system

Abstract

An object of a system according to an embodiment is to share memories between family members living apart from each other and deepen the bond between the family members.SOLUTION: A system includes a data collection part, a data filtering part, a memory creation part, a diary generation part, and a sharing part. The data collection unit collects an SNS or an action log of the family. The data filtering unit filters the data collected by the data collection unit for privacy protection. The memory creation section creates a shared memory based on the data filtered by the data filtering section. The diary generation unit generates a diary based on the memory created by the memory creation unit. The sharing unit shares the diary generated by the diary generation unit among family members.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 does not adequately facilitate the sharing of memories between family members who live far apart, and there is room for improvement in terms of deepening family ties.

[0005] The system according to the embodiment aims to share memories among family members who live far apart and deepen family ties. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data filtering unit, a memory creation unit, a diary creation unit, and a sharing unit. The data collection unit collects social media or activity logs of family members. The data filtering unit filters the data collected by the data collection unit to protect privacy. The memory creation unit creates shared memories based on the data filtered by the data filtering unit. The diary creation unit creates a diary based on the memories created by the memory creation unit. The sharing unit shares the diary created by the diary creation unit among family members. [Effects of the Invention]

[0007] The system according to the embodiment allows family members who live far apart to share memories and deepen family ties. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The AI ​​diary system according to an embodiment of the present invention collects family social media accounts and activity logs, creates shared memories while protecting privacy, and shares them as a diary. This allows the AI ​​diary system to turn the family's daily events into a diary in which to capture affection.

[0029] An AI diary system according to an embodiment includes a data collection unit, a data filtering unit, a memory creation unit, a diary generation unit, and a sharing unit. The data collection unit collects family members' social media accounts or activity logs. For example, it collects social media posts, photos, location information, activity history, etc. The data filtering unit filters the data collected by the data collection unit to protect privacy. For example, it automatically excludes posts containing personal information or private content. The memory creation unit creates shared memories based on the data filtered by the data filtering unit. For example, it compiles family trips, events, and everyday occurrences into a single diary. The diary creation unit generates a diary based on the memories created by the memory creation unit. For example, it organizes events by date and combines photos and text to create pages. The sharing unit shares the diary created by the diary creation unit among family members. For example, it stores the diary digitally in the cloud so that all family members can access it. As a result, the AI ​​diary system according to an embodiment can collect family members' social media accounts and activity logs, create shared memories while protecting privacy, and share them as a diary.

[0030] The data collection unit can automatically tag and collect data related to specific events and activities based on the behavior logs of family members. In the data collection unit, for example, the generation AI analyzes the behavior logs of family members and automatically tags data related to specific events and activities. For example, events such as trips and birthday parties are identified and related data is collected. In addition, the data collection unit automatically tags data related to specific activities based on the behavior logs of family members. For example, activities such as sporting events and family gatherings are identified and related data is collected. In addition, in the data collection unit, the generation AI analyzes the behavior logs of family members and automatically tags data related to specific events and activities. This allows for efficient collection of data related to important events and activities.

[0031] The data collection unit learns the past behavioral patterns of family members and can predict important future events and collect data in advance. In the data collection unit, for example, the generation AI learns the past behavioral patterns of family members and predicts important future events. For example, it predicts events such as annual family trips and birthdays and collects data in advance. In addition, the data collection unit predicts important future events based on the past behavioral patterns of family members and collects data in advance. For example, it predicts regular family gatherings and anniversaries. In addition, the data collection unit learns the past behavioral patterns of family members and predicts important future events and collects data in advance. This makes it possible to collect data on important events without omission.

[0032] The data collection unit can collect audio messages or video logs from family members and integrate them with text data to use as diary material. In the data collection unit, for example, the generation AI collects audio messages and video logs from family members and integrates them with text data. For example, family conversations and video messages are collected and used as diary material. In addition, the data collection unit collects audio messages and video logs from family members and the generation AI integrates them with text data. This allows the content of the audio and video to be reflected in the diary. In addition, in the data collection unit, the generation AI collects audio messages and video logs from family members and integrates them with text data. This allows the content of the audio and video to be used as diary material to create a richer diary. This allows the content of the audio and video to be used as diary material to create a richer diary.

[0033] The data collection unit automates data collection across different social media platforms and manages the data in a unified format. For example, the generation AI automatically collects data from different social media platforms and manages it in a unified format. For example, data from Facebook, Instagram, Twitter, etc. is centralized. The data collection unit also automates data collection across different social media platforms and the generation AI manages the data in a unified format. This allows data from multiple platforms to be collected efficiently. The data collection unit also automates data collection across different social media platforms and manages it in a unified format. This allows social media data of family members to be managed centrally and used as material for a diary. This allows data from multiple platforms to be collected efficiently and managed centrally.

[0034] The data filtering unit can learn the privacy settings of family members and apply individually customized filtering rules. For example, the data filtering unit allows the generation AI to learn the privacy settings of family members and apply individually customized filtering rules. For example, the data filtering unit prioritizes filtering of posts from specific members. The data filtering unit also allows the generation AI to apply individually customized filtering rules based on the privacy settings of family members. This allows data to be collected while protecting the privacy of each member. The data filtering unit also allows the generation AI to learn the privacy settings of family members and apply individually customized filtering rules. This allows necessary data to be collected while protecting privacy. This allows data to be collected while protecting the privacy of each member.

[0035] The data filtering unit automatically performs anonymization processing on the filtered data, thereby further enhancing privacy. The data filtering unit, for example, automatically performs anonymization processing on the data filtered by the generation AI. For example, personal information such as an individual's name and address is deleted. The data filtering unit also automatically performs anonymization processing on the filtered data by the generation AI, thereby enhancing privacy. This allows data to be shared with peace of mind. The data filtering unit also automatically performs anonymization processing on the filtered data by the generation AI, thereby further enhancing privacy. This makes it possible to reduce the risk of personal information being leaked. This makes it possible to reduce the risk of personal information being leaked.

[0036] The data filtering unit provides an interface that allows family members to manually set filtering rules, thereby realizing flexible privacy management. The data filtering unit, for example, provides an interface that allows family members to manually set filtering rules. For example, a setting is made to exclude specific keywords or phrases. The data filtering unit also provides an interface that allows family members to manually set filtering rules, thereby realizing flexible privacy management. This allows each member's privacy to be managed individually. The data filtering unit also provides an interface that allows family members to manually set filtering rules. This allows flexible data management according to the privacy protection needs of each member.

[0037] The data filtering unit can manage the filtered data by applying different levels of privacy protection to each family member. For example, the data filtering unit manages the data filtered by the generation AI by applying different levels of privacy protection to each family member. For example, stricter filtering is performed on children's data. The data filtering unit also manages the filtered data by applying different levels of privacy protection to each family member. This makes it possible to manage data according to the privacy protection needs of each family member. The data filtering unit also manages the data filtered by the generation AI by applying different levels of privacy protection to each family member. This increases the flexibility of privacy protection. This makes it possible to manage data according to the privacy protection needs of each family member.

[0038] The memory creation unit can learn from family members' past memories and automatically suggest new related memories. For example, the memory creation unit's generation AI learns from family members' past memories and automatically suggests new related memories. For example, it can suggest new travel destinations based on past travel destinations. The memory creation unit can also analyze family members' past memories and automatically suggest new related memories. This can enrich family memories. The memory creation unit's generation AI can also learn from family members' past memories and automatically suggest new related memories. This can create new memories that deepen family bonds. This can enrich family memories.

[0039] The memory creation unit can create personalized memories by taking into account the preferences and interests of family members. For example, the generation AI of the memory creation unit analyzes the preferences and interests of each family member to create personalized memories. For example, a diary may be created centered around activities that a specific member likes. The memory creation unit also considers the preferences and interests of each family member to create personalized memories. This allows for a diary that is special to each member. The memory creation unit also considers the preferences and interests of each family member to create personalized memories. This allows for a diary that the whole family can enjoy. This allows for a diary that is special to each member.

[0040] The memory creation unit provides a collaboration function that allows family members to edit memories together, enabling real-time collaboration. The memory creation unit provides a collaboration function that allows family members to edit memories together, for example, allowing editing to be done online in real time, so that everyone can participate. The memory creation unit also provides a collaboration function in which the generation AI supports family members' collaboration, allowing them to edit memories in real time, so that all family members can participate in creating memories. The memory creation unit also provides a collaboration function that allows family members to edit memories together, enabling real-time collaboration, so that all family members can create memories together.

[0041] The memory creation unit can integrate the memories of different family members and create a coherent story as a whole. For example, the memory creation unit uses a generation AI to integrate the memories of different family members and create a coherent story as a whole. For example, it creates a diary that incorporates the perspectives of each member. The memory creation unit also analyzes the memories of family members and the generation AI creates a coherent story. This makes it possible to bring together the memories of all family members into a single story. The memory creation unit also uses a generation AI to integrate the memories of different family members and create a coherent story as a whole. This makes it possible to create a diary that all family members can relate to. This makes it possible to bring together the memories of all family members into a single story.

[0042] The diary generation unit can provide a function to automatically summarize the contents of the diary and highlight important events and highlights. For example, the diary generation unit provides a function in which the generation AI automatically summarizes the contents of the diary and highlights important events and highlights. For example, important events on a specific date are summarized and displayed. The diary generation unit also analyzes the contents of the diary, and the generation AI automatically summarizes and highlights important events and highlights. This allows the contents of the diary to be understood concisely. The diary generation unit also provides a function in which the generation AI automatically summarizes the contents of the diary and highlights important events and highlights. This allows the diary to be enjoyed without missing any important information. This allows the contents of the diary to be understood concisely.

[0043] The diary generation unit can learn feedback from family members and reflect it in the next automatic generation. In the diary generation unit, for example, the generation AI learns feedback from family members and reflects it in the next automatic generation of a diary. For example, it learns preferences for specific designs and layouts and applies them next time. The diary generation unit also analyzes feedback from family members and the generation AI reflects it in the next automatic generation of a diary. This makes it possible to create a diary that suits the user's preferences. In addition, the diary generation unit also uses the generation AI to learn feedback from family members and reflect it in the next automatic generation of a diary. This makes it possible to continuously improve the quality of the diary. This makes it possible to create a diary that suits the user's preferences.

[0044] The diary generation unit can provide a function to automatically generate diary content in different formats (for example, video diary or audio diary). The diary generation unit, for example, provides a function whereby the generation AI automatically generates diary content in different formats. For example, not only text diaries but also video diaries and audio diaries are created. The diary generation unit also analyzes the diary content and the generation AI automatically generates it in different formats. This makes it possible to preserve family memories in a variety of formats. The diary generation unit also provides a function whereby the generation AI automatically generates diary content in different formats. This makes it possible to preserve family memories in a richer way. This makes it possible to preserve family memories in a variety of formats.

[0045] The diary generation unit provides an interface that allows family members to manually edit the diary, and the generation AI can learn the edited content. The diary generation unit, for example, provides an interface that allows family members to manually edit the diary, and the generation AI can learn the edited content. For example, it learns comments and photos added by the user. The diary generation unit also allows the generation AI to learn the manual edited content of family members and reflect it in the next automatic diary generation. This makes it possible to create a diary that suits the user's preferences. The diary generation unit also provides an interface that allows family members to manually edit the diary, and the generation AI can learn the edited content. This makes it possible to continuously improve the quality of the diary.

[0046] The sharing unit can provide a function to automatically back up diary contents and prevent data loss. For example, the sharing unit provides a function that allows the generation AI to automatically back up diary contents and prevent data loss. For example, regular backups are made to cloud storage. The sharing unit also analyzes the diary contents and the generation AI automatically backs them up. This prevents data loss and allows the diary to be stored with peace of mind. The sharing unit also provides a function that allows the generation AI to automatically back up diary contents and prevent data loss. This allows important memories to be stored safely. This prevents data loss and allows the diary to be stored with peace of mind.

[0047] The sharing unit can learn feedback from family members and reflect it in the next sharing method. In the sharing unit, for example, the generation AI learns feedback from family members and reflects it in the next diary sharing method. For example, the sharing unit learns preferences for specific sharing methods and timing and applies them next time. The sharing unit also analyzes feedback from family members and the generation AI reflects it in the next diary sharing method. This makes it possible to provide a sharing method that suits the user's preferences. In addition, the sharing unit can learn feedback from family members and reflect it in the next diary sharing method. This makes it possible to continuously improve the diary sharing method. This makes it possible to provide a sharing method that suits the user's preferences.

[0048] The sharing unit can optimize the diary so that it can be viewed on different devices (e.g., smartphone, tablet, PC). For example, the sharing unit optimizes the diary so that the generation AI can view it on different devices. For example, it provides a layout that is compatible with devices such as smartphones, tablets, and PCs. The sharing unit also analyzes the contents of the diary and optimizes it so that the generation AI can view it on different devices. This allows all family members to enjoy the diary on any device. The sharing unit also optimizes the diary so that the generation AI can view it on different devices. This allows all family members to view the diary anytime, anywhere. This allows all family members to enjoy the diary on any device.

[0049] The sharing section can provide a function that allows family members to select and share only specific pages or sections when sharing a diary. For example, the sharing section provides a function that allows the generation AI to select and share only specific pages or sections when sharing a diary. For example, selecting and sharing specific events or memories. The sharing section also provides a function that analyzes the contents of the diary and allows the generation AI to select and share only specific pages or sections. This makes it possible to share only the parts that are necessary. The sharing section also provides a function that allows the generation AI to select and share only specific pages or sections when sharing a diary. This makes it possible to share a diary while protecting privacy. This makes it possible to share only the parts that are necessary.

[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 data collection unit can also collect health data of family members and reflect it in the diary. For example, it can collect data from smartwatches and fitness trackers and record the family members' health conditions and activity levels in the diary. The data collection unit can also collect food logs of family members and reflect it in the diary. For example, it can collect photos of meals and calorie information to record the family members' eating habits. The data collection unit can also collect sleep data of family members and reflect it in the diary. For example, it can record sleep duration and quality to help manage the family members' health. This allows for a comprehensive record of the family members' health conditions and can be used for health management.

[0052] The data collection unit can also collect data related to the hobbies and interests of family members and reflect it in the diary. For example, it can collect information about events and hobbies that family members have participated in and record it in the diary. The data collection unit can also collect reading histories of family members and reflect them in the diary. For example, it can record the titles of books read and their impressions, and share the family's reading activities. The data collection unit can also collect movie and TV program viewing histories of family members and reflect them in the diary. For example, it can record the titles of movies and programs watched and their impressions, and share the family's entertainment activities. This allows the family's hobbies and interests to be comprehensively recorded and shared.

[0053] The data collection unit can also collect information about family members' travel destinations and reflect it in the diary. For example, it can collect information about tourist attractions and restaurants at travel destinations to enrich travel memories. The data collection unit can also collect photos and videos taken by family members during their trip and reflect them in the diary. For example, it can collect photos and videos taken during the trip to visually record travel memories. The data collection unit can also collect family members' impressions and reviews during the trip and reflect them in the diary. For example, it can record experiences and impressions at travel destinations and share travel memories. This allows for enriched recording and sharing of family travel memories.

[0054] The data collection unit can also collect data related to family members' learning activities and reflect it in the diary. For example, it can collect participation history in online courses and workshops and record learning progress. The data collection unit can also collect family members' learning outcomes and projects and reflect them in the diary. For example, it can record completed projects and acquired qualifications and share learning outcomes. The data collection unit can also collect family members' impressions and feedback on learning and reflect them in the diary. For example, it can record what they learned and their feelings and share their learning process. This makes it possible to comprehensively record and share family members' learning activities.

[0055] The data collection unit can also collect data related to the creative activities of family members and reflect it in the diary. For example, it can collect artwork and handmade items created by family members and record their creative activities. The data collection unit can also collect data related to the musical activities of family members and reflect it in the diary. For example, it can record the songs played and the music composed and share the musical activities. The data collection unit can also collect data related to the cooking activities of family members and reflect it in the diary. For example, it can record recipes and photos of the dishes made and share cooking memories. This makes it possible to comprehensively record and share the creative activities of the family.

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

[0057] Step 1: The data collection unit collects the family's social media or activity logs, such as social media posts, photos, location information, and activity history. Step 2: The data filtering unit filters the data collected by the data collection unit to protect privacy. For example, posts containing personal information or private content are automatically filtered out. Step 3: The memory creation unit creates shared memories based on the data filtered by the data filtering unit. For example, it compiles family trips, events, and everyday happenings into a diary. Step 4: The diary generation unit generates a diary based on the memories created by the memory creation unit. For example, it organizes events by date and creates pages by combining photos and text. Step 5: The sharing unit shares the diary created by the diary creation unit among family members, for example, by digitally storing it in the cloud so that all family members can access it.

[0058] (Example 2) The AI ​​diary system according to an embodiment of the present invention collects family social media accounts and activity logs, creates shared memories while protecting privacy, and shares them as a diary. This allows the AI ​​diary system to turn the family's daily events into a diary in which to capture affection.

[0059] An AI diary system according to an embodiment includes a data collection unit, a data filtering unit, a memory creation unit, a diary generation unit, and a sharing unit. The data collection unit collects family members' social media accounts or activity logs. For example, it collects social media posts, photos, location information, activity history, etc. The data filtering unit filters the data collected by the data collection unit to protect privacy. For example, it automatically excludes posts containing personal information or private content. The memory creation unit creates shared memories based on the data filtered by the data filtering unit. For example, it compiles family trips, events, and everyday occurrences into a single diary. The diary creation unit generates a diary based on the memories created by the memory creation unit. For example, it organizes events by date and combines photos and text to create pages. The sharing unit shares the diary created by the diary creation unit among family members. For example, it stores the diary digitally in the cloud so that all family members can access it. As a result, the AI ​​diary system according to an embodiment can collect family members' social media accounts and activity logs, create shared memories while protecting privacy, and share them as a diary.

[0060] The data collection unit performs sentiment analysis of family members' social media posts and can prioritize the collection of posts with positive emotions. In the data collection unit, for example, the generation AI analyzes family members' social media posts and performs sentiment analysis. To prioritize the collection of posts with positive emotions, posts with high sentiment scores, such as joy and emotion, are selected. In addition, the data collection unit performs sentiment analysis of family members' social media posts and filters out posts with positive emotions. For example, it prioritizes the collection of smiling photos and congratulatory messages. In addition, the data collection unit performs sentiment analysis of family members' social media posts and automatically tags posts with positive emotions. This prioritizes the collection of positive posts to use as material for the diary. In this way, by prioritizing the collection of posts with positive emotions, a more emotionally rich diary can be created.

[0061] The data collection unit can automatically tag and collect data related to specific events and activities based on the behavior logs of family members. In the data collection unit, for example, the generation AI analyzes the behavior logs of family members and automatically tags data related to specific events and activities. For example, events such as trips and birthday parties are identified and related data is collected. In addition, the data collection unit automatically tags data related to specific activities based on the behavior logs of family members. For example, activities such as sporting events and family gatherings are identified and related data is collected. In addition, in the data collection unit, the generation AI analyzes the behavior logs of family members and automatically tags data related to specific events and activities. This allows for efficient collection of data related to important events and activities.

[0062] The data collection unit learns the past behavioral patterns of family members and can predict important future events and collect data in advance. In the data collection unit, for example, the generation AI learns the past behavioral patterns of family members and predicts important future events. For example, it predicts events such as annual family trips and birthdays and collects data in advance. In addition, the data collection unit predicts important future events based on the past behavioral patterns of family members and collects data in advance. For example, it predicts regular family gatherings and anniversaries. In addition, the data collection unit learns the past behavioral patterns of family members and predicts important future events and collects data in advance. This makes it possible to collect data on important events without omission.

[0063] The data collection unit can collect audio messages or video logs from family members and integrate them with text data to use as diary material. In the data collection unit, for example, the generation AI collects audio messages and video logs from family members and integrates them with text data. For example, family conversations and video messages are collected and used as diary material. In addition, the data collection unit collects audio messages and video logs from family members and the generation AI integrates them with text data. This allows the content of the audio and video to be reflected in the diary. In addition, in the data collection unit, the generation AI collects audio messages and video logs from family members and integrates them with text data. This allows the content of the audio and video to be used as diary material to create a richer diary. This allows the content of the audio and video to be used as diary material to create a richer diary.

[0064] The data collection unit automates data collection across different social media platforms and manages the data in a unified format. For example, the generation AI automatically collects data from different social media platforms and manages it in a unified format. For example, data from Facebook, Instagram, Twitter, etc. is centralized. The data collection unit also automates data collection across different social media platforms and the generation AI manages the data in a unified format. This allows data from multiple platforms to be collected efficiently. The data collection unit also automates data collection across different social media platforms and manages it in a unified format. This allows social media data of family members to be managed centrally and used as material for a diary. This allows data from multiple platforms to be collected efficiently and managed centrally.

[0065] The data collection unit can use the emotion estimation function to collect posts in which family members express specific emotions in real time and reflect them in the diary. In the data collection unit, for example, the generation AI uses the emotion estimation function to collect posts in which family members express specific emotions in real time. For example, posts of joy or emotion are preferentially collected and reflected in the diary. The data collection unit also performs emotion estimation on the posts of family members and collects posts in which specific emotions are expressed in real time. This makes it possible to create an emotionally rich diary. In addition, the data collection unit can use the emotion estimation function to collect posts in which family members express specific emotions in real time and reflect them in the diary. This makes it possible to create a diary that reflects changes in emotions. This makes it possible to create an emotionally rich diary.

[0066] The data filtering unit can learn the privacy settings of family members and apply individually customized filtering rules. For example, the data filtering unit allows the generation AI to learn the privacy settings of family members and apply individually customized filtering rules. For example, the data filtering unit prioritizes filtering of posts from specific members. The data filtering unit also allows the generation AI to apply individually customized filtering rules based on the privacy settings of family members. This allows data to be collected while protecting the privacy of each member. The data filtering unit also allows the generation AI to learn the privacy settings of family members and apply individually customized filtering rules. This allows necessary data to be collected while protecting privacy. This allows data to be collected while protecting the privacy of each member.

[0067] The data filtering unit can filter out data containing negative emotions based on the emotional state of family members. For example, the generation AI analyzes the emotional state of family members and filters out data containing negative emotions. For example, it filters out posts of sadness or anger. The data filtering unit also takes into account the emotional state of family members and filters out data containing negative emotions. This makes it possible to collect only positive memories. The data filtering unit also analyzes the emotional state of family members and filters out data containing negative emotions. This makes it possible to create an emotionally reassuring diary. This makes it possible to collect only positive memories.

[0068] The data filtering unit automatically performs anonymization processing on the filtered data, thereby further enhancing privacy. The data filtering unit, for example, automatically performs anonymization processing on the data filtered by the generation AI. For example, personal information such as an individual's name and address is deleted. The data filtering unit also automatically performs anonymization processing on the filtered data by the generation AI, thereby enhancing privacy. This allows data to be shared with peace of mind. The data filtering unit also automatically performs anonymization processing on the filtered data by the generation AI, thereby further enhancing privacy. This makes it possible to reduce the risk of personal information being leaked. This makes it possible to reduce the risk of personal information being leaked.

[0069] The data filtering unit provides an interface that allows family members to manually set filtering rules, thereby realizing flexible privacy management. The data filtering unit, for example, provides an interface that allows family members to manually set filtering rules. For example, a setting is made to exclude specific keywords or phrases. The data filtering unit also provides an interface that allows family members to manually set filtering rules, thereby realizing flexible privacy management. This allows each member's privacy to be managed individually. The data filtering unit also provides an interface that allows family members to manually set filtering rules. This allows flexible data management according to the privacy protection needs of each member.

[0070] The data filtering unit can manage the filtered data by applying different levels of privacy protection to each family member. For example, the data filtering unit manages the data filtered by the generation AI by applying different levels of privacy protection to each family member. For example, stricter filtering is performed on children's data. The data filtering unit also manages the filtered data by applying different levels of privacy protection to each family member. This makes it possible to manage data according to the privacy protection needs of each family member. The data filtering unit also manages the data filtered by the generation AI by applying different levels of privacy protection to each family member. This increases the flexibility of privacy protection. This makes it possible to manage data according to the privacy protection needs of each family member.

[0071] The data filtering unit can use the emotion estimation function to automatically exclude data if a family member feels anxious about privacy. For example, the data filtering unit uses the emotion estimation function to automatically exclude data if the generation AI feels anxious about privacy. For example, data with a high emotion score of anxiety or fear is excluded. The data filtering unit also analyzes the emotional state of a family member and automatically excludes data if the family member feels anxious about privacy. This allows data to be shared with peace of mind. The data filtering unit also uses the emotion estimation function to automatically exclude data if the family member feels anxious about privacy. This increases the reliability of privacy protection.

[0072] The memory creation unit can analyze the emotional state of family members and prioritize creating memories that elicit positive emotions. For example, the memory creation unit uses a generation AI to analyze the emotional state of family members and prioritize creating memories that elicit positive emotions. For example, a diary can be created centered around fun events such as family trips and birthday parties. The memory creation unit can also analyze the emotional state of family members and prioritize creating memories that elicit positive emotions. This can deepen family bonds. The memory creation unit can also use a generation AI to analyze the emotional state of family members and prioritize creating memories that elicit positive emotions. This can create an emotionally rich diary. This can deepen family bonds.

[0073] The memory creation unit can learn from family members' past memories and automatically suggest new related memories. For example, the memory creation unit's generation AI learns from family members' past memories and automatically suggests new related memories. For example, it can suggest new travel destinations based on past travel destinations. The memory creation unit can also analyze family members' past memories and automatically suggest new related memories. This can enrich family memories. The memory creation unit's generation AI can also learn from family members' past memories and automatically suggest new related memories. This can create new memories that deepen family bonds. This can enrich family memories.

[0074] The memory creation unit can create personalized memories by taking into account the preferences and interests of family members. For example, the generation AI of the memory creation unit analyzes the preferences and interests of each family member to create personalized memories. For example, a diary may be created centered around activities that a specific member likes. The memory creation unit also considers the preferences and interests of each family member to create personalized memories. This allows for a diary that is special to each member. The memory creation unit also considers the preferences and interests of each family member to create personalized memories. This allows for a diary that the whole family can enjoy. This allows for a diary that is special to each member.

[0075] The memory creation unit provides a collaboration function that allows family members to edit memories together, enabling real-time collaboration. The memory creation unit provides a collaboration function that allows family members to edit memories together, for example, allowing editing to be done online in real time, so that everyone can participate. The memory creation unit also provides a collaboration function in which the generation AI supports family members' collaboration, allowing them to edit memories in real time, so that all family members can participate in creating memories. The memory creation unit also provides a collaboration function that allows family members to edit memories together, enabling real-time collaboration, so that all family members can create memories together.

[0076] The memory creation unit can integrate the memories of different family members and create a coherent story as a whole. For example, the memory creation unit uses a generation AI to integrate the memories of different family members and create a coherent story as a whole. For example, it creates a diary that incorporates the perspectives of each member. The memory creation unit also analyzes the memories of family members and the generation AI creates a coherent story. This makes it possible to bring together the memories of all family members into a single story. The memory creation unit also uses a generation AI to integrate the memories of different family members and create a coherent story as a whole. This makes it possible to create a diary that all family members can relate to. This makes it possible to bring together the memories of all family members into a single story.

[0077] The memory creation unit can use the emotion estimation function to identify the memory that moved the family member the most and compose a diary centered on that memory. In the memory creation unit, for example, the generation AI uses the emotion estimation function to identify the memory that moved the family member the most. For example, the diary can be composed centered on events with high emotion scores. The memory creation unit also analyzes the emotional states of family members and identifies the memory that moved them the most. This makes it possible to create an emotional diary. In addition, the memory creation unit can use the emotion estimation function to identify the memory that moved the family member the most and compose a diary centered on that memory. This makes it possible to create an emotionally rich diary.

[0078] The diary generation unit can analyze the emotional state of family members and automatically select a design and layout according to the emotion. In the diary generation unit, for example, the generation AI analyzes the emotional state of family members and automatically selects a design and layout according to the emotion. For example, a design with bright colors is selected for positive emotions. The diary generation unit also analyzes the emotional state of family members and the generation AI automatically selects a design and layout according to the emotion. This makes it possible to create a diary that matches the emotion. In addition, the diary generation unit analyzes the emotional state of family members and the generation AI automatically selects a design and layout according to the emotion. This makes it possible to create an emotionally rich diary. This makes it possible to create a diary that matches the emotion.

[0079] The diary generation unit can provide a function to automatically summarize the contents of the diary and highlight important events and highlights. For example, the diary generation unit provides a function in which the generation AI automatically summarizes the contents of the diary and highlights important events and highlights. For example, important events on a specific date are summarized and displayed. The diary generation unit also analyzes the contents of the diary, and the generation AI automatically summarizes and highlights important events and highlights. This allows the contents of the diary to be understood concisely. The diary generation unit also provides a function in which the generation AI automatically summarizes the contents of the diary and highlights important events and highlights. This allows the diary to be enjoyed without missing any important information. This allows the contents of the diary to be understood concisely.

[0080] The diary generation unit can learn feedback from family members and reflect it in the next automatic generation. In the diary generation unit, for example, the generation AI learns feedback from family members and reflects it in the next automatic generation of a diary. For example, it learns preferences for specific designs and layouts and applies them next time. The diary generation unit also analyzes feedback from family members and the generation AI reflects it in the next automatic generation of a diary. This makes it possible to create a diary that suits the user's preferences. In addition, the diary generation unit also uses the generation AI to learn feedback from family members and reflect it in the next automatic generation of a diary. This makes it possible to continuously improve the quality of the diary. This makes it possible to create a diary that suits the user's preferences.

[0081] The diary generation unit can provide a function to automatically generate diary content in different formats (for example, video diary or audio diary). The diary generation unit, for example, provides a function whereby the generation AI automatically generates diary content in different formats. For example, not only text diaries but also video diaries and audio diaries are created. The diary generation unit also analyzes the diary content and the generation AI automatically generates it in different formats. This makes it possible to preserve family memories in a variety of formats. The diary generation unit also provides a function whereby the generation AI automatically generates diary content in different formats. This makes it possible to preserve family memories in a richer way. This makes it possible to preserve family memories in a variety of formats.

[0082] The diary generation unit provides an interface that allows family members to manually edit the diary, and the generation AI can learn the edited content. The diary generation unit, for example, provides an interface that allows family members to manually edit the diary, and the generation AI can learn the edited content. For example, it learns comments and photos added by the user. The diary generation unit also allows the generation AI to learn the manual edited content of family members and reflect it in the next automatic diary generation. This makes it possible to create a diary that suits the user's preferences. The diary generation unit also provides an interface that allows family members to manually edit the diary, and the generation AI can learn the edited content. This makes it possible to continuously improve the quality of the diary.

[0083] The diary generation unit can use the emotion estimation function to identify the page that moved the family member the most and highlight that page. In the diary generation unit, for example, the generation AI uses the emotion estimation function to identify the page that moved the family member the most. For example, the diary generation unit highlights pages with high emotion scores. The diary generation unit also analyzes the emotional states of the family members and identifies the page that moved them the most. This makes it possible to highlight the moving page. In addition, the diary generation unit uses the emotion estimation function to identify the page that moved the family member the most and highlights that page. This makes it possible to create an emotionally rich diary.

[0084] The sharing unit can analyze the emotional state of family members and suggest a sharing method according to the emotion (for example, notification at a specific timing). In the sharing unit, for example, the generation AI analyzes the emotional state of family members and suggests a sharing method according to the emotion. For example, a notification to share a diary is sent when the emotion is positive. The sharing unit also analyzes the emotional state of family members and the generation AI suggests a sharing method according to the emotion. This makes it possible to share a diary at a timing that suits the emotion. In addition, the sharing unit also analyzes the emotional state of family members and the generation AI suggests a sharing method according to the emotion. This makes it possible to share an emotionally rich diary. This makes it possible to share a diary at a timing that suits the emotion.

[0085] The sharing unit can provide a function to automatically back up diary contents and prevent data loss. For example, the sharing unit provides a function that allows the generation AI to automatically back up diary contents and prevent data loss. For example, regular backups are made to cloud storage. The sharing unit also analyzes the diary contents and the generation AI automatically backs them up. This prevents data loss and allows the diary to be stored with peace of mind. The sharing unit also provides a function that allows the generation AI to automatically back up diary contents and prevent data loss. This allows important memories to be stored safely. This prevents data loss and allows the diary to be stored with peace of mind.

[0086] The sharing unit can learn feedback from family members and reflect it in the next sharing method. In the sharing unit, for example, the generation AI learns feedback from family members and reflects it in the next diary sharing method. For example, the sharing unit learns preferences for specific sharing methods and timing and applies them next time. The sharing unit also analyzes feedback from family members and the generation AI reflects it in the next diary sharing method. This makes it possible to provide a sharing method that suits the user's preferences. In addition, the sharing unit can learn feedback from family members and reflect it in the next diary sharing method. This makes it possible to continuously improve the diary sharing method. This makes it possible to provide a sharing method that suits the user's preferences.

[0087] The sharing unit can optimize the diary so that it can be viewed on different devices (e.g., smartphone, tablet, PC). For example, the sharing unit optimizes the diary so that the generation AI can view it on different devices. For example, it provides a layout that is compatible with devices such as smartphones, tablets, and PCs. The sharing unit also analyzes the contents of the diary and optimizes it so that the generation AI can view it on different devices. This allows all family members to enjoy the diary on any device. The sharing unit also optimizes the diary so that the generation AI can view it on different devices. This allows all family members to view the diary anytime, anywhere. This allows all family members to enjoy the diary on any device.

[0088] The sharing section can provide a function that allows family members to select and share only specific pages or sections when sharing a diary. For example, the sharing section provides a function that allows the generation AI to select and share only specific pages or sections when sharing a diary. For example, selecting and sharing specific events or memories. The sharing section also provides a function that analyzes the contents of the diary and allows the generation AI to select and share only specific pages or sections. This makes it possible to share only the parts that are necessary. The sharing section also provides a function that allows the generation AI to select and share only specific pages or sections when sharing a diary. This makes it possible to share a diary while protecting privacy. This makes it possible to share only the parts that are necessary.

[0089] The sharing unit can use the emotion estimation function to identify the page that moved the family member the most and share that page preferentially. In the sharing unit, for example, the generation AI uses the emotion estimation function to identify the page that moved the family member the most. For example, pages with high emotion scores are shared preferentially. The sharing unit also analyzes the emotional state of the family member and identifies the page that moved them the most. This allows the emotional page to be shared preferentially. In addition, the sharing unit can use the emotion estimation function to identify the page that moved the family member the most and share that page preferentially. This allows the sharing of an emotionally rich diary.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The data collection unit can also collect health data of family members and reflect it in the diary. For example, it can collect data from smartwatches and fitness trackers and record the family members' health conditions and activity levels in the diary. The data collection unit can also collect food logs of family members and reflect it in the diary. For example, it can collect photos of meals and calorie information to record the family members' eating habits. The data collection unit can also collect sleep data of family members and reflect it in the diary. For example, it can record sleep duration and quality to help manage the family members' health. This allows for a comprehensive record of the family members' health conditions and can be used for health management.

[0092] The data collection unit can also collect data related to the hobbies and interests of family members and reflect it in the diary. For example, it can collect information about events and hobbies that family members have participated in and record it in the diary. The data collection unit can also collect reading histories of family members and reflect them in the diary. For example, it can record the titles of books read and their impressions, and share the family's reading activities. The data collection unit can also collect movie and TV program viewing histories of family members and reflect them in the diary. For example, it can record the titles of movies and programs watched and their impressions, and share the family's entertainment activities. This allows the family's hobbies and interests to be comprehensively recorded and shared.

[0093] The data collection unit can also collect information about family members' travel destinations and reflect it in the diary. For example, it can collect information about tourist attractions and restaurants at travel destinations to enrich travel memories. The data collection unit can also collect photos and videos taken by family members during their trip and reflect them in the diary. For example, it can collect photos and videos taken during the trip to visually record travel memories. The data collection unit can also collect family members' impressions and reviews during the trip and reflect them in the diary. For example, it can record experiences and impressions at travel destinations and share travel memories. This allows for enriched recording and sharing of family travel memories.

[0094] The data collection unit can also collect data related to family members' learning activities and reflect it in the diary. For example, it can collect participation history in online courses and workshops and record learning progress. The data collection unit can also collect family members' learning outcomes and projects and reflect them in the diary. For example, it can record completed projects and acquired qualifications and share learning outcomes. The data collection unit can also collect family members' impressions and feedback on learning and reflect them in the diary. For example, it can record what they learned and their feelings and share their learning process. This makes it possible to comprehensively record and share family members' learning activities.

[0095] The data collection unit can also collect data related to the creative activities of family members and reflect it in the diary. For example, it can collect artwork and handmade items created by family members and record their creative activities. The data collection unit can also collect data related to the musical activities of family members and reflect it in the diary. For example, it can record the songs played and the music composed and share the musical activities. The data collection unit can also collect data related to the cooking activities of family members and reflect it in the diary. For example, it can record recipes and photos of the dishes made and share cooking memories. This makes it possible to comprehensively record and share the creative activities of the family.

[0096] The data collection unit can also estimate the emotional states of family members and filter out posts with negative emotions. For example, it can filter out posts with sadness or anger and collect only positive posts. The data collection unit can also estimate the emotional states of family members and prioritize collecting posts with positive emotions. For example, it can prioritize collecting posts with joy or emotion to create an emotionally rich diary. The data collection unit can also estimate the emotional states of family members and create a diary that reflects changes in emotions. For example, it can adjust the content and design of the diary according to changes in emotions to create an emotionally rich diary. In this way, a diary that reflects the emotional states of the family can be created.

[0097] The data collection unit can also estimate the emotional states of family members and collect posts with specific emotions in real time. For example, posts with emotions of joy or emotion can be collected in real time and reflected in the diary. The data collection unit can also estimate the emotional states of family members and create a diary that reflects changes in emotions in real time. For example, the data collection unit can adjust the content and design of the diary in real time according to changes in emotions. The data collection unit can also estimate the emotional states of family members and create a diary that reflects changes in emotions in real time. This makes it possible to create an emotionally rich diary in real time.

[0098] The data collection unit can also estimate the emotional state of family members and automatically select a design and layout according to the emotion. For example, a bright colored design is selected for positive emotions. The data collection unit can also estimate the emotional state of family members and automatically select a design and layout according to the emotion. This makes it possible to create a diary that matches the emotion. The data collection unit can also estimate the emotional state of family members and automatically select a design and layout according to the emotion. This makes it possible to create a diary that is rich in emotion. This makes it possible to create a diary that matches the emotion.

[0099] The data collection unit can also estimate the emotional state of family members and suggest a sharing method according to the emotion. For example, it can send a notification to share the diary when the emotion is positive. The data collection unit can also estimate the emotional state of family members and suggest a sharing method according to the emotion. This allows the diary to be shared at a timing that matches the emotion ... with a richer emotional content. This allows the diary to be shared at a timing that matches the emotion.

[0100] The data collection unit can also estimate the emotional states of family members and apply filtering rules according to the emotions. For example, posts with negative emotions are excluded and only positive posts are collected. The data collection unit can also estimate the emotional states of family members and apply filtering rules according to the emotions. This makes it possible to create an emotionally rich diary. The data collection unit can also estimate the emotional states of family members and apply filtering rules according to the emotions. This makes it possible to create an emotionally rich diary.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data collection unit collects the family's social media or activity logs, such as social media posts, photos, location information, and activity history. Step 2: The data filtering unit filters the data collected by the data collection unit to protect privacy. For example, posts containing personal information or private content are automatically filtered out. Step 3: The memory creation unit creates shared memories based on the data filtered by the data filtering unit. For example, it compiles family trips, events, and everyday happenings into a diary. Step 4: The diary generation unit generates a diary based on the memories created by the memory creation unit. For example, it organizes events by date and creates pages by combining photos and text. Step 5: The sharing unit shares the diary created by the diary creation unit among family members, for example, by digitally storing it in the cloud so that all family members can access it.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 data collection unit that collects family members' SNS or behavior logs; a data filtering unit that filters the data collected by the data collecting unit for privacy protection; a memory creation unit that creates shared memories based on the data filtered by the data filtering unit; a diary creation unit that creates a diary based on the memories created by the memory creation unit; a sharing unit for sharing the diary created by the diary creating unit among family members; A system characterized by:

2. The data collection unit Sentiment analysis of the social media posts of family members is performed, and posts with positive sentiment are preferentially collected.

2. The system of claim 1.

3. The data collection unit Automatically tagging and collecting the data related to specific events or activities based on the behavioral logs of family members.

2. The system of claim 1.

4. The data collection unit Learn the past behavioral patterns of family members, predict important future events, and collect data in advance 2. The system of claim 1.

5. The data collection unit Collect voice messages or video logs from family members and integrate them with text data to create the diary.

2. The system of claim 1.

6. The data collection unit Automate data collection across different social media platforms and manage data in a unified format 2. The system of claim 1.

Images