system

Abstract

The system according to this embodiment aims to organize the data of a deceased person in a digital space and provide virtual dialogue. [Solution] The system according to the embodiment comprises an upload unit, an analysis unit, an organization unit, a dialogue unit, and an access unit. The upload unit uploads the data of the deceased. The analysis unit analyzes the data uploaded by the upload unit. The organization unit organizes the data analyzed by the analysis unit in chronological order. The dialogue unit provides a virtual dialogue based on the data organized by the organization unit. The access unit accesses the virtual dialogue provided by the dialogue unit through a dedicated application.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, memories and records of the deceased have not been sufficiently organized in the digital space to provide virtual conversations, leaving room for improvement.

[0005] The system according to the embodiment aims to organize the data of the deceased in the digital space and provide virtual conversations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an upload unit, an analysis unit, an organization unit, a dialogue unit, and an access unit. The upload unit uploads data of the deceased. The analysis unit analyzes the data uploaded by the upload unit. The organization unit organizes the data analyzed by the analysis unit in chronological order. The dialogue unit provides a virtual dialogue based on the data organized by the organization unit. The access unit accesses the virtual dialogue provided by the dialogue unit through a dedicated application. [Effects of the Invention]

[0007] The system according to this embodiment can organize the data of a deceased person in a digital space and provide virtual dialogue. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Digital Memorial Garden, according to an embodiment of the present invention, is a system that permanently stores memories and testaments to the life of a deceased person in a digital space, providing a virtual grave accessible anytime, anywhere. The Digital Memorial Garden allows uploading of various data formats, such as photos, videos, audio recordings, and diaries of the deceased. AI analyzes these, automatically organizes them chronologically, and creates a moving memorial story. Furthermore, the AI ​​learns the deceased's preferences and personality, and provides a virtual dialogue function that reproduces their speech patterns and mannerisms. Family and friends can reminisce about their memories of the deceased and send virtual floral tributes and memorial messages through a dedicated app. This service not only solves the problems of traditional graves, such as lack of successors, distance, and maintenance costs, but also provides a new way to pass on the memories of the deceased to future generations. For example, by storing the deceased's information in the cloud, the Digital Memorial Garden can be easily searched even hundreds of years later. The Digital Memorial Garden provides a place to create a memorial culture suited to modern lifestyles and to eternally weave bonds with loved ones. For example, by storing memories of the deceased in a digital space and making them accessible to family and friends at any time, it allows them to feel a connection with the deceased. This allows Digital Memorial Garden to preserve memories of the deceased in a digital space and provide a virtual grave that can be accessed anytime, anywhere.

[0029] The digital memorial garden according to this embodiment comprises an upload unit, an analysis unit, an organization unit, an interaction unit, and an access unit. The upload unit uploads data of the deceased. Data of the deceased includes, but is not limited to, photographs, videos, audio, and diaries. For example, the upload unit uploads photographs of the deceased in JPEG format. The upload unit can also upload videos of the deceased in MP4 format. The upload unit can also upload audio of the deceased in MP3 format. For example, the upload unit uploads the deceased's diary in text file format. The analysis unit analyzes the data uploaded by the upload unit. For example, the analysis unit analyzes photographs of the deceased to learn their preferences and personality. For example, the analysis unit can analyze videos of the deceased to learn their preferences and personality. For example, the analysis unit can analyze audio of the deceased to learn their preferences and personality. For example, the analysis unit analyzes diaries of the deceased to learn their preferences and personality. The organization unit organizes the data analyzed by the analysis unit in chronological order. The data compilation unit, for example, arranges events from the deceased's life in chronological order to create a moving memorial story. The data compilation unit can also, for example, organize the deceased's photographs in chronological order to create a moving memorial story. Furthermore, the data compilation unit can also, for example, organize the deceased's videos in chronological order to create a moving memorial story. For example, the data compilation unit can organize the deceased's diaries in chronological order to create a moving memorial story. The dialogue unit provides virtual dialogues based on the data compiled by the data compilation unit. For example, the dialogue unit provides virtual dialogues that recreate the deceased's speech patterns and tone. The dialogue unit can also, for example, learn the deceased's preferences and personality to provide virtual dialogues. Furthermore, the dialogue unit can provide virtual dialogues that recreate the deceased's speech patterns and tone during their lifetime. For example, the dialogue unit can recreate the deceased's speech patterns and tone during their lifetime, allowing family and friends to reminisce about their memories of the deceased through virtual dialogues. The access unit accesses the virtual dialogues provided by the dialogue unit through a dedicated application. The access unit provides features such as sending virtual floral tributes and memorial messages through a dedicated app. The access unit also allows users to reminisce about memories with the deceased through a dedicated app.Furthermore, the access unit can also send virtual floral tributes and memorial messages through a dedicated app. For example, the access unit can reminisce about memories with the deceased and send virtual floral tributes and memorial messages through the dedicated app. This enables the digital memorial garden according to the embodiment to efficiently upload, analyze, organize, virtually interact with, and access the data of the deceased.

[0030] The upload function allows users to upload data of the deceased. This data may include, but is not limited to, photos, videos, audio recordings, and diaries. For example, the upload function can upload photos of the deceased in JPEG format. It can also upload videos of the deceased in MP4 format. It can also upload audio recordings of the deceased in MP3 format. For example, the upload function can upload diaries of the deceased as text files. The upload function provides an intuitive interface to allow users to easily upload data. For example, users can easily upload files using a drag-and-drop function. The upload function also supports multiple file formats, allowing users to upload various types of data at once. Furthermore, the upload function displays the progress during data upload, informing the user of the current progress in real time. This allows the user to confirm whether the upload is proceeding successfully. In addition, the upload function calculates checksums of uploaded files to ensure data integrity and verify that there is no data corruption or loss. This ensures that the uploaded data is accurate and complete. Furthermore, the upload section includes a function to encrypt and securely store data uploaded by users. This protects user privacy and prevents unauthorized access to data.

[0031] The analysis unit analyzes data uploaded by the upload unit. For example, the analysis unit can analyze the deceased's photographs to learn their preferences and personality. The analysis unit can also analyze the deceased's videos to learn their preferences and personality. Furthermore, the analysis unit can analyze the deceased's voice to learn their preferences and personality. For example, the analysis unit can analyze the deceased's diary to learn their preferences and personality. The analysis unit utilizes AI technology to analyze the uploaded data in detail. For example, it uses image recognition technology to identify specific places, people, and objects from the deceased's photographs to determine their preferences and hobbies. It also uses natural language processing technology to analyze the deceased's diary and audio data to understand their personality and emotions. In addition, the analysis unit can analyze the deceased's video data to infer their emotions and personality from their actions and facial expressions. As a result, the analysis unit can extract multifaceted information from the deceased's data and reconstruct a detailed image of the deceased. Based on these analysis results, the analysis department can learn the deceased's preferences and personality and utilize this information during virtual conversations. For example, if the deceased had a favorite place, topics related to that place can be provided in the virtual conversation. Similarly, if the deceased had a particular hobby, topics related to that hobby can be provided in the virtual conversation. This allows the analysis department to analyze the deceased's data in detail and provide a foundation for reconstructing their personality.

[0032] The organization department organizes the data analyzed by the analysis department in chronological order. For example, the organization department can arrange events from the deceased's life in chronological order to create a moving memorial story. The organization department can also organize the deceased's photographs in chronological order to create a moving memorial story. Furthermore, the organization department can organize the deceased's videos in chronological order to create a moving memorial story. For example, the organization department can organize the deceased's diaries in chronological order to create a moving memorial story. The organization department utilizes timestamps and metadata to organize the deceased's data in chronological order. For example, since photographs and videos contain the date and time they were taken, the data can be organized chronologically based on this information. Similarly, since diaries and audio data contain the date and time they were created, the data can be organized chronologically based on this information. In addition, the organization department can also organize the deceased's data by theme. For example, data related to the deceased's travels can be organized as a theme to allow people to reminisce about their travel memories. Similarly, data related to the deceased's family and friends can be organized as a theme to allow people to reminisce about their memories with family and friends. This allows the data management unit to organize the deceased's data from multiple perspectives and create a moving memorial story. Furthermore, the data management unit also has an indexing function that allows users to easily search for the deceased's data. This allows users to quickly find data related to specific events or themes.

[0033] The dialogue unit provides virtual dialogues based on data organized by the data processing unit. For example, the dialogue unit provides virtual dialogues that reproduce the deceased's speech patterns and tone. The dialogue unit can also learn the deceased's preferences and personality and provide virtual dialogues based on that information. Furthermore, the dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone during their lifetime. For example, the dialogue unit can reproduce the deceased's speech patterns and tone during their lifetime, allowing family and friends to reminisce about their memories of the deceased through virtual dialogues. The dialogue unit utilizes AI technology to reproduce the deceased's speech patterns and tone. For example, natural language processing technology can be used to analyze the deceased's speech patterns and tone and reproduce them during virtual dialogues. Additionally, speech synthesis technology can be used to reproduce the deceased's voice and use it during virtual dialogues. This allows the dialogue unit to faithfully reproduce the deceased's speech patterns and tone, enabling family and friends to reminisce about their memories of the deceased. Moreover, the dialogue unit can learn the deceased's preferences and personality and customize the content of the virtual dialogues. For example, if the deceased had a particular interest in a specific topic, a virtual dialogue related to that topic can be provided. Similarly, if the deceased had a particular hobby, a virtual dialogue related to that hobby can be provided. This allows the dialogue system to provide virtual conversations that reflect the personality of the deceased, enabling family and friends to reflect more deeply on their memories of the deceased.

[0034] The Access Unit provides access to virtual dialogues offered by the Dialogue Unit through a dedicated app. The Access Unit offers features such as the ability to send virtual floral tributes and memorial messages through the dedicated app. The Access Unit also allows users to reminisce about their memories with the deceased through the dedicated app. Furthermore, the Access Unit allows users to send virtual floral tributes and memorial messages through the dedicated app. For example, the Access Unit allows users to reminisce about their memories with the deceased and send virtual floral tributes and memorial messages through the dedicated app. The Access Unit provides an intuitive interface to allow users to easily access virtual dialogues. For example, the home screen of the dedicated app displays the deceased's photo and name, making it easy for users to start a virtual dialogue. The Access Unit also offers various options when users send virtual floral tributes and memorial messages. For example, users can choose and offer virtual flowers, or enter and send messages to the deceased. In addition, the Access Unit provides features for users to reminisce about their memories with the deceased. For example, users can view photos and videos of the deceased or read their diaries. The Access Unit also provides features for users to share their memories with the deceased. For example, family and friends can view the deceased's data together and share memories. This allows the access system to enable users to reminisce about their memories of the deceased, send virtual flowers and memorial messages, and remember the deceased together with family and friends.

[0035] The upload unit can upload data such as photos, videos, audio, and diaries of the deceased. For example, the upload unit can upload photos of the deceased in JPEG format. For example, the upload unit can also upload videos of the deceased in MP4 format. Furthermore, the upload unit can also upload audio of the deceased in MP3 format. For example, the upload unit can upload the deceased's diary in text file format. This allows for the uploading of a variety of data about the deceased. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input photos of the deceased into a generating AI and have the generating AI perform photo analysis.

[0036] The analysis unit can analyze uploaded data and learn the deceased's preferences and personality. For example, the analysis unit can analyze the deceased's photographs to learn their preferences and personality. The analysis unit can also analyze the deceased's videos to learn their preferences and personality. Furthermore, the analysis unit can analyze the deceased's voice to learn their preferences and personality. For example, the analysis unit can analyze the deceased's diary to learn their preferences and personality. By learning the deceased's preferences and personality, it is possible to provide more personalized services. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the deceased's photographs into a generating AI and have the generating AI perform the photo analysis.

[0037] The data analysis unit can organize the analyzed data chronologically and create a moving memorial story. For example, the data analysis unit can arrange the events of the deceased's life chronologically and create a moving memorial story. For example, the data analysis unit can organize the deceased's photographs chronologically and create a moving memorial story. The data analysis unit can also organize the deceased's videos chronologically and create a moving memorial story. For example, the data analysis unit can organize the deceased's diaries chronologically and create a moving memorial story. By creating a moving memorial story, one can feel the memory of the deceased more deeply. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or not. For example, the data analysis unit can input the deceased's photographs into a generating AI and have the generating AI perform the photo analysis.

[0038] The dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone of voice. For example, the dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone of voice. The dialogue unit can also learn the deceased's preferences and personality and provide virtual dialogues based on that. Furthermore, the dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone of voice during their lifetime. For example, the dialogue unit can reproduce the deceased's speech patterns and tone of voice during their lifetime, allowing family and friends to reminisce about their memories of the deceased through virtual dialogues. This makes more realistic virtual dialogues possible by reproducing the deceased's speech patterns and tone of voice. Some or all of the above-described processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the deceased's speech patterns and tone of voice into a generating AI and have the generating AI generate virtual dialogues.

[0039] The access unit can provide functions for sending virtual floral tributes and memorial messages through a dedicated app. The access unit can, for example, provide functions for sending virtual floral tributes and memorial messages through a dedicated app. The access unit can, for example, allow users to reminisce about their memories with the deceased through a dedicated app. The access unit can also send virtual floral tributes and memorial messages through a dedicated app. For example, the access unit can reminisce about their memories with the deceased through a dedicated app and send virtual floral tributes and memorial messages. This allows users to send virtual floral tributes and memorial messages through a dedicated app. Some or all of the above-described processes in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input a dedicated app into a generating AI and have the generating AI execute the sending of virtual floral tributes and memorial messages.

[0040] The upload unit can select the optimal upload method according to the type and format of the data. For example, in the case of photo data, the upload unit can efficiently upload it using compression technology. For example, in the case of video data, the upload unit can also upload it using streaming technology. Furthermore, in the case of audio data, the upload unit can select the optimal method for uploading while maintaining sound quality. In this way, by selecting the optimal upload method according to the type and format of the data, data can be uploaded efficiently. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the type and format of the data into a generating AI and have the generating AI select the optimal upload method.

[0041] The upload unit can determine upload priorities based on the importance of the data. For example, the upload unit will prioritize uploading important photos and videos. The upload unit can also postpone uploading text data such as diaries and notes. Furthermore, the upload unit can adjust the upload order based on importance levels specified by the user. This allows important data to be uploaded preferentially by determining upload priorities based on data importance. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the data importance levels into a generating AI and have the generating AI determine the upload priorities.

[0042] The upload unit can prioritize uploading highly relevant data by considering the user's geographical location. For example, if the user is in the hometown of a deceased person, the upload unit will prioritize uploading data related to that location. If the user is traveling, the upload unit can also prioritize uploading data related to their travel destination. Furthermore, if the user is participating in a specific event, the upload unit can prioritize uploading data related to that event. In this way, by considering the user's geographical location, highly relevant data can be prioritized for uploading. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0043] The upload unit can analyze a user's social media activity and upload relevant data. For example, the upload unit can automatically upload photos and videos that a user has shared on social media. The upload unit can also prioritize uploading data related to events that a user has mentioned on social media. Furthermore, the upload unit can analyze the content of a user's social media posts and upload relevant data. This allows for the efficient uploading of relevant data by analyzing the user's social media activity. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the user's social media activity into a generating AI and have the generating AI select relevant data.

[0044] The analysis unit can apply different analysis algorithms depending on the type and format of the data. For example, the analysis unit can apply an image recognition algorithm to photographic data. For example, the analysis unit can also apply a video analysis algorithm to video data. Furthermore, the analysis unit can apply a speech recognition algorithm to audio data. This allows for efficient data analysis by applying different analysis algorithms depending on the type and format of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type and format of the data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can perform analysis by considering the relationship between photos and videos. For example, the analysis unit can also perform analysis by associating audio data with diary entries. Furthermore, the analysis unit can integrate multiple data formats to perform comprehensive analysis. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize analyzing recently uploaded data. It may also postpone the analysis of older data. Furthermore, the analysis unit can adjust the order of analysis based on the submission date specified by the user. This allows for efficient data analysis by prioritizing analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on user-specified relevance. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The sorting unit can adjust the level of detail in sorting based on the importance of the data. For example, the sorting unit sorts important data in detail. For example, the sorting unit can sort less important data simply. The sorting unit can also adjust the level of detail in sorting based on the importance specified by the user. This allows for efficient data sorting by adjusting the level of detail in sorting based on the importance of the data. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in sorting.

[0049] The sorting unit can apply different sorting algorithms depending on the data category. For example, the sorting unit can apply an image sorting algorithm to photographic data. For example, it can also apply a video sorting algorithm to video data. Furthermore, it can apply an audio sorting algorithm to audio data. This allows for efficient data sorting by applying different sorting algorithms depending on the data category. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the data category into a generating AI and have the generating AI select the optimal sorting algorithm.

[0050] The sorting unit can determine sorting priorities based on the data submission date. For example, the sorting unit might prioritize sorting recently uploaded data. It can also sort older data later. Furthermore, the sorting unit can adjust the sorting order based on the submission date specified by the user. This allows for efficient data sorting by determining sorting priorities based on the data submission date. Some or all of the above processes in the sorting unit may be performed using AI, for example, or not. For example, the sorting unit can input the data submission date into a generating AI and have the generating AI determine the sorting priority.

[0051] The sorting unit can adjust the sorting order based on the relevance of the data. For example, the sorting unit can prioritize sorting highly relevant data. For example, the sorting unit can also sort less relevant data later. Furthermore, the sorting unit can adjust the sorting order based on user-specified relevance. This allows for efficient data sorting by adjusting the sorting order based on data relevance. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the data relevance into a generating AI and have the generating AI perform the sorting order adjustment.

[0052] The dialogue unit can optimize data to reproduce the deceased's speech patterns and tone during virtual dialogue. For example, the dialogue unit can analyze the deceased's past audio data to reproduce their speech patterns and tone. The dialogue unit can also generate natural dialogue based on the deceased's written data. Furthermore, the dialogue unit can reproduce facial expressions and gestures by referencing the deceased's video data. By optimizing the data to reproduce the deceased's speech patterns and tone, a more realistic virtual dialogue becomes possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the deceased's speech patterns and tone into a generating AI and have the generating AI perform the optimization.

[0053] The dialogue unit can customize the content of a virtual conversation based on the deceased's preferences and personality. For example, the dialogue unit can focus the conversation on topics the deceased enjoyed. The dialogue unit can also adopt a conversation style that suits the deceased's personality. Furthermore, the dialogue unit can select conversation content based on the deceased's hobbies and interests. This allows for more personalized conversations by customizing the content based on the deceased's preferences and personality. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the deceased's preferences and personality into a generating AI and have the generating AI perform the customization of the conversation content.

[0054] The dialogue unit can determine the content of a virtual conversation by referring to the deceased's activity history during their lifetime. For example, the dialogue unit can determine the content based on events and activities the deceased participated in during their lifetime. The dialogue unit can also include topics related to the deceased's occupation or hobbies in the conversation. Furthermore, the dialogue unit can reflect memories of the deceased with family and friends in the conversation. This allows for more realistic conversation content by referring to the deceased's activity history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the deceased's activity history into a generating AI and have the generating AI determine the content of the conversation.

[0055] The dialogue unit can improve the accuracy of the dialogue during virtual conversations by referring to the deceased's relevant literature. For example, the dialogue unit can refine the dialogue content by referring to literature and diaries written by the deceased. The dialogue unit can also supplement the dialogue content based on literature and materials cited by the deceased. Furthermore, the dialogue unit can enrich the dialogue content by referring to literature related to the deceased's research and achievements. As a result, the accuracy of the dialogue is improved by referring to the deceased's relevant literature. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the deceased's relevant literature into a generating AI and have the generating AI refine the dialogue content.

[0056] The access unit can select the optimal access method by referring to the user's past access history when accessing the system. For example, the access unit may prioritize providing access methods that the user has used in the past. For example, the access unit may also suggest the most frequently used method based on the user's past access history. Furthermore, the access unit may analyze the user's past access patterns and select the optimal method. This allows the system to provide the optimal access method by referring to the user's past access history. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit may input the user's past access history into a generating AI and have the generating AI select the optimal access method.

[0057] The access unit can provide the optimal access method by considering the user's device information at the time of access. For example, if the user is using a smartphone, the access unit can provide an access method optimized for mobile devices. If the user is using a tablet, the access unit can also provide an access method optimized for large screens. Furthermore, if the user is using a desktop computer, the access unit can provide an access method that includes detailed information. In this way, the optimal access method can be provided by considering the user's device information. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's device information into a generating AI and have the generating AI select the optimal access method.

[0058] The access unit can provide the optimal access method by considering the user's geographical location information at the time of access. For example, if the user is in the hometown of a deceased person, the access unit may prioritize access to data related to that location. For example, if the user is traveling, the access unit may also prioritize access to data related to the travel destination. Furthermore, if the user is participating in a specific event, the access unit may also prioritize access to data related to that event. In this way, the optimal access method can be provided by considering the user's geographical location information. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal access method.

[0059] The access unit can analyze the user's social media activity and suggest access methods when accessing content. For example, the access unit may prioritize access to photos and videos shared by the user on social media. It may also prioritize access to data related to events mentioned by the user on social media. Furthermore, the access unit can analyze the content of the user's social media posts and suggest access to relevant data. In this way, by analyzing the user's social media activity, the optimal access method can be suggested. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's social media activity into a generating AI and have the generating AI suggest the optimal access method.

[0060] The access unit can refer to the user's calendar information and make suggestions based on their schedule when accessing data. For example, the access unit can refer to appointments registered in the user's calendar and suggest access to related data. The access unit can also prioritize access to data related to a specific event based on the user's calendar information. Furthermore, the access unit can suggest the most suitable access method based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the user's calendar information. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's calendar information into a generating AI and have the generating AI execute a suggestion for the most suitable access method.

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

[0062] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0063] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0064] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0065] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0066] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0067] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0068] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0069] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0070] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0071] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The upload section allows you to upload the deceased person's data. This data includes photos, videos, audio, and diaries. For example, the upload section allows you to upload photos in JPEG format, videos in MP4 format, audio in MP3 format, and diaries as text files. Step 2: The analysis unit analyzes the data uploaded by the upload unit. For example, it analyzes the deceased's photos, videos, audio, and diary to learn about their preferences and personality. Step 3: The organization department organizes the data analyzed by the analysis department in chronological order. For example, they arrange events, photos, videos, and diaries from the deceased's life in chronological order to create a moving memorial story. Step 4: The dialogue unit provides a virtual dialogue based on the data organized by the data processing unit. For example, it can recreate the deceased's speech patterns and tone, allowing family and friends to reminisce about their memories of the deceased through the virtual dialogue. Step 5: The access unit accesses the virtual dialogue provided by the dialogue unit through a dedicated app. For example, it provides functions to send virtual floral tributes and memorial messages through the dedicated app, allowing users to reminisce about memories with the deceased.

[0074] (Example of form 2) The Digital Memorial Garden, according to an embodiment of the present invention, is a system that permanently stores memories and testaments to the life of a deceased person in a digital space, providing a virtual grave accessible anytime, anywhere. The Digital Memorial Garden allows uploading of various data formats, such as photos, videos, audio recordings, and diaries of the deceased. AI analyzes these, automatically organizes them chronologically, and creates a moving memorial story. Furthermore, the AI ​​learns the deceased's preferences and personality, and provides a virtual dialogue function that reproduces their speech patterns and mannerisms. Family and friends can reminisce about their memories of the deceased and send virtual floral tributes and memorial messages through a dedicated app. This service not only solves the problems of traditional graves, such as lack of successors, distance, and maintenance costs, but also provides a new way to pass on the memories of the deceased to future generations. For example, by storing the deceased's information in the cloud, the Digital Memorial Garden can be easily searched even hundreds of years later. The Digital Memorial Garden provides a place to create a memorial culture suited to modern lifestyles and to eternally weave bonds with loved ones. For example, by storing memories of the deceased in a digital space and making them accessible to family and friends at any time, it allows them to feel a connection with the deceased. This allows Digital Memorial Garden to preserve memories of the deceased in a digital space and provide a virtual grave that can be accessed anytime, anywhere.

[0075] The digital memorial garden according to this embodiment comprises an upload unit, an analysis unit, an organization unit, an interaction unit, and an access unit. The upload unit uploads data of the deceased. Data of the deceased includes, but is not limited to, photographs, videos, audio, and diaries. For example, the upload unit uploads photographs of the deceased in JPEG format. The upload unit can also upload videos of the deceased in MP4 format. The upload unit can also upload audio of the deceased in MP3 format. For example, the upload unit uploads the deceased's diary in text file format. The analysis unit analyzes the data uploaded by the upload unit. For example, the analysis unit analyzes photographs of the deceased to learn their preferences and personality. For example, the analysis unit can analyze videos of the deceased to learn their preferences and personality. For example, the analysis unit can analyze audio of the deceased to learn their preferences and personality. For example, the analysis unit analyzes diaries of the deceased to learn their preferences and personality. The organization unit organizes the data analyzed by the analysis unit in chronological order. The data compilation unit, for example, arranges events from the deceased's life in chronological order to create a moving memorial story. The data compilation unit can also, for example, organize the deceased's photographs in chronological order to create a moving memorial story. Furthermore, the data compilation unit can also, for example, organize the deceased's videos in chronological order to create a moving memorial story. For example, the data compilation unit can organize the deceased's diaries in chronological order to create a moving memorial story. The dialogue unit provides virtual dialogues based on the data compiled by the data compilation unit. For example, the dialogue unit provides virtual dialogues that recreate the deceased's speech patterns and tone. The dialogue unit can also, for example, learn the deceased's preferences and personality to provide virtual dialogues. Furthermore, the dialogue unit can provide virtual dialogues that recreate the deceased's speech patterns and tone during their lifetime. For example, the dialogue unit can recreate the deceased's speech patterns and tone during their lifetime, allowing family and friends to reminisce about their memories of the deceased through virtual dialogues. The access unit accesses the virtual dialogues provided by the dialogue unit through a dedicated application. The access unit provides features such as sending virtual floral tributes and memorial messages through a dedicated app. The access unit also allows users to reminisce about memories with the deceased through a dedicated app.Furthermore, the access unit can also send virtual floral tributes and memorial messages through a dedicated app. For example, the access unit can reminisce about memories with the deceased and send virtual floral tributes and memorial messages through the dedicated app. This enables the digital memorial garden according to the embodiment to efficiently upload, analyze, organize, virtually interact with, and access the data of the deceased.

[0076] The upload function allows users to upload data of the deceased. This data may include, but is not limited to, photos, videos, audio recordings, and diaries. For example, the upload function can upload photos of the deceased in JPEG format. It can also upload videos of the deceased in MP4 format. It can also upload audio recordings of the deceased in MP3 format. For example, the upload function can upload diaries of the deceased as text files. The upload function provides an intuitive interface to allow users to easily upload data. For example, users can easily upload files using a drag-and-drop function. The upload function also supports multiple file formats, allowing users to upload various types of data at once. Furthermore, the upload function displays the progress during data upload, informing the user of the current progress in real time. This allows the user to confirm whether the upload is proceeding successfully. In addition, the upload function calculates checksums of uploaded files to ensure data integrity and verify that there is no data corruption or loss. This ensures that the uploaded data is accurate and complete. Furthermore, the upload section includes a function to encrypt and securely store data uploaded by users. This protects user privacy and prevents unauthorized access to data.

[0077] The analysis unit analyzes data uploaded by the upload unit. For example, the analysis unit can analyze the deceased's photographs to learn their preferences and personality. The analysis unit can also analyze the deceased's videos to learn their preferences and personality. Furthermore, the analysis unit can analyze the deceased's voice to learn their preferences and personality. For example, the analysis unit can analyze the deceased's diary to learn their preferences and personality. The analysis unit utilizes AI technology to analyze the uploaded data in detail. For example, it uses image recognition technology to identify specific places, people, and objects from the deceased's photographs to determine their preferences and hobbies. It also uses natural language processing technology to analyze the deceased's diary and audio data to understand their personality and emotions. In addition, the analysis unit can analyze the deceased's video data to infer their emotions and personality from their actions and facial expressions. As a result, the analysis unit can extract multifaceted information from the deceased's data and reconstruct a detailed image of the deceased. Based on these analysis results, the analysis department can learn the deceased's preferences and personality and utilize this information during virtual conversations. For example, if the deceased had a favorite place, topics related to that place can be provided in the virtual conversation. Similarly, if the deceased had a particular hobby, topics related to that hobby can be provided in the virtual conversation. This allows the analysis department to analyze the deceased's data in detail and provide a foundation for reconstructing their personality.

[0078] The organization department organizes the data analyzed by the analysis department in chronological order. For example, the organization department can arrange events from the deceased's life in chronological order to create a moving memorial story. The organization department can also organize the deceased's photographs in chronological order to create a moving memorial story. Furthermore, the organization department can organize the deceased's videos in chronological order to create a moving memorial story. For example, the organization department can organize the deceased's diaries in chronological order to create a moving memorial story. The organization department utilizes timestamps and metadata to organize the deceased's data in chronological order. For example, since photographs and videos contain the date and time they were taken, the data can be organized chronologically based on this information. Similarly, since diaries and audio data contain the date and time they were created, the data can be organized chronologically based on this information. In addition, the organization department can also organize the deceased's data by theme. For example, data related to the deceased's travels can be organized as a theme to allow people to reminisce about their travel memories. Similarly, data related to the deceased's family and friends can be organized as a theme to allow people to reminisce about their memories with family and friends. This allows the data management unit to organize the deceased's data from multiple perspectives and create a moving memorial story. Furthermore, the data management unit also has an indexing function that allows users to easily search for the deceased's data. This allows users to quickly find data related to specific events or themes.

[0079] The dialogue unit provides virtual dialogues based on data organized by the data processing unit. For example, the dialogue unit provides virtual dialogues that reproduce the deceased's speech patterns and tone. The dialogue unit can also learn the deceased's preferences and personality and provide virtual dialogues based on that information. Furthermore, the dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone during their lifetime. For example, the dialogue unit can reproduce the deceased's speech patterns and tone during their lifetime, allowing family and friends to reminisce about their memories of the deceased through virtual dialogues. The dialogue unit utilizes AI technology to reproduce the deceased's speech patterns and tone. For example, natural language processing technology can be used to analyze the deceased's speech patterns and tone and reproduce them during virtual dialogues. Additionally, speech synthesis technology can be used to reproduce the deceased's voice and use it during virtual dialogues. This allows the dialogue unit to faithfully reproduce the deceased's speech patterns and tone, enabling family and friends to reminisce about their memories of the deceased. Moreover, the dialogue unit can learn the deceased's preferences and personality and customize the content of the virtual dialogues. For example, if the deceased had a particular interest in a specific topic, a virtual dialogue related to that topic can be provided. Similarly, if the deceased had a particular hobby, a virtual dialogue related to that hobby can be provided. This allows the dialogue system to provide virtual conversations that reflect the personality of the deceased, enabling family and friends to reflect more deeply on their memories of the deceased.

[0080] The Access Unit provides access to virtual dialogues offered by the Dialogue Unit through a dedicated app. The Access Unit offers features such as the ability to send virtual floral tributes and memorial messages through the dedicated app. The Access Unit also allows users to reminisce about their memories with the deceased through the dedicated app. Furthermore, the Access Unit allows users to send virtual floral tributes and memorial messages through the dedicated app. For example, the Access Unit allows users to reminisce about their memories with the deceased and send virtual floral tributes and memorial messages through the dedicated app. The Access Unit provides an intuitive interface to allow users to easily access virtual dialogues. For example, the home screen of the dedicated app displays the deceased's photo and name, making it easy for users to start a virtual dialogue. The Access Unit also offers various options when users send virtual floral tributes and memorial messages. For example, users can choose and offer virtual flowers, or enter and send messages to the deceased. In addition, the Access Unit provides features for users to reminisce about their memories with the deceased. For example, users can view photos and videos of the deceased or read their diaries. The Access Unit also provides features for users to share their memories with the deceased. For example, family and friends can view the deceased's data together and share memories. This allows the access system to enable users to reminisce about their memories of the deceased, send virtual flowers and memorial messages, and remember the deceased together with family and friends.

[0081] The upload unit can upload data such as photos, videos, audio, and diaries of the deceased. For example, the upload unit can upload photos of the deceased in JPEG format. For example, the upload unit can also upload videos of the deceased in MP4 format. Furthermore, the upload unit can also upload audio of the deceased in MP3 format. For example, the upload unit can upload the deceased's diary in text file format. This allows for the uploading of a variety of data about the deceased. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input photos of the deceased into a generating AI and have the generating AI perform photo analysis.

[0082] The analysis unit can analyze uploaded data and learn the deceased's preferences and personality. For example, the analysis unit can analyze the deceased's photographs to learn their preferences and personality. The analysis unit can also analyze the deceased's videos to learn their preferences and personality. Furthermore, the analysis unit can analyze the deceased's voice to learn their preferences and personality. For example, the analysis unit can analyze the deceased's diary to learn their preferences and personality. By learning the deceased's preferences and personality, it is possible to provide more personalized services. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the deceased's photographs into a generating AI and have the generating AI perform the photo analysis.

[0083] The data analysis unit can organize the analyzed data chronologically and create a moving memorial story. For example, the data analysis unit can arrange the events of the deceased's life chronologically and create a moving memorial story. For example, the data analysis unit can organize the deceased's photographs chronologically and create a moving memorial story. The data analysis unit can also organize the deceased's videos chronologically and create a moving memorial story. For example, the data analysis unit can organize the deceased's diaries chronologically and create a moving memorial story. By creating a moving memorial story, one can feel the memory of the deceased more deeply. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or not. For example, the data analysis unit can input the deceased's photographs into a generating AI and have the generating AI perform the photo analysis.

[0084] The dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone of voice. For example, the dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone of voice. The dialogue unit can also learn the deceased's preferences and personality and provide virtual dialogues based on that. Furthermore, the dialogue unit can provide virtual dialogues that reproduce the deceased's speech patterns and tone of voice during their lifetime. For example, the dialogue unit can reproduce the deceased's speech patterns and tone of voice during their lifetime, allowing family and friends to reminisce about their memories of the deceased through virtual dialogues. This makes more realistic virtual dialogues possible by reproducing the deceased's speech patterns and tone of voice. Some or all of the above-described processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the deceased's speech patterns and tone of voice into a generating AI and have the generating AI generate virtual dialogues.

[0085] The access unit can provide functions for sending virtual floral tributes and memorial messages through a dedicated app. The access unit can, for example, provide functions for sending virtual floral tributes and memorial messages through a dedicated app. The access unit can, for example, allow users to reminisce about their memories with the deceased through a dedicated app. The access unit can also send virtual floral tributes and memorial messages through a dedicated app. For example, the access unit can reminisce about their memories with the deceased through a dedicated app and send virtual floral tributes and memorial messages. This allows users to send virtual floral tributes and memorial messages through a dedicated app. Some or all of the above-described processes in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input a dedicated app into a generating AI and have the generating AI execute the sending of virtual floral tributes and memorial messages.

[0086] The upload unit can estimate the user's emotions and adjust the timing of data uploads based on the estimated emotions. For example, if the user is sad, the upload unit can delay the upload and wait until the user calms down. If the user is emotionally stable, the upload unit can also start uploading the data immediately. Furthermore, if the user is in a hurry, the upload unit can select the optimal timing to complete the upload quickly. This allows for more appropriate data uploads by adjusting the timing of data uploads according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The upload unit can select the optimal upload method according to the type and format of the data. For example, in the case of photo data, the upload unit can efficiently upload it using compression technology. For example, in the case of video data, the upload unit can also upload it using streaming technology. Furthermore, in the case of audio data, the upload unit can select the optimal method for uploading while maintaining sound quality. In this way, by selecting the optimal upload method according to the type and format of the data, data can be uploaded efficiently. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the type and format of the data into a generating AI and have the generating AI select the optimal upload method.

[0088] The upload unit can determine upload priorities based on the importance of the data. For example, the upload unit will prioritize uploading important photos and videos. The upload unit can also postpone uploading text data such as diaries and notes. Furthermore, the upload unit can adjust the upload order based on importance levels specified by the user. This allows important data to be uploaded preferentially by determining upload priorities based on data importance. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the data importance levels into a generating AI and have the generating AI determine the upload priorities.

[0089] The upload unit can estimate the user's emotions and determine the priority of data to upload based on the estimated emotions. For example, if the user is emotionally unstable, the upload unit will prioritize uploading data related to those emotions. If the user is relaxed, the upload unit can also upload all data evenly. Furthermore, if the user is in a hurry, the upload unit can prioritize uploading important data. This allows for the prioritization of more appropriate data by determining the priority of data to upload according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The upload unit can prioritize uploading highly relevant data by considering the user's geographical location. For example, if the user is in the hometown of a deceased person, the upload unit will prioritize uploading data related to that location. If the user is traveling, the upload unit can also prioritize uploading data related to their travel destination. Furthermore, if the user is participating in a specific event, the upload unit can prioritize uploading data related to that event. In this way, by considering the user's geographical location, highly relevant data can be prioritized for uploading. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0091] The upload unit can analyze a user's social media activity and upload relevant data. For example, the upload unit can automatically upload photos and videos that a user has shared on social media. The upload unit can also prioritize uploading data related to events that a user has mentioned on social media. Furthermore, the upload unit can analyze the content of a user's social media posts and upload relevant data. This allows for the efficient uploading of relevant data by analyzing the user's social media activity. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input the user's social media activity into a generating AI and have the generating AI select relevant data.

[0092] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is sad, the analysis unit will prioritize analyzing emotional data. If the user is relaxed, the analysis unit can also analyze all data equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing important data. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit can apply different analysis algorithms depending on the type and format of the data. For example, the analysis unit can apply an image recognition algorithm to photographic data. For example, the analysis unit can also apply a video analysis algorithm to video data. Furthermore, the analysis unit can apply a speech recognition algorithm to audio data. This allows for efficient data analysis by applying different analysis algorithms depending on the type and format of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type and format of the data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0094] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can perform analysis by considering the relationship between photos and videos. For example, the analysis unit can also perform analysis by associating audio data with diary entries. Furthermore, the analysis unit can integrate multiple data formats to perform comprehensive analysis. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0095] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is sad, the analysis unit can highlight emotional data. If the user is relaxed, the analysis unit can also display all data equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying important data. This allows for a more appropriate display by adjusting how the analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize analyzing recently uploaded data. It may also postpone the analysis of older data. Furthermore, the analysis unit can adjust the order of analysis based on the submission date specified by the user. This allows for efficient data analysis by prioritizing analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on user-specified relevance. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0098] The data sorting unit can estimate the user's emotions and adjust the data sorting method based on the estimated emotions. For example, if the user is sad, the sorting unit will prioritize sorting emotional data. If the user is relaxed, the sorting unit can sort all data evenly. If the user is in a hurry, the sorting unit can prioritize sorting important data. By adjusting the data sorting method according to the user's emotions, more appropriate sorting becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using AI or not using AI. For example, the sorting unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The sorting unit can adjust the level of detail in sorting based on the importance of the data. For example, the sorting unit sorts important data in detail. For example, the sorting unit can sort less important data simply. The sorting unit can also adjust the level of detail in sorting based on the importance specified by the user. This allows for efficient data sorting by adjusting the level of detail in sorting based on the importance of the data. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in sorting.

[0100] The sorting unit can apply different sorting algorithms depending on the data category. For example, the sorting unit can apply an image sorting algorithm to photographic data. For example, it can also apply a video sorting algorithm to video data. Furthermore, it can apply an audio sorting algorithm to audio data. This allows for efficient data sorting by applying different sorting algorithms depending on the data category. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the data category into a generating AI and have the generating AI select the optimal sorting algorithm.

[0101] The sorting unit can estimate the user's emotions and adjust how the sorting results are displayed based on the estimated emotions. For example, if the user is sad, the sorting unit will highlight emotional data. If the user is relaxed, the sorting unit can also display all data equally. Furthermore, if the user is in a hurry, the sorting unit can prioritize displaying important data. This allows for a more appropriate display by adjusting how the sorting results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using AI or not. For example, the sorting unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The sorting unit can determine sorting priorities based on the data submission date. For example, the sorting unit might prioritize sorting recently uploaded data. It can also sort older data later. Furthermore, the sorting unit can adjust the sorting order based on the submission date specified by the user. This allows for efficient data sorting by determining sorting priorities based on the data submission date. Some or all of the above processes in the sorting unit may be performed using AI, for example, or not. For example, the sorting unit can input the data submission date into a generating AI and have the generating AI determine the sorting priority.

[0103] The sorting unit can adjust the sorting order based on the relevance of the data. For example, the sorting unit can prioritize sorting highly relevant data. For example, the sorting unit can also sort less relevant data later. Furthermore, the sorting unit can adjust the sorting order based on user-specified relevance. This allows for efficient data sorting by adjusting the sorting order based on data relevance. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the data relevance into a generating AI and have the generating AI perform the sorting order adjustment.

[0104] The dialogue unit can estimate the user's emotions and adjust the way the virtual dialogue is expressed based on the estimated emotions. For example, if the user is sad, the dialogue unit will speak in a gentle tone. If the user is relaxed, the dialogue unit can also speak in a natural tone. Furthermore, if the user is excited, the dialogue unit can speak in a lively tone. By adjusting the way the virtual dialogue is expressed according to the user's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The dialogue unit can optimize data to reproduce the deceased's speech patterns and tone during virtual dialogue. For example, the dialogue unit can analyze the deceased's past audio data to reproduce their speech patterns and tone. The dialogue unit can also generate natural dialogue based on the deceased's written data. Furthermore, the dialogue unit can reproduce facial expressions and gestures by referencing the deceased's video data. By optimizing the data to reproduce the deceased's speech patterns and tone, a more realistic virtual dialogue becomes possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the deceased's speech patterns and tone into a generating AI and have the generating AI perform the optimization.

[0106] The dialogue unit can customize the content of a virtual conversation based on the deceased's preferences and personality. For example, the dialogue unit can focus the conversation on topics the deceased enjoyed. The dialogue unit can also adopt a conversation style that suits the deceased's personality. Furthermore, the dialogue unit can select conversation content based on the deceased's hobbies and interests. This allows for more personalized conversations by customizing the content based on the deceased's preferences and personality. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the deceased's preferences and personality into a generating AI and have the generating AI perform the customization of the conversation content.

[0107] The dialogue unit can estimate the user's emotions and adjust the length of the virtual dialogue based on the estimated emotions. For example, if the user is sad, the dialogue unit can provide a shorter dialogue to reduce the emotional burden. If the user is relaxed, the dialogue unit can also provide a longer dialogue to allow more time to reflect on memories. Furthermore, if the user is in a hurry, the dialogue unit can provide a concise and to-the-point dialogue. By adjusting the length of the virtual dialogue according to the user's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The dialogue unit can determine the content of a virtual conversation by referring to the deceased's activity history during their lifetime. For example, the dialogue unit can determine the content based on events and activities the deceased participated in during their lifetime. The dialogue unit can also include topics related to the deceased's occupation or hobbies in the conversation. Furthermore, the dialogue unit can reflect memories of the deceased with family and friends in the conversation. This allows for more realistic conversation content by referring to the deceased's activity history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the deceased's activity history into a generating AI and have the generating AI determine the content of the conversation.

[0109] The dialogue unit can improve the accuracy of the dialogue during virtual conversations by referring to the deceased's relevant literature. For example, the dialogue unit can refine the dialogue content by referring to literature and diaries written by the deceased. The dialogue unit can also supplement the dialogue content based on literature and materials cited by the deceased. Furthermore, the dialogue unit can enrich the dialogue content by referring to literature related to the deceased's research and achievements. As a result, the accuracy of the dialogue is improved by referring to the deceased's relevant literature. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the deceased's relevant literature into a generating AI and have the generating AI refine the dialogue content.

[0110] The access unit can estimate the user's emotions and adjust the access method based on the estimated emotions. For example, if the user is sad, the access unit can provide a simple and intuitive access method. For example, if the user is relaxed, the access unit can also provide an access method that includes detailed information. Furthermore, if the user is in a hurry, the access unit can provide a method for quick access. This allows for more appropriate access by adjusting the access method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the access unit may be performed using AI or not using AI. For example, the access unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The access unit can select the optimal access method by referring to the user's past access history when accessing the system. For example, the access unit may prioritize providing access methods that the user has used in the past. For example, the access unit may also suggest the most frequently used method based on the user's past access history. Furthermore, the access unit may analyze the user's past access patterns and select the optimal method. This allows the system to provide the optimal access method by referring to the user's past access history. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit may input the user's past access history into a generating AI and have the generating AI select the optimal access method.

[0112] The access unit can provide the optimal access method by considering the user's device information at the time of access. For example, if the user is using a smartphone, the access unit can provide an access method optimized for mobile devices. If the user is using a tablet, the access unit can also provide an access method optimized for large screens. Furthermore, if the user is using a desktop computer, the access unit can provide an access method that includes detailed information. In this way, the optimal access method can be provided by considering the user's device information. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's device information into a generating AI and have the generating AI select the optimal access method.

[0113] The access unit can estimate the user's emotions and determine access priorities based on the estimated emotions. For example, if the user is sad, the access unit will prioritize access to emotional data. If the user is relaxed, the access unit may also provide equal access to all data. Furthermore, if the user is in a hurry, the access unit may prioritize access to important data. This allows for more appropriate access by determining access priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the access unit may be performed using AI or not using AI. For example, the access unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The access unit can provide the optimal access method by considering the user's geographical location information at the time of access. For example, if the user is in the hometown of a deceased person, the access unit may prioritize access to data related to that location. For example, if the user is traveling, the access unit may also prioritize access to data related to the travel destination. Furthermore, if the user is participating in a specific event, the access unit may also prioritize access to data related to that event. In this way, the optimal access method can be provided by considering the user's geographical location information. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal access method.

[0115] The access unit can analyze the user's social media activity and suggest access methods when accessing content. For example, the access unit may prioritize access to photos and videos shared by the user on social media. It may also prioritize access to data related to events mentioned by the user on social media. Furthermore, the access unit can analyze the content of the user's social media posts and suggest access to relevant data. In this way, by analyzing the user's social media activity, the optimal access method can be suggested. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's social media activity into a generating AI and have the generating AI suggest the optimal access method.

[0116] The access unit can refer to the user's calendar information and make suggestions based on their schedule when accessing data. For example, the access unit can refer to appointments registered in the user's calendar and suggest access to related data. The access unit can also prioritize access to data related to a specific event based on the user's calendar information. Furthermore, the access unit can suggest the most suitable access method based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the user's calendar information. Some or all of the above processing in the access unit may be performed using AI, for example, or without AI. For example, the access unit can input the user's calendar information into a generating AI and have the generating AI execute a suggestion for the most suitable access method.

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

[0118] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0119] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0120] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0121] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0122] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0123] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0124] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0125] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0126] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

[0127] A digital memorial garden not only preserves memories of the deceased but can also provide interactive experiences based on their activities during their lifetime. For example, a virtual tour can be created based on data of places the deceased visited and events they attended. This allows family and friends to retrace their steps and share memories. Letters and notes written by the deceased can also be digitized and used as part of a virtual dialogue. Furthermore, a more personalized memorial experience can be achieved by providing customized content based on the deceased's hobbies and interests.

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

[0129] Step 1: The upload section allows you to upload the deceased person's data. This data includes photos, videos, audio, and diaries. For example, the upload section allows you to upload photos in JPEG format, videos in MP4 format, audio in MP3 format, and diaries as text files. Step 2: The analysis unit analyzes the data uploaded by the upload unit. For example, it analyzes the deceased's photos, videos, audio, and diary to learn about their preferences and personality. Step 3: The organization department organizes the data analyzed by the analysis department in chronological order. For example, they arrange events, photos, videos, and diaries from the deceased's life in chronological order to create a moving memorial story. Step 4: The dialogue unit provides a virtual dialogue based on the data organized by the data processing unit. For example, it can recreate the deceased's speech patterns and tone, allowing family and friends to reminisce about their memories of the deceased through the virtual dialogue. Step 5: The access unit accesses the virtual dialogue provided by the dialogue unit through a dedicated app. For example, it provides functions to send virtual floral tributes and memorial messages through the dedicated app, allowing users to reminisce about memories with the deceased.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0133] Each of the multiple elements described above, including the upload unit, analysis unit, organization unit, dialogue unit, and access unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the smart device 14 and uploads photos, videos, audio, diaries, etc., of the deceased. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded data. The organization unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes the analyzed data in chronological order. The dialogue unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides virtual dialogue based on the organized data. The access unit is implemented by the control unit 46A of the smart device 14 and provides functions for virtual dialogue, virtual flower offering, and sending memorial messages through a dedicated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the upload unit, analysis unit, organization unit, dialogue unit, and access unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the smart glasses 214 and uploads photos, videos, audio, diaries, etc., of the deceased. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded data. The organization unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes the analyzed data in chronological order. The dialogue unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides virtual dialogue based on the organized data. The access unit is implemented by the control unit 46A of the smart glasses 214 and provides functions for virtual dialogue, virtual flower offering, and sending memorial messages through a dedicated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the upload unit, analysis unit, organization unit, dialogue unit, and access unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the headset terminal 314 and uploads photos, videos, audio, diaries, etc., of the deceased. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded data. The organization unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes the analyzed data in chronological order. The dialogue unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides virtual dialogue based on the organized data. The access unit is implemented by the control unit 46A of the headset terminal 314 and provides functions for virtual dialogue, virtual flower offering, and sending memorial messages through a dedicated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the upload unit, analysis unit, organization unit, dialogue unit, and access unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the robot 414 and uploads photos, videos, audio, diaries, etc., of the deceased. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded data. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and organizes the analyzed data in chronological order. The dialogue unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides virtual dialogue based on the organized data. The access unit is implemented by, for example, the control unit 46A of the robot 414 and provides functions for virtual dialogue, virtual flower offering, and sending memorial messages through a dedicated application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0192] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0201] (Note 1) The upload section for uploading data of the deceased, An analysis unit analyzes the data uploaded by the aforementioned upload unit, The data analyzed by the aforementioned analysis unit is organized in chronological order by the organization unit, A dialogue unit provides a virtual dialogue based on the data organized by the aforementioned organizing unit, The system includes an access unit that accesses the virtual dialogue provided by the aforementioned dialogue unit through a dedicated application. A system characterized by the following features. (Note 2) The aforementioned upload unit, Upload data such as photos, videos, audio recordings, and diaries of the deceased. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The system analyzes uploaded data to learn the deceased person's preferences and personality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned editing unit, The analyzed data is organized chronologically to create a moving memorial story. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, This service provides virtual dialogue that recreates the deceased person's speech patterns and manner of speaking. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned access unit is The app provides a function to send virtual floral tributes and memorial messages. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned upload unit, It estimates user sentiment and adjusts data upload timing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned upload unit, Select the optimal upload method depending on the type and format of the data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned upload unit, Prioritize uploads based on data importance. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned upload unit, It estimates the user's emotions and prioritizes the data to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned upload unit, Prioritize uploading highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned upload unit, Analyze users' social media activity and upload relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Apply different analytical algorithms depending on the type and format of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Improve the accuracy of analysis by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Prioritize analysis based on data submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editing unit, We estimate the user's emotions and adjust how the data is organized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editing unit, Adjust the level of detail in the sorting based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editing unit, Apply different sorting algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned editing unit, It estimates the user's emotions and adjusts how the results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned editing unit, Prioritize the processing based on the data submission date. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned editing unit, Adjust the order of sorting based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way virtual dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, Optimizing data to reproduce the deceased person's speech patterns and tone during virtual conversations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During virtual conversations, the dialogue content is customized based on the deceased person's preferences and personality. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the length of the virtual dialogue based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During virtual dialogue, the content of the conversation is determined by referring to the deceased person's activity history during their lifetime. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During virtual dialogues, we improve the accuracy of the conversation by referring to relevant literature about the deceased. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned access unit is It estimates the user's emotions and adjusts the access method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned access unit is When accessing a system, the system selects the optimal access method by referring to the user's past access history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned access unit is When accessing the system, the system provides the optimal access method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned access unit is It estimates user sentiment and determines access priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned access unit is When accessing the site, the system provides the optimal access method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned access unit is When accessing the site, the system analyzes the user's social media activity and suggests ways to access it. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned access unit is When accessed, the system references the user's calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The upload section for uploading data of the deceased, An analysis unit analyzes the data uploaded by the aforementioned upload unit, The data analyzed by the aforementioned analysis unit is organized in chronological order by the organization unit, A dialogue unit provides a virtual dialogue based on the data organized by the aforementioned organizing unit, The system includes an access unit that accesses the virtual dialogue provided by the aforementioned dialogue unit through a dedicated application. A system characterized by the following features.

2. The aforementioned upload unit is Upload data such as photos, videos, audio recordings, and diaries of the deceased. The system according to feature 1.

3. The aforementioned analysis unit is The system analyzes uploaded data to learn the deceased person's preferences and personality. The system according to feature 1.

4. The aforementioned editing unit, The analyzed data is organized chronologically to create a moving memorial story. The system according to feature 1.

5. The aforementioned dialogue unit, This service provides virtual dialogue that recreates the deceased person's speech patterns and manner of speaking. The system according to feature 1.

6. The aforementioned access unit is The app provides a function to send virtual floral tributes and memorial messages. The system according to feature 1.

7. The aforementioned upload unit is It estimates user sentiment and adjusts data upload timing based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned upload unit is Select the optimal upload method depending on the type and format of the data. The system according to feature 1.

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