System

Abstract

An object of the system according to the embodiment is to move the grave of the deceased to the cloud and solve the problem of the cost and distance of maintenance and management.SOLUTION: A system includes a grave construction part on a cloud, a database part, and a generation AI part. The grave construction unit on the cloud transfers the grave of the deceased to the cloud. The database unit stores a certificate of life of the deceased. A generation AI part creates memories of the deceased.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] With conventional technology, there are issues with maintaining the graves of the deceased and the cost and distance of visiting the graves, as well as the issue of a lack of successors.

[0005] The system of the embodiment aims to migrate the graves of deceased people to the cloud and solve the problems of maintenance costs and distance. [Means for solving the problem]

[0006] The system according to the embodiment comprises a cloud-based gravestone construction unit, a database unit, and a generation AI unit. The cloud-based gravestone construction unit transfers the deceased person's grave to the cloud. The database unit stores proof of the deceased person's life. The generation AI unit creates memories of the deceased. [Effects of the Invention]

[0007] The system of the embodiment can migrate the graves of deceased people to the cloud, solving the problems of maintenance costs and distance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The cloud grave system according to an embodiment of the present invention is a system that migrates the grave of the deceased to the cloud and uses AI to efficiently create memories of the deceased. This solves problems such as the lack of a successor, the cost of maintenance, and the difficulty of visiting the grave due to living far away.

[0029] The cloud grave system according to the embodiment includes a cloud-based grave construction unit, a database unit, and a generation AI unit. The cloud-based grave construction unit migrates the deceased's grave to the cloud. For example, it converts physical grave information into digital data and stores it on the cloud. The cloud-based grave construction unit can also upload the deceased's photos and videos to the cloud. The database unit stores evidence of the deceased's life. For example, it stores data such as the deceased's photos, videos, audio messages, and letters. The database unit can also store the deceased's social media posts and activity history. The generation AI unit creates memories of the deceased. For example, the generation AI can analyze the deceased's photos and videos and create a slideshow of memories. The generation AI can also create short films based on episodes from the deceased's life. The generation AI can also generate poems and prose that reflect the deceased's emotions. This allows the cloud grave system to migrate the deceased's grave to the cloud and efficiently create memories of the deceased.

[0030] The database unit can automatically collect the deceased's activity history and social media posts to build a more detailed database. For example, the database unit can automatically collect posts from the deceased's social media accounts and store them in a cloud-based gravestone. For example, it can analyze Facebook and Twitter posts and add them to the database as memories of the deceased. The database unit can also collect the deceased's GPS data and store their activity history. For example, it can add the history of places the deceased visited and events they attended to the database. The database unit can also collect the deceased's purchasing history and record their activities in detail. For example, it can add the history of products and services the deceased purchased to the database. This allows for the construction of a detailed database of the deceased.

[0031] The database unit can automatically suggest related content based on the deceased's hobbies and interests and add it to the cloud-based gravestone. For example, the database unit can analyze the deceased's music playlist and automatically suggest related songs to add to the cloud-based gravestone. For example, it can suggest memorable songs based on Spotify or Apple Music playlists. The database unit can also analyze the deceased's reading history and suggest related books. For example, it can add related books to the database based on a list of books the deceased read. The database unit can also analyze the deceased's movie-watching history and suggest related movies. For example, it can add related movies to the database based on a list of movies the deceased watched. This allows content based on the deceased's hobbies and interests to be added.

[0032] A cloud-based grave can be enhanced with a feature that allows a digital avatar of the deceased to be created and allows family and friends to interact with it. For example, a cloud-based grave can create a digital avatar based on photos and videos of the deceased, allowing family and friends to interact with it. For example, an avatar can be created that reproduces the voice and facial expressions of the deceased. A cloud-based grave can also provide an interactive storybook based on episodes from the deceased's life. For example, a storybook can be generated based on important events in the deceased's life, which family and friends can view interactively. A cloud-based grave can also provide interactive content based on the hobbies and interests of the deceased. For example, interactive content can be provided based on the music and movies that the deceased liked. This allows family and friends to interact with the deceased through the digital avatar of the deceased.

[0033] Virtual reality and augmented reality technologies can be used to create a cloud-based grave that gives the feeling of actually visiting. For example, a cloud-based grave can use VR technology to create a system that gives the feeling of actually visiting a cloud-based grave. For example, a VR experience can be provided that recreates the deceased's tombstone and the surrounding scenery. Cloud-based graves can also use AR technology to recreate places that hold special memories for the deceased. For example, places the deceased visited or had special memories for can be recreated using AR, allowing family and friends to experience them through a smartphone or tablet. Cloud-based graves can also use VR and AR technologies to recreate episodes from the deceased's life. For example, special moments the deceased spent with their family can be recreated using VR or AR, allowing family and friends to experience them. This allows the use of VR and AR technologies to create a feeling of actually visiting a grave.

[0034] The generation AI can analyze the conversations and messages of the deceased while they were alive and generate a memorial message that reproduces the words of the deceased. For example, the generation AI can analyze the emails and messages of the deceased and generate a memorial message that reproduces the words of the deceased. For example, it can reproduce the words of the deceased based on their interactions with family and friends. The generation AI can also analyze the voice messages of the deceased and reproduce those words. For example, it can generate a memorial message based on a voice message the deceased wrote to their family. The generation AI can also analyze the letters the deceased wrote and reproduce those words. For example, it can generate a memorial message based on a letter the deceased wrote to a friend. In this way, it is possible to generate a memorial message that reproduces the words of the deceased.

[0035] Generative AI can analyze photos and videos of the deceased and automatically create slideshows and video montages. For example, generative AI can analyze photos of the deceased and automatically create slideshows. For example, it can arrange photos of the deceased from their lifetime in chronological order to generate a slideshow of memories. Generative AI can also analyze videos of the deceased and automatically create video montages. For example, it can edit videos of the deceased from their lifetime to generate a video montage of memories. Generative AI can also combine photos and videos of the deceased to create slideshows and video montages. For example, it can combine photos and videos of the deceased to generate a slideshow of memories. This makes it possible to create slideshows and video montages based on photos and videos of the deceased.

[0036] Generative AI can generate an interactive storybook based on the memories of the deceased, allowing family and friends to relive the life of the deceased. For example, generative AI creates an interactive storybook based on episodes from the deceased's life. For example, it generates a storybook based on important events in the deceased's life. Generative AI can also create an interactive storybook based on photos and videos of the deceased. For example, it can generate an interactive storybook by combining photos and videos of the deceased from their life. Generative AI can also create an interactive storybook based on letters and audio messages from the deceased. For example, it can generate an interactive storybook based on letters and audio messages that the deceased wrote to their family. In this way, an interactive storybook can be generated based on the memories of the deceased.

[0037] A digital archive can automatically collect and preserve the activities and achievements of a deceased person during their lifetime. For example, a digital archive can automatically collect the work history and achievements of a deceased person and preserve them as a digital archive. For example, a resume and performance report can be analyzed and added to a database. A digital archive can also automatically collect and preserve the papers and patents of a deceased person. For example, papers written by the deceased and patents obtained by the deceased can be added to a database. A digital archive can also automatically collect and preserve the awards and certificates of commendation of a deceased person. For example, awards and certificates of commendation received by the deceased can be added to a database. In this way, the activities and achievements of a deceased person can be preserved as a digital archive.

[0038] Messages and memories can be collected from the deceased's friends and acquaintances and added to the cloud-based gravestone. For example, messages from the deceased's friends and acquaintances can be collected and added to the cloud-based gravestone. For example, emails and social media messages can be analyzed and added to a database as memories. Letters from the deceased's friends and acquaintances can also be collected and added to the cloud-based gravestone. For example, memories can be added to a database based on letters written by the deceased to friends. Voice messages from the deceased's friends and acquaintances can also be collected and added to the cloud-based gravestone. For example, voice messages written by the deceased to friends can be added to a database as memories. In this way, messages and memories from the deceased's friends and acquaintances can be collected and added to the cloud-based gravestone.

[0039] A digital time capsule can be created based on the activities of the deceased during their lifetime and can be automatically made public at a specific date and time. For example, a digital time capsule can be created based on the activities of the deceased during their lifetime and automatically made public on the deceased's birthday or death anniversary. For example, photos and videos of the deceased can be saved in the time capsule and made public at a specific date and time. A digital time capsule can also be created based on letters and voice messages from the deceased and made public at a specific date and time. For example, letters and voice messages from the deceased to family members can be saved in the time capsule and made public at a specific date and time. A digital time capsule can also be created based on episodes from the deceased's lifetime and made public at a specific date and time. For example, special moments spent with friends can be saved in the time capsule and made public at a specific date and time. In this way, a digital time capsule can be created based on the activities of the deceased and made public at a specific date and time.

[0040] Digital artworks can be generated based on memories of the deceased and displayed on the cloud-based gravestone. For example, digital artworks can be generated based on photographs and videos of the deceased and displayed on the cloud-based gravestone. For example, photographs of the deceased from their lifetime can be generated as artwork and added to a database. Digital artworks can also be generated based on letters and voice messages from the deceased and displayed on the cloud-based gravestone. For example, artwork can be generated based on letters and voice messages from the deceased to their family and added to a database. Digital artworks can also be generated based on episodes from the deceased's lifetime and displayed on the cloud-based gravestone. For example, artwork can be generated based on special moments the deceased spent with friends and added to a database. In this way, digital artworks based on the memories of the deceased can be generated and displayed.

[0041] Dedicated apps can be developed to facilitate remote access to cloud-based graves. For example, a dedicated app can be developed to access cloud-based graves, making it easy to visit the grave even from a remote location. For example, an app can be provided for smartphones and tablets. Cloud-based graves also allow users to view memories of the deceased through a dedicated app. For example, photos and videos of the deceased can be viewed through the dedicated app. Cloud-based graves also allow users to share stories from the deceased's life through the dedicated app. For example, special moments the deceased spent with their family can be shared through the dedicated app. This allows for the development of dedicated apps to facilitate remote access.

[0042] Virtual tours are provided based on the activities and memories of the deceased during their lifetime, allowing family and friends to remember the deceased even from a remote location. For example, a system is constructed to provide virtual tours based on the activities and memories of the deceased during their lifetime. For example, a virtual tour recreating places the deceased visited or had fond memories of is provided. Virtual tours can also be provided based on episodes from the deceased's lifetime. For example, a virtual tour recreating special moments the deceased spent with their family is provided. Virtual tours can also provide content based on the hobbies and interests of the deceased. For example, a virtual tour based on the music or movies the deceased liked is provided. In this way, a virtual tour based on the activities and memories of the deceased can be provided, allowing the deceased to be remembered even from a remote location.

[0043] Dedicated devices can be developed to facilitate remote access to cloud-based graves. For example, dedicated devices can be developed to access cloud-based graves, making it easy to visit the grave even from a remote location. For example, dedicated tablets or smart displays can be provided. Cloud-based graves also allow for viewing of memories of the deceased through dedicated devices. For example, photos and videos of the deceased can be viewed on dedicated devices. Cloud-based graves also allow sharing of stories from the deceased's life through dedicated devices. For example, special moments the deceased spent with their family can be shared on dedicated devices. This allows for the development of dedicated devices that facilitate remote access.

[0044] Virtual reality can be provided based on the activities and memories of the deceased, allowing family and friends to remember the deceased even from a remote location. For example, virtual reality can be used to build a system that provides VR tours based on the activities and memories of the deceased. For example, a VR tour can be provided that recreates places the deceased visited or places that held memories for the deceased. Virtual reality can also be provided based on episodes from the deceased's life. For example, a VR tour can be provided that recreates special moments the deceased spent with their family. Virtual reality can also provide content based on the hobbies and interests of the deceased. For example, a VR tour can be provided based on the music or movies the deceased liked. This allows VR tours based on the activities and memories of the deceased to be provided, allowing family and friends to remember the deceased even from a remote location.

[0045] The database is automatically backed up, ensuring data safety. For example, a system can be built to periodically and automatically back up the cloud-based gravestone database. For example, a daily or weekly backup schedule can be set. The database can also encrypt and securely store the backup data. For example, the backup data can be encrypted and stored in cloud storage. The database can also set up recovery procedures for the backup data to ensure data safety. For example, procedures can be set up to quickly restore the backup data. This allows the cloud-based gravestone database to be automatically backed up, ensuring data safety.

[0046] The database is updated regularly, and new memories and memorial content can be added. For example, a system can be built to regularly update the cloud-based grave database and add new memories and memorial content. For example, a monthly update schedule can be set. The database can also accept posts from users and add new memories and memorial content. For example, family and friends can post memories of the deceased and add them to the database. The database can also automatically generate and add new memorial content using generative AI. For example, a memorial video based on episodes from the deceased's life can be generated and added to the database. This allows the cloud-based grave database to be regularly updated and new memories and memorial content can be added.

[0047] The database can be linked with other cloud services to share and integrate data. For example, the database can link the cloud-based gravestone database with other cloud services such as Google Drive or Dropbox to share and integrate data. For example, a data synchronization function can be added. The database can also share data with other cloud services through API integration. For example, an API can be used to send and share data with other cloud services. The database can also integrate data with other cloud services through data migration. For example, data can be migrated to and integrated with other cloud services. This allows the cloud-based gravestone database to be linked with other cloud services to share and integrate data.

[0048] A database-based memorial app can make it easy for family and friends to access the database. For example, a memorial app based on a cloud-based database of graves can be developed to make it easy for family and friends to access the database. For example, an app for smartphones or tablets can be provided. The memorial app can also provide a user-friendly interface to make it easy for family and friends to operate. For example, an interface that allows intuitive operation can be provided. The memorial app can also provide a single sign-on function to make it easy for family and friends to log in. For example, a single sign-on function can be provided that allows access to multiple services with a single login. This allows a memorial app based on a cloud-based database of graves to be developed to make it easy for family and friends to access the database.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The cloud grave system can also create a digital time capsule based on the activities of the deceased during their lifetime and automatically publish it at a specific date and time. For example, photos and videos of the deceased can be saved in a time capsule to coincide with the deceased's birthday or death anniversary, and published at a specific date and time. It can also create a digital time capsule based on the deceased's letters and voice messages and publish it at a specific date and time. This allows a digital time capsule to be created based on the activities of the deceased during their lifetime and published at a specific date and time.

[0051] The cloud grave system can also be equipped with a digital archive that automatically collects and preserves the activities and achievements of the deceased. For example, the work history and achievements of the deceased can be automatically collected and preserved as a digital archive. Resumes and performance reports can be analyzed and added to the database. The deceased's papers and patents can also be automatically collected and preserved. This allows the deceased's activities and achievements to be preserved as a digital archive.

[0052] The cloud grave system can also collect messages and memories from friends and acquaintances of the deceased and add them to the cloud grave. For example, it can analyze emails and social media messages from the deceased's friends and acquaintances and add them to a database as memories. It can also collect letters and voice messages from the deceased's friends and acquaintances and add them to the cloud grave. This allows messages and memories from the deceased's friends and acquaintances to be collected and added to the cloud grave.

[0053] The cloud grave system can also generate digital artwork based on the deceased's activities and memories and display it on the cloud grave. For example, digital artwork can be generated based on the deceased's photos and videos and added to the database. Artwork generated based on the deceased's letters and voice messages can also be displayed on the cloud grave. This allows digital artwork to be generated and displayed based on the deceased's memories.

[0054] The cloud grave system can also provide virtual reality tours based on the activities and memories of the deceased. For example, it can provide a VR tour that recreates the places the deceased visited or had fond memories of. It can also provide VR tours based on episodes from the deceased's life. This allows family and friends to remember the deceased even from a distance by providing a VR tour based on the deceased's activities and memories.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The cloud-based grave construction unit transfers the deceased person's grave to the cloud. For example, it converts the physical grave information into digital data and stores it on the cloud. The cloud-based grave construction unit can also upload photos and videos of the deceased to the cloud. Step 2: The database unit stores evidence of the deceased's life, such as photos, videos, voice messages, and letters. The database unit can also store the deceased's social media posts and activity history. Step 3: The generative AI creates memories of the deceased. For example, the generative AI analyzes photos and videos of the deceased and creates a slideshow of memories. The generative AI can also create a short film based on episodes from the deceased's life. The generative AI can also generate poems and writings that reflect the emotions of the deceased.

[0057] (Example 2) The cloud grave system according to an embodiment of the present invention is a system that migrates the grave of the deceased to the cloud and uses AI to efficiently create memories of the deceased. This solves problems such as the lack of a successor, the cost of maintenance, and the difficulty of visiting the grave due to living far away.

[0058] The cloud grave system according to the embodiment includes a cloud-based grave construction unit, a database unit, and a generation AI unit. The cloud-based grave construction unit migrates the deceased's grave to the cloud. For example, it converts physical grave information into digital data and stores it on the cloud. The cloud-based grave construction unit can also upload the deceased's photos and videos to the cloud. The database unit stores evidence of the deceased's life. For example, it stores data such as the deceased's photos, videos, audio messages, and letters. The database unit can also store the deceased's social media posts and activity history. The generation AI unit creates memories of the deceased. For example, the generation AI can analyze the deceased's photos and videos and create a slideshow of memories. The generation AI can also create short films based on episodes from the deceased's life. The generation AI can also generate poems and prose that reflect the deceased's emotions. This allows the cloud grave system to migrate the deceased's grave to the cloud and efficiently create memories of the deceased.

[0059] The database unit can automatically collect the deceased's activity history and social media posts to build a more detailed database. For example, the database unit can automatically collect posts from the deceased's social media accounts and store them in a cloud-based gravestone. For example, it can analyze Facebook and Twitter posts and add them to the database as memories of the deceased. The database unit can also collect the deceased's GPS data and store their activity history. For example, it can add the history of places the deceased visited and events they attended to the database. The database unit can also collect the deceased's purchasing history and record their activities in detail. For example, it can add the history of products and services the deceased purchased to the database. This allows for the construction of a detailed database of the deceased.

[0060] The database unit can automatically suggest related content based on the deceased's hobbies and interests and add it to the cloud-based gravestone. For example, the database unit can analyze the deceased's music playlist and automatically suggest related songs to add to the cloud-based gravestone. For example, it can suggest memorable songs based on Spotify or Apple Music playlists. The database unit can also analyze the deceased's reading history and suggest related books. For example, it can add related books to the database based on a list of books the deceased read. The database unit can also analyze the deceased's movie-watching history and suggest related movies. For example, it can add related movies to the database based on a list of movies the deceased watched. This allows content based on the deceased's hobbies and interests to be added.

[0061] The database unit can use the emotion estimation function to analyze the emotions of the deceased during their lifetime and generate memorial content based on those emotions. For example, the database unit can analyze the emotions in the deceased's social media posts and generate memorial content based on posts with strong positive emotions. For example, posts expressing joy or gratitude can be extracted and added to the database as memories. The database unit can also analyze the emotions in the deceased's voice messages and generate memorial content based on those emotions. For example, a memorial video can be generated based on a voice message in which the deceased recounts an event that moved them. The database unit can also analyze the emotions in the deceased's letters and generate memorial content based on those emotions. For example, a memorial album can be generated based on a letter of gratitude written by the deceased to their family. This allows memorial content to be generated based on the deceased's emotions.

[0062] A cloud-based grave can be enhanced with a feature that allows a digital avatar of the deceased to be created and allows family and friends to interact with it. For example, a cloud-based grave can create a digital avatar based on photos and videos of the deceased, allowing family and friends to interact with it. For example, an avatar can be created that reproduces the voice and facial expressions of the deceased. A cloud-based grave can also provide an interactive storybook based on episodes from the deceased's life. For example, a storybook can be generated based on important events in the deceased's life, which family and friends can view interactively. A cloud-based grave can also provide interactive content based on the hobbies and interests of the deceased. For example, interactive content can be provided based on the music and movies that the deceased liked. This allows family and friends to interact with the deceased through the digital avatar of the deceased.

[0063] Virtual reality and augmented reality technologies can be used to create a cloud-based grave that gives the feeling of actually visiting. For example, a cloud-based grave can use VR technology to create a system that gives the feeling of actually visiting a cloud-based grave. For example, a VR experience can be provided that recreates the deceased's tombstone and the surrounding scenery. Cloud-based graves can also use AR technology to recreate places that hold special memories for the deceased. For example, places the deceased visited or had special memories for can be recreated using AR, allowing family and friends to experience them through a smartphone or tablet. Cloud-based graves can also use VR and AR technologies to recreate episodes from the deceased's life. For example, special moments the deceased spent with their family can be recreated using VR or AR, allowing family and friends to experience them. This allows the use of VR and AR technologies to create a feeling of actually visiting a grave.

[0064] A cloud-based grave can use emotion estimation to provide a virtual tour based on the emotions of the deceased during their lifetime, allowing family and friends to relive their memories of the deceased. For example, a cloud-based grave can use emotion estimation to build a system that provides a virtual tour based on the emotions of the deceased during their lifetime. For example, a virtual tour that recreates places and events that particularly moved the deceased is provided. The cloud-based grave can also provide an interactive storybook that reflects the emotions of the deceased during their lifetime. For example, a storybook can be generated based on important events in the deceased's life, which can be viewed interactively by family and friends. The cloud-based grave can also provide memorial content based on the emotions of the deceased. For example, a memorial video can be generated based on events that particularly moved the deceased, which can be viewed by family and friends. This makes it possible to provide a virtual tour based on the emotions of the deceased.

[0065] The generation AI can analyze the conversations and messages of the deceased while they were alive and generate a memorial message that reproduces the words of the deceased. For example, the generation AI can analyze the emails and messages of the deceased and generate a memorial message that reproduces the words of the deceased. For example, it can reproduce the words of the deceased based on their interactions with family and friends. The generation AI can also analyze the voice messages of the deceased and reproduce those words. For example, it can generate a memorial message based on a voice message the deceased wrote to their family. The generation AI can also analyze the letters the deceased wrote and reproduce those words. For example, it can generate a memorial message based on a letter the deceased wrote to a friend. In this way, it is possible to generate a memorial message that reproduces the words of the deceased.

[0066] Generative AI can analyze photos and videos of the deceased and automatically create slideshows and video montages. For example, generative AI can analyze photos of the deceased and automatically create slideshows. For example, it can arrange photos of the deceased from their lifetime in chronological order to generate a slideshow of memories. Generative AI can also analyze videos of the deceased and automatically create video montages. For example, it can edit videos of the deceased from their lifetime to generate a video montage of memories. Generative AI can also combine photos and videos of the deceased to create slideshows and video montages. For example, it can combine photos and videos of the deceased to generate a slideshow of memories. This makes it possible to create slideshows and video montages based on photos and videos of the deceased.

[0067] The generative AI can use its emotion estimation function to generate poems and writings that reflect the emotions of the deceased during their lifetime and store them on a cloud-based gravestone. For example, the generative AI can analyze the emotions in the deceased's social media posts and messages and generate poems and writings that reflect those emotions. For example, it can generate poems and writings based on events that particularly moved the deceased. The generative AI can also analyze the emotions in the deceased's letters and generate poems and writings that reflect those emotions. For example, it can generate poems and writings based on letters of gratitude written by the deceased to their family. The generative AI can also analyze the emotions in the deceased's voice messages and generate poems and writings that reflect those emotions. For example, it can generate poems and writings based on voice messages written by the deceased to friends. In this way, poems and writings that reflect the emotions of the deceased can be generated and stored.

[0068] Generative AI can generate an interactive storybook based on the memories of the deceased, allowing family and friends to relive the life of the deceased. For example, generative AI creates an interactive storybook based on episodes from the deceased's life. For example, it generates a storybook based on important events in the deceased's life. Generative AI can also create an interactive storybook based on photos and videos of the deceased. For example, it can generate an interactive storybook by combining photos and videos of the deceased from their life. Generative AI can also create an interactive storybook based on letters and audio messages from the deceased. For example, it can generate an interactive storybook based on letters and audio messages that the deceased wrote to their family. In this way, an interactive storybook can be generated based on the memories of the deceased.

[0069] The generative AI can use its emotion estimation function to generate a music album based on the emotions of the deceased during their lifetime and store it on a cloud-based grave. For example, the generative AI can analyze the emotions in the deceased's social media posts and messages and generate a music album based on those emotions. For example, it can generate music based on events that particularly moved the deceased. The generative AI can also analyze the emotions in the deceased's voice messages and generate a music album based on those emotions. For example, it can generate a music album based on a voice message of gratitude that the deceased wrote to their family. The generative AI can also analyze the emotions in the deceased's letters and generate a music album based on those emotions. For example, it can generate a music album based on a letter the deceased wrote to a friend. In this way, a music album based on the emotions of the deceased can be generated and stored.

[0070] A digital archive can automatically collect and preserve the activities and achievements of a deceased person during their lifetime. For example, a digital archive can automatically collect the work history and achievements of a deceased person and preserve them as a digital archive. For example, a resume and performance report can be analyzed and added to a database. A digital archive can also automatically collect and preserve the papers and patents of a deceased person. For example, papers written by the deceased and patents obtained by the deceased can be added to a database. A digital archive can also automatically collect and preserve the awards and certificates of commendation of a deceased person. For example, awards and certificates of commendation received by the deceased can be added to a database. In this way, the activities and achievements of a deceased person can be preserved as a digital archive.

[0071] Messages and memories can be collected from the deceased's friends and acquaintances and added to the cloud-based gravestone. For example, messages from the deceased's friends and acquaintances can be collected and added to the cloud-based gravestone. For example, emails and social media messages can be analyzed and added to a database as memories. Letters from the deceased's friends and acquaintances can also be collected and added to the cloud-based gravestone. For example, memories can be added to a database based on letters written by the deceased to friends. Voice messages from the deceased's friends and acquaintances can also be collected and added to the cloud-based gravestone. For example, voice messages written by the deceased to friends can be added to a database as memories. In this way, messages and memories from the deceased's friends and acquaintances can be collected and added to the cloud-based gravestone.

[0072] The emotion estimation function can create a digital memorial that reflects the emotions of the deceased during their lifetime and share it with family and friends. For example, the emotion estimation function can analyze the emotions in the deceased's social media posts and messages and create a digital memorial that reflects those emotions. For example, a digital memorial can be generated based on an event that particularly moved the deceased. The emotion estimation function can also analyze the emotions in the deceased's voice messages and create a digital memorial that reflects those emotions. For example, a digital memorial can be generated based on a voice message of gratitude that the deceased wrote to their family. The emotion estimation function can also analyze the emotions in letters written by the deceased and create a digital memorial that reflects those emotions. For example, a digital memorial can be generated based on a letter written by the deceased to a friend. This allows a digital memorial that reflects the emotions of the deceased to be created and shared.

[0073] A digital time capsule can be created based on the activities of the deceased during their lifetime and can be automatically made public at a specific date and time. For example, a digital time capsule can be created based on the activities of the deceased during their lifetime and automatically made public on the deceased's birthday or death anniversary. For example, photos and videos of the deceased can be saved in the time capsule and made public at a specific date and time. A digital time capsule can also be created based on letters and voice messages from the deceased and made public at a specific date and time. For example, letters and voice messages from the deceased to family members can be saved in the time capsule and made public at a specific date and time. A digital time capsule can also be created based on episodes from the deceased's lifetime and made public at a specific date and time. For example, special moments spent with friends can be saved in the time capsule and made public at a specific date and time. In this way, a digital time capsule can be created based on the activities of the deceased and made public at a specific date and time.

[0074] Digital artworks can be generated based on memories of the deceased and displayed on the cloud-based gravestone. For example, digital artworks can be generated based on photographs and videos of the deceased and displayed on the cloud-based gravestone. For example, photographs of the deceased from their lifetime can be generated as artwork and added to a database. Digital artworks can also be generated based on letters and voice messages from the deceased and displayed on the cloud-based gravestone. For example, artwork can be generated based on letters and voice messages from the deceased to their family and added to a database. Digital artworks can also be generated based on episodes from the deceased's lifetime and displayed on the cloud-based gravestone. For example, artwork can be generated based on special moments the deceased spent with friends and added to a database. In this way, digital artworks based on the memories of the deceased can be generated and displayed.

[0075] The emotion estimation function can host a digital memorial event based on the emotions of the deceased during their lifetime, allowing family and friends to participate. The emotion estimation function can, for example, build a system for hosting a digital memorial event based on the emotions of the deceased during their lifetime. For example, an event can be held based on an event that was particularly moving to the deceased. The emotion estimation function can also provide an interactive storybook that reflects the emotions of the deceased during their lifetime. For example, a storybook can be generated based on important events in the deceased's life, which can be interactively viewed by family and friends. The emotion estimation function can also provide memorial content based on the emotions of the deceased. For example, a memorial video can be generated based on an event that was particularly moving to the deceased, which can be viewed by family and friends. This allows a digital memorial event based on the emotions of the deceased to be held, allowing family and friends to participate.

[0076] Dedicated apps can be developed to facilitate remote access to cloud-based graves. For example, a dedicated app can be developed to access cloud-based graves, making it easy to visit the grave even from a remote location. For example, an app can be provided for smartphones and tablets. Cloud-based graves also allow users to view memories of the deceased through a dedicated app. For example, photos and videos of the deceased can be viewed through the dedicated app. Cloud-based graves also allow users to share stories from the deceased's life through the dedicated app. For example, special moments the deceased spent with their family can be shared through the dedicated app. This allows for the development of dedicated apps to facilitate remote access.

[0077] Virtual tours are provided based on the activities and memories of the deceased during their lifetime, allowing family and friends to remember the deceased even from a remote location. For example, a system is constructed to provide virtual tours based on the activities and memories of the deceased during their lifetime. For example, a virtual tour recreating places the deceased visited or had fond memories of is provided. Virtual tours can also be provided based on episodes from the deceased's lifetime. For example, a virtual tour recreating special moments the deceased spent with their family is provided. Virtual tours can also provide content based on the hobbies and interests of the deceased. For example, a virtual tour based on the music or movies the deceased liked is provided. In this way, a virtual tour based on the activities and memories of the deceased can be provided, allowing the deceased to be remembered even from a remote location.

[0078] Dedicated devices can be developed to facilitate remote access to cloud-based graves. For example, dedicated devices can be developed to access cloud-based graves, making it easy to visit the grave even from a remote location. For example, dedicated tablets or smart displays can be provided. Cloud-based graves also allow for viewing of memories of the deceased through dedicated devices. For example, photos and videos of the deceased can be viewed on dedicated devices. Cloud-based graves also allow sharing of stories from the deceased's life through dedicated devices. For example, special moments the deceased spent with their family can be shared on dedicated devices. This allows for the development of dedicated devices that facilitate remote access.

[0079] Virtual reality can be provided based on the activities and memories of the deceased, allowing family and friends to remember the deceased even from a remote location. For example, virtual reality can be used to build a system that provides VR tours based on the activities and memories of the deceased. For example, a VR tour can be provided that recreates places the deceased visited or places that held memories for the deceased. Virtual reality can also be provided based on episodes from the deceased's life. For example, a VR tour can be provided that recreates special moments the deceased spent with their family. Virtual reality can also provide content based on the hobbies and interests of the deceased. For example, a VR tour can be provided based on the music or movies the deceased liked. This allows VR tours based on the activities and memories of the deceased to be provided, allowing family and friends to remember the deceased even from a remote location.

[0080] The emotion estimation function can provide a virtual assistant that reflects the emotions of the deceased when visiting a grave from a remote location. The emotion estimation function can be used, for example, to build a system that provides a virtual assistant that reflects the emotions of the deceased when visiting a grave from a remote location. For example, a virtual assistant that reproduces the voice and facial expressions of the deceased can be provided. The emotion estimation function can also provide an interactive storybook that reflects the emotions of the deceased when they were alive. For example, a storybook based on important events in the deceased's life can be generated, which can be interactively viewed by family and friends. The emotion estimation function can also provide memorial content based on the emotions of the deceased. For example, a memorial video based on events that particularly moved the deceased can be generated, which can be viewed by family and friends. This makes it possible to provide a virtual assistant that reflects the emotions of the deceased when visiting a grave from a remote location.

[0081] The database is automatically backed up, ensuring data safety. For example, a system can be built to periodically and automatically back up the cloud-based gravestone database. For example, a daily or weekly backup schedule can be set. The database can also encrypt and securely store the backup data. For example, the backup data can be encrypted and stored in cloud storage. The database can also set up recovery procedures for the backup data to ensure data safety. For example, procedures can be set up to quickly restore the backup data. This allows the cloud-based gravestone database to be automatically backed up, ensuring data safety.

[0082] The database is updated regularly, and new memories and memorial content can be added. For example, a system can be built to regularly update the cloud-based grave database and add new memories and memorial content. For example, a monthly update schedule can be set. The database can also accept posts from users and add new memories and memorial content. For example, family and friends can post memories of the deceased and add them to the database. The database can also automatically generate and add new memorial content using generative AI. For example, a memorial video based on episodes from the deceased's life can be generated and added to the database. This allows the cloud-based grave database to be regularly updated and new memories and memorial content can be added.

[0083] The emotion estimation function can automatically generate memorial content that reflects the emotions of the deceased during their lifetime and add it to a gravestone on the cloud. The emotion estimation function can, for example, build a system that automatically generates memorial content that reflects the emotions of the deceased during their lifetime. For example, memorial content can be generated based on events that particularly moved the deceased. The emotion estimation function can also analyze the emotions in the deceased's voice messages and automatically generate memorial content that reflects those emotions. For example, memorial content can be generated based on a voice message of gratitude written by the deceased to their family. The emotion estimation function can also analyze the emotions in letters written by the deceased and automatically generate memorial content that reflects those emotions. For example, memorial content can be generated based on a letter written by the deceased to a friend. In this way, memorial content that reflects the emotions of the deceased can be automatically generated and added to a gravestone on the cloud.

[0084] The database can be linked with other cloud services to share and integrate data. For example, the database can link the cloud-based gravestone database with other cloud services such as Google Drive or Dropbox to share and integrate data. For example, a data synchronization function can be added. The database can also share data with other cloud services through API integration. For example, an API can be used to send and share data with other cloud services. The database can also integrate data with other cloud services through data migration. For example, data can be migrated to and integrated with other cloud services. This allows the cloud-based gravestone database to be linked with other cloud services to share and integrate data.

[0085] A database-based memorial app can make it easy for family and friends to access the database. For example, a memorial app based on a cloud-based database of graves can be developed to make it easy for family and friends to access the database. For example, an app for smartphones or tablets can be provided. The memorial app can also provide a user-friendly interface to make it easy for family and friends to operate. For example, an interface that allows intuitive operation can be provided. The memorial app can also provide a single sign-on function to make it easy for family and friends to log in. For example, a single sign-on function can be provided that allows access to multiple services with a single login. This allows a memorial app based on a cloud-based database of graves to be developed to make it easy for family and friends to access the database.

[0086] The emotion estimation function can automatically generate memorial content that reflects the emotions of the deceased during their lifetime and add it to a gravestone on the cloud. The emotion estimation function can, for example, build a system that automatically generates memorial content that reflects the emotions of the deceased during their lifetime. For example, memorial content can be generated based on events that particularly moved the deceased. The emotion estimation function can also analyze the emotions in the deceased's voice messages and automatically generate memorial content that reflects those emotions. For example, memorial content can be generated based on a voice message of gratitude written by the deceased to their family. The emotion estimation function can also analyze the emotions in letters written by the deceased and automatically generate memorial content that reflects those emotions. For example, memorial content can be generated based on a letter written by the deceased to a friend. In this way, memorial content that reflects the emotions of the deceased can be automatically generated and added to a gravestone on the cloud.

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

[0088] The cloud grave system can also create a digital time capsule based on the activities of the deceased during their lifetime and automatically publish it at a specific date and time. For example, photos and videos of the deceased can be saved in a time capsule to coincide with the deceased's birthday or death anniversary, and published at a specific date and time. It can also create a digital time capsule based on the deceased's letters and voice messages and publish it at a specific date and time. This allows a digital time capsule to be created based on the activities of the deceased during their lifetime and published at a specific date and time.

[0089] The cloud grave system can also be equipped with a digital archive that automatically collects and preserves the activities and achievements of the deceased. For example, the work history and achievements of the deceased can be automatically collected and preserved as a digital archive. Resumes and performance reports can be analyzed and added to the database. The deceased's papers and patents can also be automatically collected and preserved. This allows the deceased's activities and achievements to be preserved as a digital archive.

[0090] The cloud grave system can also collect messages and memories from friends and acquaintances of the deceased and add them to the cloud grave. For example, it can analyze emails and social media messages from the deceased's friends and acquaintances and add them to a database as memories. It can also collect letters and voice messages from the deceased's friends and acquaintances and add them to the cloud grave. This allows messages and memories from the deceased's friends and acquaintances to be collected and added to the cloud grave.

[0091] The cloud grave system can also generate digital artwork based on the deceased's activities and memories and display it on the cloud grave. For example, digital artwork can be generated based on the deceased's photos and videos and added to the database. Artwork generated based on the deceased's letters and voice messages can also be displayed on the cloud grave. This allows digital artwork to be generated and displayed based on the deceased's memories.

[0092] The cloud grave system can also provide virtual reality tours based on the activities and memories of the deceased. For example, it can provide a VR tour that recreates the places the deceased visited or had fond memories of. It can also provide VR tours based on episodes from the deceased's life. This allows family and friends to remember the deceased even from a distance by providing a VR tour based on the deceased's activities and memories.

[0093] The cloud grave system uses emotion estimation to hold digital memorial events that reflect the emotions of the deceased, allowing family and friends to participate. For example, an event could be held based on an event that particularly touched the deceased. It can also provide an interactive storybook that reflects the emotions of the deceased. This allows digital memorial events based on the emotions of the deceased to be held, allowing family and friends to participate.

[0094] The cloud grave system uses emotion estimation functionality to generate a music album that reflects the emotions of the deceased during their lifetime and store it on the cloud grave. For example, it can analyze the emotions in the deceased's social media posts and messages and generate a music album based on those emotions. It can also analyze the emotions in the deceased's voice messages and generate a music album based on those emotions. This allows for the generation and storage of a music album based on the emotions of the deceased.

[0095] The cloud grave system uses emotion estimation functionality to generate poems and texts that reflect the emotions of the deceased during their lifetime and store them on the cloud grave. For example, it can analyze the emotions expressed in the deceased's social media posts and messages and generate poems and texts that reflect those emotions. It can also analyze the emotions expressed in the deceased's letters and generate poems and texts that reflect those emotions. This allows poems and texts that reflect the emotions of the deceased to be generated and stored.

[0096] The cloud tomb system uses emotion estimation to provide a virtual tour that reflects the emotions of the deceased, allowing family and friends to relive their memories. For example, it can provide a virtual tour that recreates places and events that particularly moved the deceased. It can also provide an interactive storybook that reflects the emotions of the deceased. This allows family and friends to relive their memories by providing a virtual tour based on the emotions of the deceased.

[0097] The cloud grave system uses emotion estimation functionality to automatically generate memorial content that reflects the emotions of the deceased during their lifetime and can add it to the cloud-based grave. For example, memorial content can be generated based on an event that particularly moved the deceased. It can also analyze the emotions in the deceased's voice message and automatically generate memorial content that reflects those emotions. This allows memorial content that reflects the emotions of the deceased to be automatically generated and added to the cloud-based grave.

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

[0099] Step 1: The cloud-based grave construction unit transfers the deceased person's grave to the cloud. For example, it converts the physical grave information into digital data and stores it on the cloud. The cloud-based grave construction unit can also upload photos and videos of the deceased to the cloud. Step 2: The database unit stores evidence of the deceased's life, such as photos, videos, voice messages, and letters. The database unit can also store the deceased's social media posts and activity history. Step 3: The generative AI creates memories of the deceased. For example, the generative AI analyzes photos and videos of the deceased and creates a slideshow of memories. The generative AI can also create a short film based on episodes from the deceased's life. The generative AI can also generate poems and writings that reflect the emotions of the deceased.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0134] 7, 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A cloud-based grave construction department that transfers the deceased's grave to the cloud, A database section that stores the living proof of the deceased, A generating AI unit that creates memories of the deceased, A system characterized by:

2. The database unit Automatically collect the deceased's activity history and social media posts to build a more detailed database 2. The system of claim 1.

3. To the grave on the cloud, Create a digital avatar of the deceased and add the ability for family and friends to interact with it 2. The system of claim 1.

4. The generated AI is Analyzes conversations and messages from the deceased and generates a memorial message that reproduces the words of the deceased.

2. The system of claim 1.

5. The digital archive is Automatically collect and store the activities and achievements of the deceased 2. The system of claim 1.

6. The emotion estimation function is Automatically generate messages that reflect the feelings of the deceased when visiting a grave from a remote location 2. The system of claim 1.

7. The emotion estimation function is Automatically generate memorial content that reflects the emotions of the deceased and add it to the grave in the cloud 2. The system of claim 1.

8. The database unit Using emotion estimation functionality to analyze the emotions of the deceased during their lifetime and generate memorial content based on those emotions 2. The system of claim 1.

Images