a function to automatically list preparations and procedures required for a proposed shared experience. For example, the suggestion unit lists items and procedures required for a picnic. The suggestion unit can also optimize the preparations and procedures required for a proposed shared experience based on the user's behavior history. For example, the suggestion unit lists preparations and procedures that are optimal for the user based on past experiences. The suggestion unit can also update the preparations and procedures required for a proposed shared experience in real time. For example, the suggestion unit updates the required items and procedures depending on changes in weather or conditions. In this way, the suggestion unit can support user actions by providing a function to automatically list preparations and procedures required for a proposed shared experience.

[0049] The suggestion unit can provide a tutorial or guide related to the proposed shared experience to support the user's actions. For example, the suggestion unit can provide a tutorial or guide related to the proposed shared experience. For example, the suggestion unit can provide a guide that provides detailed instructions for cooking recipes or steps. The suggestion unit can also provide a video tutorial related to the proposed shared experience. For example, the suggestion unit can provide a video tutorial about a specific activity or event. The suggestion unit can also provide an interactive guide related to the proposed shared experience. For example, the suggestion unit can provide an interactive guide that the user progresses through by answering questions. This makes it possible to support the user's actions by providing a tutorial or guide related to the proposed shared experience.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The lifestyle analysis unit can analyze the user's life log data and extract detailed lifestyle patterns. For example, it can analyze heart rate and step count data obtained from a smartwatch to understand the user's exercise habits and health condition. For example, it can analyze the user's lifestyle in detail based on the amount of daily exercise and sleep patterns. The lifestyle analysis unit can also analyze data from a fitness tracker to understand the user's activity level and calorie consumption. For example, it can analyze the user's exercise patterns and eating habits based on the fitness tracker data. The lifestyle analysis unit can also analyze the smartphone's location information data to understand the user's movement patterns and visited places. For example, it can analyze the user's commute route and how they spend their weekends based on the location information data. In this way, by analyzing the user's life log data, more detailed lifestyle patterns can be extracted.

[0052] The lifestyle analysis unit can analyze a user's past SNS posts or photos to track changes in their interests. For example, it can analyze a user's SNS posts and extract their interests from the content of the posts and hashtags. For example, it can identify themes such as travel, cooking, and sports and track changes in the user's interests. The lifestyle analysis unit can also analyze a user's past photos and identify their interests using image analysis technology. For example, it can identify the user's hobbies and activities based on the content of the photos and the locations where they were taken. The lifestyle analysis unit can also analyze a user's SNS posts and photos in combination to track changes in their interests in more detail. For example, it can analyze the relevance between the content of the posts and the photos to comprehensively understand changes in the user's interests. This makes it possible to track changes in the user's interests by analyzing a user's past SNS posts and photos.

[0053] The lifestyle analysis unit can incorporate dialogue data with the voice assistant and extract interests from everyday conversations. For example, the dialogue data with the voice assistant can be analyzed to extract interests from the user's everyday conversations. For example, interests can be identified based on themes frequently discussed by the user or the content of questions. The lifestyle analysis unit can also analyze the dialogue data with the voice assistant to analyze the user's emotions and tone. For example, the user's interests can be identified based on positive or negative tones. The lifestyle analysis unit can also analyze the dialogue data with the voice assistant to understand the user's behavioral patterns and habits. For example, the lifestyle analysis unit can analyze the user's lifestyle based on the content of conversations at specific times of day or in specific places. In this way, interests can be extracted from everyday conversations by analyzing the dialogue data with the voice assistant.

[0054] The lifestyle analysis unit can analyze data of users of different age groups or cultural backgrounds and make suggestions that take into account the diversity of lifestyles. For example, it can analyze data of users of different age groups and make suggestions that take into account the diversity of lifestyles. For example, it can grasp the differences in lifestyles between young people and elderly people and make suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users of different cultural backgrounds and make suggestions that take into account the diversity of lifestyles. For example, it can grasp the lifestyles of users of different nationalities or religions and make suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users in different regions and make suggestions that take into account the diversity of lifestyles. For example, it can grasp the differences in lifestyles between urban and rural areas and make suggestions that are appropriate for each. In this way, by analyzing data of different age groups and cultural backgrounds, it becomes possible to make suggestions that take into account the diversity of lifestyles.

[0055] The suggestion unit can analyze the user's history of past shared experiences, extract patterns of successful experiences, and reflect them in suggestions. For example, the suggestion unit can analyze the user's history of past shared experiences and extract patterns of successful experiences. For example, suggestions are made based on positive experiences such as family trips or events with friends. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of unsuccessful experiences. For example, similar suggestions are avoided based on experiences that the user did not enjoy. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of success under specific conditions. For example, suggestions are made based on successful experiences in specific seasons or weather conditions. In this way, by analyzing the history of past shared experiences, patterns of successful experiences can be extracted and reflected in suggestions.

[0056] The suggestion unit can incorporate the user's geographical location information to suggest nearby events or activities. For example, nearby events and activities can be suggested based on the user's geographical location information. For example, events held near the user's current location can be suggested. The suggestion unit can also suggest activities at specific locations based on the user's geographical location information. For example, tourist spots and leisure facilities near the user's current location can be suggested. The suggestion unit can also suggest events and activities within a range of travel based on the user's geographical location information. For example, events held within a certain distance from the user's current location can be suggested. In this way, by incorporating the user's geographical location information, nearby events and activities can be suggested.

[0057] The suggestion unit can suggest shared experiences according to different seasons or weather conditions, and make suggestions for enjoying the sense of the seasons. For example, the suggestion unit can suggest shared experiences according to different seasons. For example, suggestions for enjoying the sense of the seasons, such as cherry blossom viewing in spring, swimming in the sea in summer, leaf viewing in autumn, and skiing in winter, can be made. The suggestion unit can also suggest shared experiences according to different weather conditions. For example, outdoor activities can be suggested on sunny days, and indoor activities on rainy days. The suggestion unit can also suggest special events or activities according to the seasons or weather conditions. For example, seasonal festivals or workshops according to the weather can be suggested. In this way, suggestions according to different seasons or weather conditions can be made, allowing the sense of the seasons to be enjoyed.

[0058] The recording tool providing unit can provide a function to automatically add captions or tags to recorded photos or videos using a generation AI. For example, the recording tool providing unit can automatically generate captions for recorded photos using a generation AI. For example, the content of the photo can be analyzed and captions such as "family picnic" or "barbecue with friends" can be added. The recording tool providing unit can also automatically generate captions for recorded videos using a generation AI. For example, the content of the video can be analyzed and captions such as "travel memories" or "event highlights" can be added. The recording tool providing unit can also automatically add tags to recorded photos and videos using a generation AI. For example, the content of the photo or video can be analyzed and tags such as "travel," "family," and "friends" can be added. This allows captions and tags to be automatically added to recorded photos and videos using a generation AI.

[0059] The recording tool providing unit can provide a function to automatically organize recorded memories and generate albums for specific themes or events. For example, the recording tool providing unit can automatically organize recorded photos and videos and generate albums for specific themes or events. For example, albums such as "family trips," "parties with friends," and "sports events" can be automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific time periods. For example, albums such as "2023 memories" or "summer memories" can be automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific locations. For example, albums such as "memories of travel destinations" or "memories at home" can be automatically generated. This allows the recording tool providing unit to automatically organize recorded memories and generate albums for specific themes or events.

[0060] The recording tool providing unit can add a voice memo function to the recording tool, allowing the user to record by voice what they feel on the spot. For example, the voice memo function can be added to the recording tool, allowing the user to record by voice what they feel on the spot. For example, impressions at a travel destination or emotions at an event can be recorded by voice. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot in real time. For example, emotions and memories felt at a specific moment can be recorded in real time as voice memos. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot as a memo to review later. For example, impressions about a specific event or activity can be recorded as voice memos to review later. In this way, adding the voice memo function allows the user to record by voice what they feel on the spot.

[0061] The recording tool providing unit can provide a private shared album function for sharing recorded memories with family or friends. For example, a private shared album function for sharing recorded memories with family and friends can be provided. For example, an album that can only be accessed by specific members can be created. The recording tool providing unit can also use the private shared album function to share memories of specific events or activities. For example, memories of a family trip or a party with friends can be shared. The recording tool providing unit can also use the private shared album function to share memories for specific periods of time. For example, "Memories of 2023" or "Summer Memories" can be shared with family and friends. In this way, by providing the private shared album function, recorded memories can be shared with family and friends.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The lifestyle analysis unit analyzes the user's lifestyle or interests. For example, it analyzes the user's hobbies and activities to extract lifestyle patterns. It can also analyze past behavioral history and track changes in interests and concerns. Furthermore, it can use the emotion estimation function to analyze changes in the user's emotions and make lifestyle suggestions based on those emotions. Step 2: The suggestion unit suggests experiences to share with family or friends based on the results of the analysis by the lifestyle analysis unit. For example, it makes specific suggestions such as "Why don't you go on a family picnic today?" or "Let's cook together with friends." It can also make suggestions at the optimal time by taking into account the user's current mood and physical condition. Furthermore, it can use its emotion estimation function to predict the user's emotional reaction to the suggested shared experiences and prioritize suggestions that are likely to elicit a positive reaction. Step 3: The recording tool provider provides recording tools for recording the shared experiences suggested by the suggestion unit. For example, it provides functions for taking photos and videos, keeping a diary, and recording the shared experiences in a calendar. It also provides functions for automatically adding captions and tags using generative AI, and for automatically organizing the recorded memories and generating albums for specific themes or events.

[0064] (Example 2) The Memory Maker AI system according to an embodiment of the present invention is a system that suggests shared experiences with family and friends based on the user's lifestyle and interests, and provides tools for recording those experiences, allowing the user to enjoy special moments in their daily lives without missing them.

[0065] The memory maker AI system according to the embodiment includes a lifestyle analysis unit, a suggestion unit, and a recording tool providing unit. The lifestyle analysis unit analyzes a user's lifestyle or interests. For example, the lifestyle analysis unit analyzes the user's hobbies and activities to extract lifestyle patterns. The lifestyle analysis unit can also analyze the user's past behavioral history and track changes in the user's interests. The lifestyle analysis unit can also use an emotion estimation function to analyze changes in the user's emotions and make lifestyle suggestions based on the emotions. The suggestion unit suggests shared experiences with family or friends based on the results of the analysis by the lifestyle analysis unit. For example, the suggestion unit makes specific suggestions such as, "Why don't you go on a family picnic today?" or "Let's cook together with friends." The suggestion unit can also make suggestions at optimal times, taking into account the user's current mood and physical condition. The suggestion unit can also use the emotion estimation function to predict the user's emotional reaction to the proposed shared experiences and prioritize suggestions that are expected to elicit a positive reaction. The recording tool providing unit provides a recording tool for recording the shared experiences suggested by the suggestion unit. For example, the recording tool providing unit may provide functions such as taking photos and videos, keeping a diary, and recording shared experiences on a calendar. The recording tool providing unit may also provide a function that automatically adds captions and tags to recorded photos and videos using a generation AI. The recording tool providing unit may also provide a function that automatically organizes recorded memories and generates albums for specific themes or events. This allows the Memory Maker AI system according to the embodiment to ensure users do not miss out on special moments in their daily lives. For example, users can deepen bonds with family and friends through shared experiences. Furthermore, discovering hidden value in small everyday moments can make each day special.

[0066] The lifestyle analysis unit can analyze the user's life log data and extract detailed lifestyle patterns. The lifestyle analysis unit, for example, analyzes heart rate and step count data obtained from a smartwatch to understand the user's exercise habits and health condition. For example, the lifestyle analysis unit analyzes the user's lifestyle in detail based on the amount of daily exercise and sleep patterns. The lifestyle analysis unit can also analyze data from a fitness tracker to understand the user's activity level and calorie consumption. For example, the lifestyle analysis unit can analyze the user's exercise patterns and eating habits based on the fitness tracker data. The lifestyle analysis unit can also analyze the smartphone's location information data to understand the user's movement patterns and visited places. For example, the location information data can be used to analyze the user's commute route and how they spend their weekends. In this way, by analyzing the user's life log data, more detailed lifestyle patterns can be extracted.

[0067] The lifestyle analysis unit can analyze a user's past SNS posts or photos to track changes in their interests. For example, the lifestyle analysis unit analyzes a user's SNS posts and extracts their interests from the content of the posts and hashtags. For example, it identifies themes such as travel, cooking, and sports and tracks changes in the user's interests. The lifestyle analysis unit can also analyze a user's past photos and identify their interests using image analysis technology. For example, it can identify the user's hobbies and activities based on the content and location of the photos. The lifestyle analysis unit can also analyze a user's SNS posts and photos in combination to track changes in their interests in more detail. For example, it can analyze the relevance between the content of the posts and the photos to comprehensively understand changes in the user's interests. In this way, it is possible to track changes in the user's interests by analyzing a user's past SNS posts and photos.

[0068] The lifestyle analysis unit can use the emotion estimation function to analyze changes in emotions from the user's past actions or comments and suggest lifestyles based on those emotions. The lifestyle analysis unit, for example, analyzes the user's past SNS posts and messages and identifies changes in emotions using the emotion estimation function. For example, it identifies periods when the user has more positive emotions and periods when the user has more negative emotions. The lifestyle analysis unit can also analyze the user's past behavioral history and identify changes in emotions using the emotion estimation function. For example, it analyzes changes in emotions in response to specific events or activities. The lifestyle analysis unit can also analyze the user's past comments and identify changes in emotions using the emotion estimation function. For example, it analyzes changes in emotions based on the content and tone of the comments. In this way, the emotion estimation function can be used to analyze changes in the user's emotions and suggest lifestyles based on those emotions.

[0069] The lifestyle analysis unit can incorporate dialogue data with the voice assistant and extract interests and concerns from everyday conversations. The lifestyle analysis unit, for example, analyzes dialogue data with the voice assistant and extracts interests and concerns from the user's everyday conversations. For example, interests can be identified based on themes frequently discussed by the user or the content of questions. The lifestyle analysis unit can also analyze dialogue data with the voice assistant and analyze the user's emotions and tone. For example, the user's interests and concerns can be identified based on positive or negative tones. The lifestyle analysis unit can also analyze dialogue data with the voice assistant and understand the user's behavioral patterns and habits. For example, the lifestyle analysis unit can analyze the user's lifestyle based on the content of conversations at specific times of day or in specific places. In this way, interests and concerns can be extracted from everyday conversations by analyzing dialogue data with the voice assistant.

[0070] The lifestyle analysis unit can analyze data of users of different age groups or cultural backgrounds and make suggestions that take into account the diversity of lifestyles. The lifestyle analysis unit, for example, analyzes data of users of different age groups and makes suggestions that take into account the diversity of lifestyles. For example, it understands the differences in lifestyles between young people and elderly people and makes suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users of different cultural backgrounds and make suggestions that take into account the diversity of lifestyles. For example, it understands the lifestyles of users of different nationalities or religions and makes suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users in different regions and make suggestions that take into account the diversity of lifestyles. For example, it understands the differences in lifestyles between urban and rural areas and makes suggestions that are appropriate for each. In this way, by analyzing data of different age groups and cultural backgrounds, it becomes possible to make suggestions that take into account the diversity of lifestyles.

[0071] The lifestyle analysis unit can use the emotion estimation function to analyze the emotions a user feels when performing a specific activity in real time and suggest activities that elicit positive emotions. The lifestyle analysis unit, for example, uses the emotion estimation function to analyze the emotions a user feels when performing a specific activity in real time. For example, it analyzes emotions when exercising or engaging in a hobby, and suggests activities that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze the emotions a user feels in a specific place in real time. For example, it analyzes emotions in a park or a cafe, and suggests places that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze the emotions a user feels at a specific time of day in real time. For example, it analyzes emotions in the morning or evening, and suggests time periods that elicit positive emotions. In this way, the emotion estimation function can be used to analyze the emotions a user feels when performing a specific activity in real time and suggest activities that elicit positive emotions.

[0072] The suggestion unit can analyze the user's history of past shared experiences, extract patterns of successful experiences, and reflect them in suggestions. The suggestion unit, for example, analyzes the user's history of past shared experiences and extracts patterns of successful experiences. For example, suggestions are made based on positive experiences such as family trips or events with friends. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of unsuccessful experiences. For example, similar suggestions are avoided based on experiences that the user did not enjoy. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of success under specific conditions. For example, suggestions are made based on successful experiences in specific seasons or weather conditions. In this way, by analyzing the history of past shared experiences, patterns of successful experiences can be extracted and reflected in suggestions.

[0073] The suggestion unit can make suggestions at the optimal timing, taking into account the user's current mood or physical condition. The suggestion unit, for example, analyzes the user's current mood or physical condition, and suggests a shared experience at the optimal timing based on that. For example, it suggests a relaxing experience when the user is relaxed. The suggestion unit can also analyze the user's current mood or physical condition, and suggest an experience that reduces stress. For example, it suggests an experience that helps the user relax when the user is feeling stressed. The suggestion unit can also analyze the user's current mood or physical condition, and suggest an experience that increases energy. For example, it suggests an experience that will refresh the user when the user is tired. This makes it possible to make suggestions at the optimal timing by taking into account the user's current mood and physical condition.

[0074] The suggestion unit can use the emotion estimation function to predict the user's emotional response to the proposed shared experience and prioritize suggestions that are expected to elicit a positive response. The suggestion unit, for example, uses the emotion estimation function to predict the user's emotional response to the proposed shared experience. For example, it prioritizes suggestions of experiences that are expected to elicit a positive response based on past data. The suggestion unit can also use the emotion estimation function to predict a negative response to the proposed shared experience and make suggestions that avoid that. For example, it avoids experiences that are expected to elicit a negative response based on past data. The suggestion unit can also use the emotion estimation function to monitor the emotional response to the proposed shared experience in real time and modify the suggestion as needed. For example, it analyzes the user's emotional response in real time and adjusts the suggestion to elicit a positive response. In this way, the emotion estimation function can predict the user's emotional response to the proposed shared experience and prioritize suggestions that are expected to elicit a positive response.

[0075] The suggestion unit can incorporate the user's geographical location information to suggest nearby events or activities. The suggestion unit, for example, can suggest nearby events or activities based on the user's geographical location information. For example, it can suggest events held near the user's current location. The suggestion unit can also suggest activities in specific locations based on the user's geographical location information. For example, it can suggest tourist spots and leisure facilities near the user's current location. The suggestion unit can also suggest events and activities within a range of movement based on the user's geographical location information. For example, it can suggest events held within a certain distance from the user's current location. In this way, by incorporating the user's geographical location information, nearby events and activities can be suggested.

[0076] The suggestion unit can suggest shared experiences according to different seasons or weather conditions, and make suggestions for enjoying the sense of the seasons. The suggestion unit, for example, suggests shared experiences according to different seasons. For example, suggestions for enjoying the sense of the seasons, such as cherry blossom viewing in spring, swimming in the sea in summer, leaf-viewing in autumn, and skiing in winter. The suggestion unit can also suggest shared experiences according to different weather conditions. For example, outdoor activities can be suggested on sunny days, and indoor activities on rainy days. The suggestion unit can also suggest special events or activities according to the seasons or weather conditions. For example, seasonal festivals or workshops according to the weather can be suggested. In this way, suggestions according to different seasons or weather conditions can be made, allowing the sense of the seasons to be enjoyed.

[0077] The suggestion unit can use the emotion estimation function to consider the emotional reactions of family or friends to the proposed shared experience and suggest an experience that everyone can enjoy. For example, the suggestion unit can use the emotion estimation function to predict the emotional reactions of family and friends to the proposed shared experience and suggest an experience that everyone can enjoy. For example, the suggestion unit can suggest an experience that everyone will have a positive reaction to based on past data. The suggestion unit can also use the emotion estimation function to predict the negative reactions of family and friends to the proposed shared experience and make a suggestion to avoid that. For example, the suggestion unit can avoid an experience that is predicted to have a negative reaction based on past data. The suggestion unit can also use the emotion estimation function to monitor the emotional reactions of family and friends to the proposed shared experience in real time and modify the suggestion as needed. For example, the suggestion unit can analyze the emotional reactions of family and friends in real time and adjust the suggestion so that everyone can enjoy it. In this way, the emotion estimation function can consider the emotional reactions of family and friends and suggest an experience that everyone can enjoy.

[0078] The recording tool providing unit can provide a function to automatically add captions or tags to recorded photos or videos using a generation AI. The recording tool providing unit, for example, automatically generates captions for recorded photos using a generation AI. For example, it analyzes the content of the photo and adds captions such as "family picnic" or "barbecue with friends." The recording tool providing unit can also automatically generate captions for recorded videos using a generation AI. For example, it analyzes the content of the video and adds captions such as "travel memories" or "event highlights." The recording tool providing unit can also automatically add tags to recorded photos and videos using a generation AI. For example, it analyzes the content of the photo or video and adds tags such as "travel," "family," and "friends." This allows captions and tags to be automatically added to recorded photos and videos using a generation AI.

[0079] The recording tool providing unit can provide a function for automatically organizing recorded memories and generating albums for specific themes or events. For example, the recording tool providing unit automatically organizes recorded photos and videos and generates albums for specific themes or events. For example, albums such as "family trips," "parties with friends," and "sports events" are automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific time periods. For example, albums such as "2023 memories" or "summer memories" are automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific locations. For example, albums such as "memories of travel destinations" or "memories at home" are automatically generated. This allows recorded memories to be automatically organized and albums to be generated for specific themes or events.

[0080] The recording tool providing unit can use the emotion estimation function to analyze the user's emotions toward the recorded memories and create highlights of the memories based on the emotions. The recording tool providing unit, for example, uses the emotion estimation function to analyze the user's emotions toward the recorded memories and create highlights based on the emotions. For example, moments of strong positive emotions are selected as highlights. The recording tool providing unit can also use the emotion estimation function to analyze the user's negative emotions toward the recorded memories and create highlights that avoid those emotions. For example, highlights are created that avoid moments of strong negative emotions. The recording tool providing unit can also use the emotion estimation function to analyze the user's emotions toward the recorded memories in real time and create highlights based on the emotions. For example, emotions are analyzed in real time and moments of strong positive emotions are selected as highlights. In this way, the emotion estimation function can be used to analyze the user's emotions toward the recorded memories and create highlights based on the emotions.

[0081] The recording tool providing unit can add a voice memo function to the recording tool, allowing the user to record by voice what they feel on the spot. The recording tool providing unit, for example, adds a voice memo function to the recording tool, allowing the user to record by voice what they feel on the spot. For example, impressions at a travel destination or emotions at an event are recorded by voice. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot in real time. For example, emotions and memories felt at a specific moment are recorded in real time as voice memos. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot as a memo to review later. For example, impressions about a specific event or activity are recorded as voice memos to review later. In this way, by adding the voice memo function, the user can record by voice what they feel on the spot.

[0082] The recording tool providing unit can provide a private shared album function for sharing recorded memories with family or friends. The recording tool providing unit, for example, provides a private shared album function for sharing recorded memories with family and friends. For example, an album that can only be accessed by specific members can be created. The recording tool providing unit can also use the private shared album function to share memories of specific events or activities. For example, memories of a family trip or a party with friends can be shared. The recording tool providing unit can also use the private shared album function to share memories for specific periods of time. For example, "Memories of 2023" or "Summer Memories" can be shared with family and friends. In this way, by providing the private shared album function, recorded memories can be shared with family and friends.

[0083] The recording tool providing unit can use the emotion estimation function to collect the emotional reactions of family or friends to the recorded memories and increase the value of the shared experience. The recording tool providing unit, for example, uses the emotion estimation function to collect the emotional reactions of family and friends to the recorded memories. For example, the emotional reactions can be identified by analyzing comments and reactions on the shared album. The recording tool providing unit can also use the emotion estimation function to collect the emotional reactions of family and friends to the recorded memories in real time. For example, the emotional reactions can be identified by analyzing real-time comments and feedback. The recording tool providing unit can also use the emotion estimation function to analyze the emotional reactions of family and friends to the recorded memories and provide feedback to increase the value of the shared experience. For example, the emotion estimation function can reflect the emotional reactions of family and friends to the recorded memories in suggestions for the next experience. In this way, the emotion estimation function can be used to collect the emotional reactions of family and friends to the recorded memories and increase the value of the shared experience.

[0084] The lifestyle analysis unit can analyze the user's daily behavioral patterns and find small moments that occur at specific times or places. The lifestyle analysis unit, for example, analyzes the user's daily behavioral patterns and finds small moments that occur at specific times or places. For example, it identifies special moments in daily life, such as a morning coffee break or an evening walk. The lifestyle analysis unit can also analyze the user's behavioral patterns and find small moments related to specific activities or events. For example, it identifies moments related to specific hobbies or activities. The lifestyle analysis unit can also analyze the user's behavioral patterns and find small moments related to specific emotions or moods. For example, it identifies moments when the user can relax or have fun. In this way, by analyzing the user's daily behavioral patterns, it is possible to find small moments that occur at specific times or places.

[0085] The lifestyle analysis unit can predict and suggest particularly valuable moments in daily life based on the user's past behavioral history. The lifestyle analysis unit, for example, analyzes the user's past behavioral history to predict particularly valuable moments in daily life. For example, based on moments in which positive emotions were expressed in the past, it predicts and suggests similar moments. The lifestyle analysis unit can also analyze the user's past behavioral history to predict valuable moments under specific conditions. For example, it predicts valuable moments in specific seasons or weather. The lifestyle analysis unit can also analyze the user's past behavioral history to predict valuable moments related to specific activities or events. For example, it predicts moments related to specific hobbies or activities. In this way, it is possible to predict and suggest particularly valuable moments in daily life based on the user's past behavioral history.

[0086] The lifestyle analysis unit can use the emotion estimation function to analyze the emotions the user feels toward small moments in their daily lives and suggest valuable moments based on those emotions. The lifestyle analysis unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward small moments in their daily lives. For example, it analyzes emotions during specific activities or places and suggests moments that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze emotions the user feels during specific time periods. For example, it analyzes emotions in the morning or evening and suggests time periods that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze emotions the user feels during specific events or activities. For example, it analyzes emotions during specific hobbies or activities and suggests moments that elicit positive emotions. In this way, the emotion estimation function can be used to analyze the emotions the user feels toward small moments in their daily lives and suggest valuable moments based on those emotions.

[0087] The lifestyle analysis unit can incorporate the growth records of the user's pets or plants to discover the value of small everyday moments. The lifestyle analysis unit, for example, incorporates the growth records of the user's pets to discover the value of small everyday moments. For example, it records the pet's growth process and specific behaviors and suggests valuable moments based on the data. The lifestyle analysis unit can also incorporate the growth records of the user's plants to discover the value of small everyday moments. For example, it records the plant's growth process and flowering moments and suggests valuable moments based on the data. The lifestyle analysis unit can also analyze the growth records of the user's pets or plants to discover valuable moments under specific conditions. For example, it suggests valuable moments based on growth records in specific seasons or weather conditions. In this way, the value of small everyday moments can be discovered by incorporating the growth records of the user's pets or plants.

[0088] The lifestyle analysis unit suggests small everyday moments related to the user's hobbies or interests, allowing the user to find value through hobby activities. The lifestyle analysis unit, for example, suggests small everyday moments related to the user's hobbies or interests. For example, for a user whose hobby is gardening, it suggests moments of observing plants growing. The lifestyle analysis unit can also suggest events or activities related to the user's hobbies or interests. For example, for a user whose hobby is music, it suggests concerts or live events. The lifestyle analysis unit can also suggest places or time periods related to the user's hobbies or interests. For example, for a user whose hobby is photography, it suggests taking photos at specific places or time periods. In this way, by suggesting small everyday moments related to the user's hobbies or interests, it is possible to find value through hobby activities.

[0089] The lifestyle analysis unit can use the emotion estimation function to analyze the emotions felt by the user in a specific place or time period and suggest valuable moments based on the emotions. The lifestyle analysis unit, for example, uses the emotion estimation function to analyze the emotions felt by the user in a specific place or time period. For example, the emotion estimation function can analyze the emotions felt by the user in a specific park or cafe and suggest moments that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze the emotions felt by the user at a specific event or activity. For example, the emotion estimation function can analyze the emotions felt by the user in a specific hobby or activity and suggest moments that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze the emotions felt by the user in a specific time period. For example, the emotion estimation function can analyze the emotions felt in the morning or evening and suggest times that elicit positive emotions. In this way, the emotion estimation function can be used to analyze the emotions felt by the user in a specific place or time period and suggest valuable moments based on the emotions.

[0090] The suggestion unit can analyze the user's behavior history and optimize reminders to increase the rate at which the proposed shared experience is performed. The suggestion unit, for example, analyzes the user's behavior history and optimizes reminders to increase the rate at which the proposed shared experience is performed. For example, the suggestion unit may send reminders at times when the user is likely to forget. The suggestion unit can also analyze the user's behavior history and optimize reminders under specific conditions. For example, the suggestion unit may optimize reminders for specific times of day or locations. The suggestion unit can also analyze the user's behavior history and optimize the content and format of reminders. For example, the suggestion unit may send reminders in a format that is optimal for the user, such as a text message or a push notification. In this way, by analyzing the user's behavior history, it is possible to optimize reminders to increase the rate at which the proposed shared experience is performed.

[0091] The suggestion unit can establish a feedback loop for collecting feedback on the user's behavior and reflecting it in the next suggestion. The suggestion unit, for example, establishes a feedback loop for collecting feedback on the user's behavior and reflecting it in the next suggestion. For example, the result of the user acting according to the suggestion is recorded and reflected in the next suggestion. The suggestion unit can also collect feedback on the user's behavior in real time and reflect it in the next suggestion. For example, the suggestion unit adjusts the suggestion based on the real-time feedback. The suggestion unit can also analyze feedback on the user's behavior and establish a feedback loop for improving the accuracy of the suggestion. For example, the suggestion unit improves the suggestion based on positive feedback. In this way, a feedback loop can be established for collecting feedback on the user's behavior and reflecting it in the next suggestion.

[0092] The suggestion unit can use the emotion estimation function to analyze the emotion of the user when acting in accordance with the suggestion, and support the action of eliciting positive emotion. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion of the user when acting in accordance with the suggestion, and support the action of eliciting positive emotion. For example, the suggestion unit makes a next suggestion based on an experience that the user enjoyed. The suggestion unit can also use the emotion estimation function to analyze negative emotion when the user acts in accordance with the suggestion, and support the action of avoiding that. For example, the suggestion unit adjusts the next suggestion based on an experience that the user experienced as stressful. The suggestion unit can also use the emotion estimation function to analyze the emotion of the user when acting in accordance with the suggestion in real time, and support the action of eliciting positive emotion. For example, the suggestion unit analyzes emotion in real time and makes a suggestion that elicits positive emotion. In this way, the emotion estimation function can be used to analyze the emotion of the user when acting in accordance with the suggestion, and support the action of eliciting positive emotion.

[0093] The suggestion unit can provide a function to automatically list preparations or procedures required for a proposed shared experience to support user actions. The suggestion unit, for example, provides a function to automatically list preparations and procedures required for a proposed shared experience. For example, the suggestion unit lists items and procedures required for a picnic. The suggestion unit can also optimize the preparations and procedures required for a proposed shared experience based on the user's behavior history. For example, the suggestion unit lists preparations and procedures that are optimal for the user based on past experiences. The suggestion unit can also update the preparations and procedures required for a proposed shared experience in real time. For example, the suggestion unit updates the required items and procedures depending on changes in weather or conditions. In this way, the suggestion unit can support user actions by providing a function to automatically list preparations and procedures required for a proposed shared experience.

[0094] The suggestion unit can provide a tutorial or guide related to the proposed shared experience to support the user's actions. For example, the suggestion unit can provide a tutorial or guide related to the proposed shared experience. For example, the suggestion unit can provide a guide that provides detailed instructions for cooking recipes or steps. The suggestion unit can also provide a video tutorial related to the proposed shared experience. For example, the suggestion unit can provide a video tutorial about a specific activity or event. The suggestion unit can also provide an interactive guide related to the proposed shared experience. For example, the suggestion unit can provide an interactive guide that the user progresses through by answering questions. This makes it possible to support the user's actions by providing a tutorial or guide related to the proposed shared experience.

[0095] The suggestion unit can use the emotion estimation function to monitor in real time the emotional reaction of the user when acting in accordance with the suggestion and provide support as needed. The suggestion unit, for example, uses the emotion estimation function to monitor in real time the emotional reaction of the user when acting in accordance with the suggestion. For example, if the user is feeling stressed, the suggestion unit can make a suggestion to help the user relax. The suggestion unit can also use the emotion estimation function to analyze the user's emotional reaction in real time and provide support as needed. For example, the suggestion unit can provide support when the user is feeling difficulty. The suggestion unit can also use the emotion estimation function to monitor the user's emotional reaction in real time and adjust the suggestion. For example, the suggestion unit changes the content of the suggestion depending on the user's emotional reaction. In this way, by using the emotion estimation function, the emotional reaction of the user when acting in accordance with the suggestion can be monitored in real time and support can be provided as needed.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The lifestyle analysis unit analyzes the user's lifestyle or interests. For example, the lifestyle analysis unit analyzes the user's hobbies and activities to extract lifestyle patterns. The lifestyle analysis unit can also analyze the user's past behavioral history and track changes in interests and concerns. The lifestyle analysis unit can also use an emotion estimation function to analyze changes in the user's emotions and make lifestyle suggestions based on the emotions. The suggestion unit suggests shared experiences with family or friends based on the results of the analysis by the lifestyle analysis unit. For example, the suggestion unit makes specific suggestions such as, "Why don't you go on a family picnic today?" or "Let's cook together with friends." The suggestion unit can also make suggestions at the optimal time, taking into account the user's current mood and physical condition. The suggestion unit can also use the emotion estimation function to predict the user's emotional reaction to the proposed shared experience and prioritize suggestions that are expected to elicit a positive reaction. The recording tool provision unit provides a recording tool for recording the shared experiences suggested by the suggestion unit. For example, the recording tool providing unit may provide functions such as taking photos and videos, keeping a diary, and recording shared experiences on a calendar. The recording tool providing unit may also provide a function that automatically adds captions and tags to recorded photos and videos using a generation AI. The recording tool providing unit may also provide a function that automatically organizes recorded memories and generates albums for specific themes or events. This allows the Memory Maker AI system according to the embodiment to ensure users do not miss out on special moments in their daily lives. For example, users can deepen bonds with family and friends through shared experiences. Furthermore, discovering hidden value in small everyday moments can make each day special.

[0098] The lifestyle analysis unit can analyze the user's life log data and extract detailed lifestyle patterns. For example, it can analyze heart rate and step count data obtained from a smartwatch to understand the user's exercise habits and health condition. For example, it can analyze the user's lifestyle in detail based on the amount of daily exercise and sleep patterns. The lifestyle analysis unit can also analyze data from a fitness tracker to understand the user's activity level and calorie consumption. For example, it can analyze the user's exercise patterns and eating habits based on the fitness tracker data. The lifestyle analysis unit can also analyze the smartphone's location information data to understand the user's movement patterns and visited places. For example, it can analyze the user's commute route and how they spend their weekends based on the location information data. In this way, by analyzing the user's life log data, more detailed lifestyle patterns can be extracted.

[0099] The lifestyle analysis unit can analyze a user's past SNS posts or photos to track changes in their interests. For example, it can analyze a user's SNS posts and extract their interests from the content of the posts and hashtags. For example, it can identify themes such as travel, cooking, and sports and track changes in the user's interests. The lifestyle analysis unit can also analyze a user's past photos and identify their interests using image analysis technology. For example, it can identify the user's hobbies and activities based on the content of the photos and the locations where they were taken. The lifestyle analysis unit can also analyze a user's SNS posts and photos in combination to track changes in their interests in more detail. For example, it can analyze the relevance between the content of the posts and the photos to comprehensively understand changes in the user's interests. This makes it possible to track changes in the user's interests by analyzing a user's past SNS posts and photos.

[0100] The lifestyle analysis unit can use the emotion estimation function to analyze changes in emotions from the user's past actions or comments and suggest lifestyles based on those emotions. For example, the emotion estimation function can be used to analyze the user's past SNS posts and messages and identify changes in emotions. For example, it can identify periods when the user had more positive emotions or more negative emotions. The lifestyle analysis unit can also analyze the user's past behavioral history and identify changes in emotions using the emotion estimation function. For example, it can analyze changes in emotions in response to specific events or activities. The lifestyle analysis unit can also analyze the user's past comments and identify changes in emotions using the emotion estimation function. For example, it can analyze changes in emotions based on the content and tone of the comments. In this way, the emotion estimation function can be used to analyze changes in the user's emotions and suggest lifestyles based on those emotions.

[0101] The lifestyle analysis unit can incorporate dialogue data with the voice assistant and extract interests from everyday conversations. For example, the dialogue data with the voice assistant can be analyzed to extract interests from the user's everyday conversations. For example, interests can be identified based on themes frequently discussed by the user or the content of questions. The lifestyle analysis unit can also analyze the dialogue data with the voice assistant to analyze the user's emotions and tone. For example, the user's interests can be identified based on positive or negative tones. The lifestyle analysis unit can also analyze the dialogue data with the voice assistant to understand the user's behavioral patterns and habits. For example, the lifestyle analysis unit can analyze the user's lifestyle based on the content of conversations at specific times of day or in specific places. In this way, interests can be extracted from everyday conversations by analyzing the dialogue data with the voice assistant.

[0102] The lifestyle analysis unit can analyze data of users of different age groups or cultural backgrounds and make suggestions that take into account the diversity of lifestyles. For example, it can analyze data of users of different age groups and make suggestions that take into account the diversity of lifestyles. For example, it can grasp the differences in lifestyles between young people and elderly people and make suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users of different cultural backgrounds and make suggestions that take into account the diversity of lifestyles. For example, it can grasp the lifestyles of users of different nationalities or religions and make suggestions that are appropriate for each. The lifestyle analysis unit can also analyze data of users in different regions and make suggestions that take into account the diversity of lifestyles. For example, it can grasp the differences in lifestyles between urban and rural areas and make suggestions that are appropriate for each. In this way, by analyzing data of different age groups and cultural backgrounds, it becomes possible to make suggestions that take into account the diversity of lifestyles.

[0103] The lifestyle analysis unit can use the emotion estimation function to analyze the emotions a user feels when performing a specific activity in real time and suggest activities that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions a user feels when performing a specific activity in real time. For example, the emotion estimation function can be used to analyze the emotions a user feels when performing exercise or a hobby, and suggest activities that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze the emotions a user feels in a specific place in real time. For example, the emotion estimation function can be used to analyze the emotions a user feels in a park or a cafe, and suggest places that elicit positive emotions. The lifestyle analysis unit can also use the emotion estimation function to analyze the emotions a user feels at a specific time of day in real time. For example, the emotion estimation function can be used to analyze the emotions a user feels in the morning or evening, and suggest times of day that elicit positive emotions. In this way, the emotion estimation function can be used to analyze the emotions a user feels when performing a specific activity in real time and suggest activities that elicit positive emotions.

[0104] The suggestion unit can analyze the user's history of past shared experiences, extract patterns of successful experiences, and reflect them in suggestions. For example, the suggestion unit can analyze the user's history of past shared experiences and extract patterns of successful experiences. For example, suggestions are made based on positive experiences such as family trips or events with friends. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of unsuccessful experiences. For example, similar suggestions are avoided based on experiences that the user did not enjoy. The suggestion unit can also analyze the user's history of past shared experiences and extract patterns of success under specific conditions. For example, suggestions are made based on successful experiences in specific seasons or weather conditions. In this way, by analyzing the history of past shared experiences, patterns of successful experiences can be extracted and reflected in suggestions.

[0105] The suggestion unit can make suggestions at the optimal timing, taking into account the user's current mood or physical condition. For example, it can analyze the user's current mood or physical condition and suggest a shared experience at the optimal timing based on that. For example, it can suggest a relaxing experience when the user is relaxed. The suggestion unit can also analyze the user's current mood or physical condition and suggest an experience that reduces stress. For example, it can suggest a relaxing experience when the user is feeling stressed. The suggestion unit can also analyze the user's current mood or physical condition and suggest an experience that increases energy. For example, it can suggest a refreshing experience when the user is tired. This makes it possible to make suggestions at the optimal timing by taking into account the user's current mood and physical condition.

[0106] The suggestion unit can use the emotion estimation function to predict the user's emotional response to the proposed shared experience and prioritize suggestions that are expected to elicit a positive response. For example, the emotion estimation function can be used to predict the user's emotional response to the proposed shared experience. For example, experiences that are expected to elicit a positive response based on past data can be prioritized for suggestion. The suggestion unit can also use the emotion estimation function to predict a negative response to the proposed shared experience and make suggestions that avoid that. For example, experiences that are expected to elicit a negative response based on past data can be avoided. The suggestion unit can also use the emotion estimation function to monitor the emotional response to the proposed shared experience in real time and modify the suggestion as needed. For example, the suggestion unit can analyze the user's emotional response in real time and adjust the suggestion to elicit a positive response. In this way, the emotion estimation function can be used to predict the user's emotional response to the proposed shared experience and prioritize suggestions that are expected to elicit a positive response.

[0107] The suggestion unit can incorporate the user's geographical location information to suggest nearby events or activities. For example, nearby events and activities can be suggested based on the user's geographical location information. For example, events held near the user's current location can be suggested. The suggestion unit can also suggest activities at specific locations based on the user's geographical location information. For example, tourist spots and leisure facilities near the user's current location can be suggested. The suggestion unit can also suggest events and activities within a range of travel based on the user's geographical location information. For example, events held within a certain distance from the user's current location can be suggested. In this way, by incorporating the user's geographical location information, nearby events and activities can be suggested.

[0108] The suggestion unit can suggest shared experiences according to different seasons or weather conditions, and make suggestions for enjoying the sense of the seasons. For example, the suggestion unit can suggest shared experiences according to different seasons. For example, suggestions for enjoying the sense of the seasons, such as cherry blossom viewing in spring, swimming in the sea in summer, leaf viewing in autumn, and skiing in winter, can be made. The suggestion unit can also suggest shared experiences according to different weather conditions. For example, outdoor activities can be suggested on sunny days, and indoor activities on rainy days. The suggestion unit can also suggest special events or activities according to the seasons or weather conditions. For example, seasonal festivals or workshops according to the weather can be suggested. In this way, suggestions according to different seasons or weather conditions can be made, allowing the sense of the seasons to be enjoyed.

[0109] The suggestion unit can use the emotion estimation function to consider the emotional reactions of family or friends to the proposed shared experience and suggest experiences that everyone can enjoy. For example, the emotion estimation function can be used to predict the emotional reactions of family and friends to the proposed shared experience and suggest experiences that everyone can enjoy. For example, the suggestion unit can suggest experiences that everyone will have a positive reaction to based on past data. The suggestion unit can also use the emotion estimation function to predict the negative reactions of family and friends to the proposed shared experience and make suggestions to avoid them. For example, the suggestion unit can avoid experiences that are predicted to have a negative reaction based on past data. The suggestion unit can also use the emotion estimation function to monitor the emotional reactions of family and friends to the proposed shared experience in real time and modify the suggestions as needed. For example, the suggestion unit can analyze the emotional reactions of family and friends in real time and adjust the suggestions so that everyone can enjoy the experience. In this way, the emotion estimation function can consider the emotional reactions of family and friends and suggest experiences that everyone can enjoy.

[0110] The recording tool providing unit can provide a function to automatically add captions or tags to recorded photos or videos using a generation AI. For example, the recording tool providing unit can automatically generate captions for recorded photos using a generation AI. For example, the content of the photo can be analyzed and captions such as "family picnic" or "barbecue with friends" can be added. The recording tool providing unit can also automatically generate captions for recorded videos using a generation AI. For example, the content of the video can be analyzed and captions such as "travel memories" or "event highlights" can be added. The recording tool providing unit can also automatically add tags to recorded photos and videos using a generation AI. For example, the content of the photo or video can be analyzed and tags such as "travel," "family," and "friends" can be added. This allows captions and tags to be automatically added to recorded photos and videos using a generation AI.

[0111] The recording tool providing unit can provide a function to automatically organize recorded memories and generate albums for specific themes or events. For example, the recording tool providing unit can automatically organize recorded photos and videos and generate albums for specific themes or events. For example, albums such as "family trips," "parties with friends," and "sports events" can be automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific time periods. For example, albums such as "2023 memories" or "summer memories" can be automatically generated. The recording tool providing unit can also automatically organize recorded memories and generate albums for specific locations. For example, albums such as "memories of travel destinations" or "memories at home" can be automatically generated. This allows the recording tool providing unit to automatically organize recorded memories and generate albums for specific themes or events.

[0112] The recording tool providing unit can use the emotion estimation function to analyze the user's emotions toward the recorded memories and create highlights of the memories based on the emotions. For example, the emotion estimation function can be used to analyze the user's emotions toward the recorded memories and create highlights based on the emotions. For example, moments with strong positive emotions can be selected as highlights. The recording tool providing unit can also use the emotion estimation function to analyze the user's negative emotions toward the recorded memories and create highlights that avoid those emotions. For example, highlights can be created that avoid moments with strong negative emotions. The recording tool providing unit can also use the emotion estimation function to analyze the user's emotions toward the recorded memories in real time and create highlights based on the emotions. For example, emotions can be analyzed in real time and moments with strong positive emotions can be selected as highlights. In this way, the emotion estimation function can be used to analyze the user's emotions toward the recorded memories and create highlights based on the emotions.

[0113] The recording tool providing unit can add a voice memo function to the recording tool, allowing the user to record by voice what they feel on the spot. For example, the voice memo function can be added to the recording tool, allowing the user to record by voice what they feel on the spot. For example, impressions at a travel destination or emotions at an event can be recorded by voice. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot in real time. For example, emotions and memories felt at a specific moment can be recorded in real time as voice memos. The recording tool providing unit can also use the voice memo function to record what the user feels on the spot as a memo to review later. For example, impressions about a specific event or activity can be recorded as voice memos to review later. In this way, adding the voice memo function allows the user to record by voice what they feel on the spot.

[0114] The recording tool providing unit can provide a private shared album function for sharing recorded memories with family or friends. For example, a private shared album function for sharing recorded memories with family and friends can be provided. For example, an album that can only be accessed by specific members can be created. The recording tool providing unit can also use the private shared album function to share memories of specific events or activities. For example, memories of a family trip or a party with friends can be shared. The recording tool providing unit can also use the private shared album function to share memories for specific periods of time. For example, "Memories of 2023" or "Summer Memories" can be shared with family and friends. In this way, by providing the private shared album function, recorded memories can be shared with family and friends.

[0115] The recording tool providing unit can use the emotion estimation function to collect the emotional reactions of family or friends to the recorded memories and increase the value of the shared experience. For example, the emotion estimation function is used to collect the emotional reactions of family and friends to the recorded memories. For example, the emotional reactions can be identified by analyzing comments and reactions on the shared album. The recording tool providing unit can also use the emotion estimation function to collect the emotional reactions of family and friends to the recorded memories in real time. For example, the emotional reactions can be identified by analyzing comments and feedback in real time. The recording tool providing unit can also use the emotion estimation function to analyze the emotional reactions of family and friends to the recorded memories and provide feedback to increase the value of the shared experience. For example, the emotion estimation function can reflect the emotional reactions of family and friends to the recorded memories in suggestions for the next experience. In this way, the emotion estimation function can be used to collect the emotional reactions of family and friends to the recorded memories and increase the value of the shared experience.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The lifestyle analysis unit analyzes the user's lifestyle or interests. For example, it analyzes the user's hobbies and activities to extract lifestyle patterns. It can also analyze past behavioral history and track changes in interests and concerns. Furthermore, it can use the emotion estimation function to analyze changes in the user's emotions and make lifestyle suggestions based on those emotions. Step 2: The suggestion unit suggests experiences to share with family or friends based on the results of the analysis by the lifestyle analysis unit. For example, it makes specific suggestions such as "Why don't you go on a family picnic today?" or "Let's cook together with friends." It can also make suggestions at the optimal time by taking into account the user's current mood and physical condition. Furthermore, it can use its emotion estimation function to predict the user's emotional reaction to the suggested shared experiences and prioritize suggestions that are likely to elicit a positive reaction. Step 3: The recording tool provider provides recording tools for recording the shared experiences suggested by the suggestion unit. For example, it provides functions for taking photos and videos, keeping a diary, and recording the shared experiences in a calendar. It also provides functions for automatically adding captions and tags using generative AI, and for automatically organizing the recorded memories and generating albums for specific themes or events.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0162] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a lifestyle analysis unit that analyzes a user's lifestyle or interests; a suggestion unit that suggests shared experiences with family or friends based on the results of the analysis by the lifestyle analysis unit; a recording tool providing unit that provides a recording tool for recording the shared experience suggested by the suggestion unit. A system characterized by:

2. The lifestyle analysis unit Incorporating conversation data with voice assistants to extract interests and concerns from everyday conversations 2. The system of claim 1.

3. The proposal unit Analyze the user's past shared experiences, extract patterns of successful experiences, and reflect them in the proposals.

2. The system of claim 1.

4. The recording tool providing unit Providing the ability to automatically caption or tag recorded photos or videos using generative AI 2. The system of claim 1.

5. The lifestyle analysis unit Using the emotion estimation function, changes in emotions are analyzed based on the user's past actions or statements, and lifestyle suggestions are made based on emotions.

2. The system of claim 1.

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