System

Abstract

The system according to the embodiment aims to easily convert digital content into a physical form.SOLUTION: A system includes a digital content collection unit, an analysis unit, and a conversion unit. The digital content collection unit collects digital content from a user. The analysis unit analyzes the digital content collected by the digital content collection unit. The conversion unit converts the digital content analyzed by the analysis unit into a physical form.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process of converting digital content into a physical form is complicated, and there is a lack of services that are easily accessible to users.

[0005] The system according to the embodiment aims to easily convert digital content into a physical form. [Means for solving the problem]

[0006] A system according to an embodiment includes a digital content collection unit, an analysis unit, and a conversion unit. The digital content collection unit collects digital content from users. The analysis unit analyzes the digital content collected by the digital content collection unit. The conversion unit converts the digital content analyzed by the analysis unit into a physical form. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy conversion of digital content into physical form. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Memories Cube System according to an embodiment of the present invention is a system that allows users to convert their memories of the deceased from digital content into a physical form, allowing them to feel a connection with the deceased in their daily lives. In this way, the Memories Cube System allows users to keep their memories of the deceased in a physical form, allowing them to feel a connection with the deceased in their daily lives.

[0029] The memory cube system according to the embodiment includes a digital content collection unit, an analysis unit, and a conversion unit. The digital content collection unit collects digital content from users. For example, it collects photos, videos, and audio messages uploaded by users. The digital content collection unit can also store the digital content provided by users in cloud storage. For example, it can store data using cloud services such as Google Drive and Dropbox. The digital content collection unit can also automatically categorize the digital content provided by users. For example, it can store the content in categories such as photos, videos, and audio messages. The analysis unit analyzes the collected digital content. For example, a generation AI analyzes the content of photos and videos to extract important elements. The generation AI also converts the content of audio messages into text and extracts important messages. For example, the generation AI can identify specific people or places in photos or specific scenes in videos. The generation AI can also analyze the content of audio messages and evaluate the intensity and type of emotions. The conversion unit converts the analyzed digital content into a physical form. For example, it uses 3D printing technology to create small items or custom souvenirs. The conversion unit can also propose optimal shapes and designs based on the user's preferences. Examples include a figurine that resembles the face of the deceased, a miniature that recreates a specific scene, or an audio device that plays the deceased's voice. This allows the memory cube system of the embodiment to preserve memories of the deceased in a physical form, allowing users to feel a connection with the deceased in their daily lives. Examples include a figurine that can be displayed in the living room and seen every day, or an audio device that allows users to hear the deceased's voice.

[0030] The digital content collection unit can automatically collect relevant digital content from social media accounts or email archives and suggest it to the user. The digital content collection unit, for example, accesses the deceased's social media accounts and builds a system that automatically collects posted photos, videos, and messages. For example, it analyzes Facebook and Instagram posts and extracts relevant content. The digital content collection unit can also collect relevant digital content from the deceased's email archives. For example, it can search emails based on specific keywords and extract relevant messages and attachments. Furthermore, the digital content collection unit can suggest collected content to the user. For example, it can present collected photos and videos to the user to help them make a selection. This allows related digital content to be automatically collected from the deceased's social media accounts and email archives.

[0031] The digital content collection unit can use generative AI to analyze interviews in which users talk about their memories with the deceased, convert important episodes into text, and collect the text as digital content. For example, the digital content collection unit records interviews in which users talk about their memories with the deceased and analyzes the audio data using generative AI. For example, it converts the content of the interview into text and extracts important episodes. The digital content collection unit can also analyze video interviews. For example, it can analyze the video and audio of a video interview and extract important scenes and messages. Furthermore, the digital content collection unit can save the analysis results as digital content. For example, it can save the converted text episodes in cloud storage and make them accessible to users. This allows the digital content collection unit to analyze interviews in which users talk about their memories with the deceased, convert important episodes into text, and collect them.

[0032] The digital content collection unit can include objects related to the hobbies and interests of the deceased in its collection. The digital content collection unit can, for example, build a system that automatically collects music and movie scenes that the deceased liked. For example, it can analyze Spotify or Netflix history and extract related content. The digital content collection unit can also collect photos and videos related to the deceased's hobbies. For example, it can collect photos taken by the deceased and video clips related to the hobbies. Furthermore, the digital content collection unit can suggest collected objects to the user. For example, it can present collected music and movie scenes to the user to help them make a selection. This allows objects related to the deceased's hobbies and interests to be included in its collection.

[0033] The digital content collection unit provides an online platform for users to share memories with the deceased and can collect feedback from other users. The digital content collection unit, for example, builds an online platform for users to share memories with the deceased. For example, the digital content collection unit uploads photos and videos and collects comments and feedback from other users. The digital content collection unit can also collect ratings and impressions of the shared content. For example, the digital content collection unit analyzes comments and ratings posted by other users and provides them as feedback. Furthermore, the digital content collection unit can make new suggestions to the user based on the collected feedback. For example, the digital content collection unit can suggest new ways to collect memories to the user based on content that has received high ratings from other users. This allows the user to share memories with the deceased and collect feedback from other users.

[0034] The analysis unit can automatically translate the analysis results of the digital content into different languages ​​and obtain feedback from an international perspective. The analysis unit, for example, uses generative AI to build a system that automatically translates the analysis results of the digital content into different languages. For example, it translates into multiple languages ​​such as English, French, and Chinese. The analysis unit can also collect feedback to evaluate the translated content from an international perspective. For example, it collects comments and ratings from users who speak different languages ​​and provides them as feedback. Furthermore, the analysis unit can optimize the analysis results based on the collected feedback. For example, it can reflect feedback from an international perspective and improve the analysis results. This allows automatic translation into different languages ​​and obtain feedback from an international perspective.

[0035] The analysis unit can automatically generate new content related to the hobbies and interests of the deceased. For example, the analysis unit uses generative AI to build a system that automatically generates new content related to the hobbies and interests of the deceased. For example, new content can be created based on music or movie scenes that the deceased liked. The analysis unit can also generate new content based on photos and videos related to the hobbies of the deceased. For example, it can create artwork based on photos taken by the deceased or video clips related to the hobbies. Furthermore, the analysis unit can provide the generated new content to the user. For example, it can present new music or movie scenes to the user and share memories. This makes it possible to automatically generate new content related to the hobbies and interests of the deceased.

[0036] The conversion unit can design custom-made keepsakes that reflect the emotions or personality of the deceased. For example, the conversion unit uses generative AI to build a system that designs custom-made keepsakes that reflect the emotions and personality of the deceased. For example, it can create a figurine that recreates the deceased's smile or an object based on a specific episode. The conversion unit can also suggest optimal designs based on the user's wishes. For example, it can suggest designs related to the deceased's hobbies and interests. Furthermore, the conversion unit can create the designed keepsakes using 3D printing technology. For example, it can 3D print a figurine that resembles the deceased's face or a miniature that recreates a specific scene. This makes it possible to design custom-made keepsakes that reflect the emotions and personality of the deceased.

[0037] The conversion unit can 3D print an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to design an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content and build a system for 3D printing. For example, it reproduces the facial and bodily features of the deceased. The conversion unit can also design figurine with interactive functions. For example, it can create figurine with moving parts and audio playback functions. Furthermore, the conversion unit can suggest customizable figurine according to the user's wishes. For example, it can customize the clothing and accessories of the deceased. This allows for 3D printing of an interactive figurine that reproduces the features of the deceased.

[0038] The conversion unit can design an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to build a system that designs an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. For example, it creates a speaker that reproduces the voice of the deceased. The conversion unit can also suggest a customizable audio device according to the user's wishes. For example, it can create a portable device that plays the message of the deceased. Furthermore, the conversion unit can optimize the design of the audio device. For example, it can suggest a design related to the hobbies and interests of the deceased. This makes it possible to design an audio device that plays the voice or message of the deceased.

[0039] The conversion unit can 3D print objects related to the hobbies or interests of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to build a system that designs and 3D prints objects related to the hobbies and interests of the deceased based on the analysis results of the digital content. For example, it can recreate musical instruments or movie scenes that the deceased loved. The conversion unit can also suggest customizable objects according to the user's wishes. For example, it can suggest designs related to the hobbies of the deceased. Furthermore, the conversion unit can use 3D printing technology to create objects with high precision. For example, it can create figurines or models that are reproduced in detail. This allows objects related to the hobbies and interests of the deceased to be 3D printed.

[0040] The conversion unit can create a miniature set that recreates the life story of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to design a miniature set that recreates the life story of the deceased based on the analysis results of the digital content and build a system for 3D printing it. For example, it recreates important scenes from the deceased's life. The conversion unit can also suggest customizable miniature sets according to the user's wishes. For example, it can recreate scenes from the deceased's home, workplace, travel destinations, etc. Furthermore, the conversion unit can optimize the design of the miniature set. For example, it can suggest designs related to the hobbies and interests of the deceased. This allows for the creation of a miniature set that recreates the life story of the deceased.

[0041] The conversion unit can suggest custom-made interior items that allow users to feel memories of the deceased in their daily lives. For example, the conversion unit uses generative AI to build a system that suggests custom-made interior items that allow users to feel memories of the deceased in their daily lives. For example, it can create artwork based on photographs of the deceased or interior items that recreate specific scenes. The conversion unit can also suggest customizable interior items according to the user's wishes. For example, it can suggest designs related to the hobbies and interests of the deceased. Furthermore, the conversion unit can optimize the design of the interior items. For example, it can suggest designs that incorporate photographs and messages of the deceased. This makes it possible to suggest custom-made interior items that allow users to feel memories of the deceased in their daily lives.

[0042] The conversion unit can automatically generate a diary or memorial book themed around memories of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generation AI to build a system that automatically generates a diary or memorial book themed around memories of the deceased based on the analysis results of the digital content. For example, it creates a diary that combines photos and messages. The conversion unit can also suggest customizable diaries and memorial books according to the user's wishes. For example, it can suggest designs related to the hobbies and interests of the deceased. Furthermore, the conversion unit can provide the generated diary or memorial book to the user. For example, it can provide it as a printed book or a digital album. This makes it possible to automatically generate diaries and memorial books themed around memories of the deceased.

[0043] The conversion unit can create artwork themed around memories of the deceased based on the analysis results of the digital content and provide it to the user. The conversion unit, for example, uses generative AI to build a system that creates artwork themed around memories of the deceased based on the analysis results of the digital content. For example, it creates a painting based on a photograph of the deceased or an artwork that recreates a specific scene. The conversion unit can also suggest customizable artwork according to the user's wishes. For example, it can suggest a design related to the hobbies and interests of the deceased. Furthermore, the conversion unit can provide the generated artwork to the user. For example, it can provide it as a printed poster or a digital artwork. In this way, artwork themed around memories of the deceased can be created and provided to the user.

[0044] The conversion unit can provide an online community for users to share memories of the deceased and promote interaction with other users. The conversion unit, for example, builds an online community for users to share memories of the deceased. For example, users upload photos and videos and exchange comments and messages with other users. The conversion unit can also collect ratings and opinions on the shared content. For example, the conversion unit can analyze comments and ratings posted by other users and provide them as feedback. Furthermore, the conversion unit can promote events and discussions within the online community. For example, an event themed around memories of the deceased can be held to deepen interaction between users. This allows users to share memories of the deceased and promote interaction with other users.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The digital content collection unit provides an online platform for users to share memories with the deceased and can collect feedback from other users. For example, an online platform is constructed for users to share memories with the deceased. For example, photos and videos are uploaded and comments and feedback from other users are collected. The digital content collection unit can also collect ratings and impressions of the shared content. For example, the comments and ratings posted by other users are analyzed and provided as feedback. Furthermore, the digital content collection unit can make new suggestions to the user based on the collected feedback. For example, the digital content collection unit can suggest new ways to collect memories to the user based on content that has received high ratings from other users. This allows the user to share memories with the deceased and collect feedback from other users.

[0047] The conversion unit can 3D print an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content. For example, a system can be built using generative AI to design and 3D print an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content. For example, the facial and bodily features of the deceased can be reproduced. The conversion unit can also design figurine with interactive functions. For example, it can create a figurine with moving parts or audio playback functions. Furthermore, the conversion unit can suggest customizable figurine according to the user's wishes. For example, customizing the clothing and accessories of the deceased. This allows for the 3D printing of an interactive figurine that reproduces the features of the deceased.

[0048] The conversion unit can create a miniature set that recreates the life story of the deceased based on the analysis results of the digital content. For example, a system can be built using generative AI to design and 3D print a miniature set that recreates the life story of the deceased based on the analysis results of the digital content. For example, it can recreate important scenes from the deceased's life. The conversion unit can also suggest customizable miniature sets according to the user's wishes. For example, it can recreate scenes from the deceased's home, workplace, travel destinations, etc. Furthermore, the conversion unit can optimize the design of the miniature set. For example, it can suggest designs related to the hobbies and interests of the deceased. This allows for the creation of a miniature set that recreates the life story of the deceased.

[0049] The conversion unit can design an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. For example, a system can be built using generative AI to design an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. For example, a speaker that reproduces the voice of the deceased can be created. The conversion unit can also suggest customizable audio devices according to the user's wishes. For example, a portable device that plays the message of the deceased can be created. Furthermore, the conversion unit can optimize the design of the audio device. For example, a design related to the hobbies and interests of the deceased can be suggested. This makes it possible to design an audio device that plays the voice or message of the deceased.

[0050] The conversion unit can create artwork themed around memories of the deceased based on the analysis results of the digital content and provide it to the user. For example, a system can be built using generative AI to create artwork themed around memories of the deceased based on the analysis results of the digital content. For example, a painting based on a photograph of the deceased or an artwork recreating a specific scene can be created. The conversion unit can also suggest customizable artwork according to the user's wishes. For example, it can suggest a design related to the hobbies and interests of the deceased. Furthermore, the conversion unit can provide the generated artwork to the user. For example, it can provide it as a printed poster or a digital artwork. In this way, artwork themed around memories of the deceased can be created and provided to the user.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The digital content collection unit collects digital content from users. For example, it collects photos, videos, and audio messages uploaded by users. The digital content collection unit can also store the digital content provided by users in cloud storage. For example, it can store data using cloud services such as Google Drive and Dropbox. Furthermore, the digital content collection unit can automatically classify the digital content provided by users. For example, it can store the content in categories such as photos, videos, and audio messages. Step 2: The analysis unit analyzes the collected digital content. For example, the generation AI analyzes the content of photos and videos and extracts important elements. The generation AI also converts the content of voice messages into text and extracts important messages. For example, the generation AI can identify specific people or places in photos, or specific scenes in videos. The generation AI can also analyze the content of voice messages and evaluate the intensity and type of emotions. Step 3: The converter transforms the analyzed digital content into a physical form. For example, it can use 3D printing technology to create small items or custom keepsakes. The converter can also suggest the optimal shape and design based on the user's preferences. For example, it could be a figurine of the deceased's face, a miniature replica of a specific scene, or an audio device that plays the deceased's voice.

[0053] (Example 2) The Memories Cube System according to an embodiment of the present invention is a system that allows users to convert their memories of the deceased from digital content into a physical form, allowing them to feel a connection with the deceased in their daily lives. In this way, the Memories Cube System allows users to keep their memories of the deceased in a physical form, allowing them to feel a connection with the deceased in their daily lives.

[0054] The memory cube system according to the embodiment includes a digital content collection unit, an analysis unit, and a conversion unit. The digital content collection unit collects digital content from users. For example, it collects photos, videos, and audio messages uploaded by users. The digital content collection unit can also store the digital content provided by users in cloud storage. For example, it can store data using cloud services such as Google Drive and Dropbox. The digital content collection unit can also automatically categorize the digital content provided by users. For example, it can store the content in categories such as photos, videos, and audio messages. The analysis unit analyzes the collected digital content. For example, a generation AI analyzes the content of photos and videos to extract important elements. The generation AI also converts the content of audio messages into text and extracts important messages. For example, the generation AI can identify specific people or places in photos or specific scenes in videos. The generation AI can also analyze the content of audio messages and evaluate the intensity and type of emotions. The conversion unit converts the analyzed digital content into a physical form. For example, it uses 3D printing technology to create small items or custom souvenirs. The conversion unit can also propose optimal shapes and designs based on the user's preferences. Examples include a figurine that resembles the face of the deceased, a miniature that recreates a specific scene, or an audio device that plays the deceased's voice. This allows the memory cube system of the embodiment to preserve memories of the deceased in a physical form, allowing users to feel a connection with the deceased in their daily lives. Examples include a figurine that can be displayed in the living room and seen every day, or an audio device that allows users to hear the deceased's voice.

[0055] The digital content collection unit performs sentiment analysis on digital content and can automatically select important memories based on the intensity and type of emotion. The digital content collection unit uses generative AI to perform sentiment analysis on photos and videos uploaded by users. For example, it detects emotional expressions such as smiles and tears and quantifies the intensity of the emotion. It prioritizes the selection of content with a high sentiment score. The digital content collection unit can also perform sentiment analysis on voice messages. For example, it analyzes the tone and speed of the voice to evaluate the intensity of the emotion. Furthermore, the digital content collection unit can suggest important memories to the user based on the results of the sentiment analysis. For example, it can present photos and videos with high sentiment scores to the user to assist in the selection. This allows for the automatic selection of emotionally important memories.

[0056] The digital content collection unit can automatically collect relevant digital content from social media accounts or email archives and suggest it to the user. The digital content collection unit, for example, accesses the deceased's social media accounts and builds a system that automatically collects posted photos, videos, and messages. For example, it analyzes Facebook and Instagram posts and extracts relevant content. The digital content collection unit can also collect relevant digital content from the deceased's email archives. For example, it can search emails based on specific keywords and extract relevant messages and attachments. Furthermore, the digital content collection unit can suggest collected content to the user. For example, it can present collected photos and videos to the user to help them make a selection. This allows related digital content to be automatically collected from the deceased's social media accounts and email archives.

[0057] The digital content collection unit can use generative AI to analyze interviews in which users talk about their memories with the deceased, convert important episodes into text, and collect the text as digital content. For example, the digital content collection unit records interviews in which users talk about their memories with the deceased and analyzes the audio data using generative AI. For example, it converts the content of the interview into text and extracts important episodes. The digital content collection unit can also analyze video interviews. For example, it can analyze the video and audio of a video interview and extract important scenes and messages. Furthermore, the digital content collection unit can save the analysis results as digital content. For example, it can save the converted text episodes in cloud storage and make them accessible to users. This allows the digital content collection unit to analyze interviews in which users talk about their memories with the deceased, convert important episodes into text, and collect them.

[0058] The digital content collection unit can include objects related to the hobbies and interests of the deceased in its collection. The digital content collection unit can, for example, build a system that automatically collects music and movie scenes that the deceased liked. For example, it can analyze Spotify or Netflix history and extract related content. The digital content collection unit can also collect photos and videos related to the deceased's hobbies. For example, it can collect photos taken by the deceased and video clips related to the hobbies. Furthermore, the digital content collection unit can suggest collected objects to the user. For example, it can present collected music and movie scenes to the user to help them make a selection. This allows objects related to the deceased's hobbies and interests to be included in its collection.

[0059] The digital content collection unit provides an online platform for users to share memories with the deceased and can collect feedback from other users. The digital content collection unit, for example, builds an online platform for users to share memories with the deceased. For example, the digital content collection unit uploads photos and videos and collects comments and feedback from other users. The digital content collection unit can also collect ratings and impressions of the shared content. For example, the digital content collection unit analyzes comments and ratings posted by other users and provides them as feedback. Furthermore, the digital content collection unit can make new suggestions to the user based on the collected feedback. For example, the digital content collection unit can suggest new ways to collect memories to the user based on content that has received high ratings from other users. This allows the user to share memories with the deceased and collect feedback from other users.

[0060] The digital content collection unit can use an app equipped with an emotion estimation function to estimate the emotions of a user when entering memories of the deceased in real time and make suggestions to elicit positive emotions. The digital content collection unit, for example, develops an app equipped with the emotion estimation function and analyzes the emotions of a user when entering memories of the deceased in real time. For example, the app analyzes the user's facial expressions and voice using a camera or microphone. The digital content collection unit can also use the emotion estimation function to calculate the user's emotion score. For example, the digital content collection unit evaluates the intensity and type of emotion based on changes in the user's facial expressions and voice. Furthermore, the digital content collection unit can make suggestions to elicit positive emotions from the user based on the emotion score. For example, the digital content collection unit can present memories that make the user smile and elicit positive emotions. This allows the user to elicit positive emotions when entering memories of the deceased.

[0061] The analysis unit can extract emotionally significant scenes or messages from digital content and present them to the user. The analysis unit, for example, uses generative AI to build a system that automatically extracts emotionally significant scenes from photos and videos. For example, it identifies scenes that express strong emotions, such as smiles or tears. The analysis unit can also extract emotionally significant messages from audio messages. For example, it identifies important messages based on the intensity and type of emotion. Furthermore, the analysis unit can present the extracted scenes and messages to the user. For example, it can present emotionally significant scenes and messages to the user to help them select memories. This allows emotionally significant scenes and messages to be presented to the user.

[0062] The analysis unit can analyze changes in the deceased's voice or facial expression and visualize the changes in their emotions. For example, the analysis unit uses generative AI to analyze changes in the deceased's voice and build a system that visualizes the changes in their emotions. For example, it analyzes changes in voice tone and pitch and displays the changes in emotions in a graph. The analysis unit can also analyze changes in the deceased's facial expression. For example, it uses facial expression recognition technology to analyze changes in facial expressions such as smiles and tears and visualize the changes in emotions. Furthermore, the analysis unit can present the analysis results to the user. For example, it can provide the user with graphs or charts showing the changes in the deceased's emotions to deepen their understanding of their memories. This makes it possible to analyze changes in the deceased's voice and facial expression and visualize the changes in emotions.

[0063] The analysis unit can automatically translate the analysis results of the digital content into different languages ​​and obtain feedback from an international perspective. The analysis unit, for example, uses generative AI to build a system that automatically translates the analysis results of the digital content into different languages. For example, it translates into multiple languages ​​such as English, French, and Chinese. The analysis unit can also collect feedback to evaluate the translated content from an international perspective. For example, it collects comments and ratings from users who speak different languages ​​and provides them as feedback. Furthermore, the analysis unit can optimize the analysis results based on the collected feedback. For example, it can reflect feedback from an international perspective and improve the analysis results. This allows automatic translation into different languages ​​and obtain feedback from an international perspective.

[0064] The analysis unit can automatically generate new content related to the hobbies and interests of the deceased. For example, the analysis unit uses generative AI to build a system that automatically generates new content related to the hobbies and interests of the deceased. For example, new content can be created based on music or movie scenes that the deceased liked. The analysis unit can also generate new content based on photos and videos related to the hobbies of the deceased. For example, it can create artwork based on photos taken by the deceased or video clips related to the hobbies. Furthermore, the analysis unit can provide the generated new content to the user. For example, it can present new music or movie scenes to the user and share memories. This makes it possible to automatically generate new content related to the hobbies and interests of the deceased.

[0065] The analysis unit can use the emotion estimation function to collect the user's emotional reactions to the analyzed digital content and optimize the analysis results based on the collected data. The analysis unit, for example, uses the emotion estimation function to build a system that collects the user's emotional reactions to the analyzed digital content in real time. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotional score. The analysis unit can also optimize the analysis results based on the collected emotional reactions. For example, content with a high user emotional score can be presented preferentially. Furthermore, the analysis unit can accumulate emotional reaction data and improve the accuracy of the analysis algorithm. For example, the accuracy of the analysis results can be improved based on past emotional reaction data. In this way, the user's emotional reactions can be collected and the analysis results can be optimized.

[0066] The conversion unit can design custom-made keepsakes that reflect the emotions or personality of the deceased. For example, the conversion unit uses generative AI to build a system that designs custom-made keepsakes that reflect the emotions and personality of the deceased. For example, it can create a figurine that recreates the deceased's smile or an object based on a specific episode. The conversion unit can also suggest optimal designs based on the user's wishes. For example, it can suggest designs related to the deceased's hobbies and interests. Furthermore, the conversion unit can create the designed keepsakes using 3D printing technology. For example, it can 3D print a figurine that resembles the deceased's face or a miniature that recreates a specific scene. This makes it possible to design custom-made keepsakes that reflect the emotions and personality of the deceased.

[0067] The conversion unit can 3D print an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to design an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content and build a system for 3D printing. For example, it reproduces the facial and bodily features of the deceased. The conversion unit can also design figurine with interactive functions. For example, it can create figurine with moving parts and audio playback functions. Furthermore, the conversion unit can suggest customizable figurine according to the user's wishes. For example, it can customize the clothing and accessories of the deceased. This allows for 3D printing of an interactive figurine that reproduces the features of the deceased.

[0068] The conversion unit can design an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to build a system that designs an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. For example, it creates a speaker that reproduces the voice of the deceased. The conversion unit can also suggest a customizable audio device according to the user's wishes. For example, it can create a portable device that plays the message of the deceased. Furthermore, the conversion unit can optimize the design of the audio device. For example, it can suggest a design related to the hobbies and interests of the deceased. This makes it possible to design an audio device that plays the voice or message of the deceased.

[0069] The conversion unit can 3D print objects related to the hobbies or interests of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to build a system that designs and 3D prints objects related to the hobbies and interests of the deceased based on the analysis results of the digital content. For example, it can recreate musical instruments or movie scenes that the deceased loved. The conversion unit can also suggest customizable objects according to the user's wishes. For example, it can suggest designs related to the hobbies of the deceased. Furthermore, the conversion unit can use 3D printing technology to create objects with high precision. For example, it can create figurines or models that are reproduced in detail. This allows objects related to the hobbies and interests of the deceased to be 3D printed.

[0070] The conversion unit can create a miniature set that recreates the life story of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generative AI to design a miniature set that recreates the life story of the deceased based on the analysis results of the digital content and build a system for 3D printing it. For example, it recreates important scenes from the deceased's life. The conversion unit can also suggest customizable miniature sets according to the user's wishes. For example, it can recreate scenes from the deceased's home, workplace, travel destinations, etc. Furthermore, the conversion unit can optimize the design of the miniature set. For example, it can suggest designs related to the hobbies and interests of the deceased. This allows for the creation of a miniature set that recreates the life story of the deceased.

[0071] The conversion unit uses the emotion estimation function to suggest a design that the user most emotionally identifies with, and can create a commemorative gift based on that design. The conversion unit, for example, uses the emotion estimation function to build a system that suggests a design that the user most emotionally identifies with. For example, it analyzes the user's facial expressions and voice and suggests a design with a high emotion score. The conversion unit can also create a custom-made commemorative gift based on the proposed design. For example, it can create a figurine that resembles the face of the deceased, or an object based on a specific episode. Furthermore, the conversion unit can customize the design according to the user's wishes. For example, it can suggest a design related to the hobbies and interests of the deceased. This allows the conversion unit to suggest a design that the user most emotionally identifies with, and create a commemorative gift based on that design.

[0072] The conversion unit can suggest custom-made interior items that allow users to feel memories of the deceased in their daily lives. For example, the conversion unit uses generative AI to build a system that suggests custom-made interior items that allow users to feel memories of the deceased in their daily lives. For example, it can create artwork based on photographs of the deceased or interior items that recreate specific scenes. The conversion unit can also suggest customizable interior items according to the user's wishes. For example, it can suggest designs related to the hobbies and interests of the deceased. Furthermore, the conversion unit can optimize the design of the interior items. For example, it can suggest designs that incorporate photographs and messages of the deceased. This makes it possible to suggest custom-made interior items that allow users to feel memories of the deceased in their daily lives.

[0073] The conversion unit can automatically generate a diary or memorial book themed around memories of the deceased based on the analysis results of the digital content. The conversion unit, for example, uses generation AI to build a system that automatically generates a diary or memorial book themed around memories of the deceased based on the analysis results of the digital content. For example, it creates a diary that combines photos and messages. The conversion unit can also suggest customizable diaries and memorial books according to the user's wishes. For example, it can suggest designs related to the hobbies and interests of the deceased. Furthermore, the conversion unit can provide the generated diary or memorial book to the user. For example, it can provide it as a printed book or a digital album. This makes it possible to automatically generate diaries and memorial books themed around memories of the deceased.

[0074] The conversion unit can create artwork themed around memories of the deceased based on the analysis results of the digital content and provide it to the user. The conversion unit, for example, uses generative AI to build a system that creates artwork themed around memories of the deceased based on the analysis results of the digital content. For example, it creates a painting based on a photograph of the deceased or an artwork that recreates a specific scene. The conversion unit can also suggest customizable artwork according to the user's wishes. For example, it can suggest a design related to the hobbies and interests of the deceased. Furthermore, the conversion unit can provide the generated artwork to the user. For example, it can provide it as a printed poster or a digital artwork. In this way, artwork themed around memories of the deceased can be created and provided to the user.

[0075] The conversion unit can provide an online community for users to share memories of the deceased and promote interaction with other users. The conversion unit, for example, builds an online community for users to share memories of the deceased. For example, users upload photos and videos and exchange comments and messages with other users. The conversion unit can also collect ratings and opinions on the shared content. For example, the conversion unit can analyze comments and ratings posted by other users and provide them as feedback. Furthermore, the conversion unit can promote events and discussions within the online community. For example, an event themed around memories of the deceased can be held to deepen interaction between users. This allows users to share memories of the deceased and promote interaction with other users.

[0076] The conversion unit can use the emotion estimation function to make suggestions for integrating memories of the deceased into daily life in a way that the user most emotionally empathizes with. For example, the conversion unit uses the emotion estimation function to build a system that makes suggestions for integrating memories of the deceased into daily life in a way that the user most emotionally empathizes with. For example, the conversion unit analyzes the user's facial expressions and voice and suggests a method with a high emotion score. The conversion unit can also create custom interior items or keepsakes based on the suggested methods. For example, it can create artwork based on a photograph of the deceased or interior items that recreate a specific scene. Furthermore, the conversion unit can customize the suggestions according to the user's wishes. For example, it can suggest designs related to the hobbies and interests of the deceased. This makes it possible to make suggestions for integrating memories of the deceased into daily life in a way that the user most emotionally empathizes with.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The digital content collection unit provides an online platform for users to share memories with the deceased and can collect feedback from other users. For example, an online platform is constructed for users to share memories with the deceased. For example, photos and videos are uploaded and comments and feedback from other users are collected. The digital content collection unit can also collect ratings and impressions of the shared content. For example, the comments and ratings posted by other users are analyzed and provided as feedback. Furthermore, the digital content collection unit can make new suggestions to the user based on the collected feedback. For example, the digital content collection unit can suggest new ways to collect memories to the user based on content that has received high ratings from other users. This allows the user to share memories with the deceased and collect feedback from other users.

[0079] The analysis unit can extract emotionally significant scenes or messages from digital content and present them to the user. For example, generative AI can be used to build a system that automatically extracts emotionally significant scenes from photos and videos. For example, it can identify scenes that express strong emotions, such as smiles or tears. The analysis unit can also extract emotionally significant messages from audio messages. For example, it can identify important messages based on the intensity and type of emotion. Furthermore, the analysis unit can present the extracted scenes and messages to the user. For example, it can present emotionally significant scenes and messages to the user to help them select memories. This allows emotionally significant scenes and messages to be presented to the user.

[0080] The conversion unit can 3D print an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content. For example, a system can be built using generative AI to design and 3D print an interactive figurine that reproduces the features of the deceased based on the analysis results of the digital content. For example, the facial and bodily features of the deceased can be reproduced. The conversion unit can also design figurine with interactive functions. For example, it can create a figurine with moving parts or audio playback functions. Furthermore, the conversion unit can suggest customizable figurine according to the user's wishes. For example, customizing the clothing and accessories of the deceased. This allows for the 3D printing of an interactive figurine that reproduces the features of the deceased.

[0081] The conversion unit can use the emotion estimation function to suggest a design that the user most emotionally identifies with and create a commemorative gift based on that design. For example, a system can be built using the emotion estimation function to suggest a design that the user most emotionally identifies with. For example, the system can analyze the user's facial expressions and voice and suggest designs with high emotion scores. The conversion unit can also create custom-made commemorative gifts based on the proposed designs. For example, it can create a figurine that resembles the face of the deceased or an object based on a specific episode. Furthermore, the conversion unit can customize the design according to the user's wishes. For example, it can suggest a design related to the hobbies and interests of the deceased. This allows the system to suggest a design that the user most emotionally identifies with and create a commemorative gift based on that design.

[0082] The conversion unit can create a miniature set that recreates the life story of the deceased based on the analysis results of the digital content. For example, a system can be built using generative AI to design and 3D print a miniature set that recreates the life story of the deceased based on the analysis results of the digital content. For example, it can recreate important scenes from the deceased's life. The conversion unit can also suggest customizable miniature sets according to the user's wishes. For example, it can recreate scenes from the deceased's home, workplace, travel destinations, etc. Furthermore, the conversion unit can optimize the design of the miniature set. For example, it can suggest designs related to the hobbies and interests of the deceased. This allows for the creation of a miniature set that recreates the life story of the deceased.

[0083] The analysis unit can analyze changes in the deceased's voice or facial expression and visualize the changes in their emotions. For example, a system can be built using generative AI to analyze changes in the deceased's voice and visualize the changes in their emotions. For example, changes in vocal tone and pitch can be analyzed and the changes in emotions displayed in a graph. The analysis unit can also analyze changes in the deceased's facial expression. For example, facial expression recognition technology can be used to analyze changes in facial expressions such as smiles and tears and visualize the changes in emotions. The analysis unit can also present the analysis results to the user. For example, graphs and charts showing the changes in the deceased's emotions can be provided to the user to deepen their understanding of their memories. This makes it possible to analyze changes in the deceased's voice and facial expression and visualize the changes in emotions.

[0084] The conversion unit can design an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. For example, a system can be built using generative AI to design an audio device that plays the voice or message of the deceased based on the analysis results of the digital content. For example, a speaker that reproduces the voice of the deceased can be created. The conversion unit can also suggest customizable audio devices according to the user's wishes. For example, a portable device that plays the message of the deceased can be created. Furthermore, the conversion unit can optimize the design of the audio device. For example, a design related to the hobbies and interests of the deceased can be suggested. This makes it possible to design an audio device that plays the voice or message of the deceased.

[0085] The analysis unit can use the emotion estimation function to collect the user's emotional reactions to the analyzed digital content and optimize the analysis results based on the collected data. For example, a system can be constructed that uses the emotion estimation function to collect the user's emotional reactions to the analyzed digital content in real time. For example, the user's facial expressions and voice can be analyzed to calculate an emotional score. The analysis unit can also optimize the analysis results based on the collected emotional reactions. For example, content with a high user emotional score can be presented preferentially. Furthermore, the analysis unit can accumulate emotional reaction data and improve the accuracy of the analysis algorithm. For example, the accuracy of the analysis results can be improved based on past emotional reaction data. In this way, the user's emotional reactions can be collected and the analysis results can be optimized.

[0086] The conversion unit can create artwork themed around memories of the deceased based on the analysis results of the digital content and provide it to the user. For example, a system can be built using generative AI to create artwork themed around memories of the deceased based on the analysis results of the digital content. For example, a painting based on a photograph of the deceased or an artwork recreating a specific scene can be created. The conversion unit can also suggest customizable artwork according to the user's wishes. For example, it can suggest a design related to the hobbies and interests of the deceased. Furthermore, the conversion unit can provide the generated artwork to the user. For example, it can provide it as a printed poster or a digital artwork. In this way, artwork themed around memories of the deceased can be created and provided to the user.

[0087] The conversion unit can use the emotion estimation function to make suggestions for integrating memories of the deceased into daily life in a way that the user most emotionally empathizes with. For example, a system can be constructed that uses the emotion estimation function to make suggestions for integrating memories of the deceased into daily life in a way that the user most emotionally empathizes with. For example, the system can analyze the user's facial expressions and voice and suggest methods with high emotion scores. The conversion unit can also create custom interior items or keepsakes based on the suggested methods. For example, it can create artwork based on photographs of the deceased or interior items that recreate specific scenes. Furthermore, the conversion unit can customize the suggestions according to the user's wishes. For example, it can suggest designs related to the hobbies and interests of the deceased. This makes it possible to make suggestions for integrating memories of the deceased into daily life in a way that the user most emotionally empathizes with.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The digital content collection unit collects digital content from users. For example, it collects photos, videos, and audio messages uploaded by users. The digital content collection unit can also store the digital content provided by users in cloud storage. For example, it can store data using cloud services such as Google Drive and Dropbox. Furthermore, the digital content collection unit can automatically classify the digital content provided by users. For example, it can store the content in categories such as photos, videos, and audio messages. Step 2: The analysis unit analyzes the collected digital content. For example, the generation AI analyzes the content of photos and videos and extracts important elements. The generation AI also converts the content of voice messages into text and extracts important messages. For example, the generation AI can identify specific people or places in photos, or specific scenes in videos. The generation AI can also analyze the content of voice messages and evaluate the intensity and type of emotions. Step 3: The converter transforms the analyzed digital content into a physical form. For example, it can use 3D printing technology to create small items or custom keepsakes. The converter can also suggest the optimal shape and design based on the user's preferences. For example, it could be a figurine of the deceased's face, a miniature replica of a specific scene, or an audio device that plays the deceased's voice.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a digital content collection unit that collects digital content from users; an analysis unit that analyzes the digital content collected by the digital content collection unit; a conversion unit that converts the digital content analyzed by the analysis unit into a physical form. A system characterized by:

2. The digital content collection unit Sentiment analysis is performed on the digital content, and important memories are automatically selected based on the intensity and type of emotion.

2. The system of claim 1.

3. The analysis unit Extracting emotionally significant scenes or messages from the digital content and presenting them to the user.

2. The system of claim 1.

4. The conversion unit Design a custom memorial that reflects the sentiment or personality of the deceased 2. The system of claim 1.

5. The digital content collection unit Using an app equipped with an emotion estimation function, the app estimates the emotions of the user in real time as they input their memories of the deceased, and makes suggestions to elicit positive emotions.

2. The system of claim 1.

6. The analysis unit Analyzing changes in the deceased's voice or facial expression to visualize emotional transitions 2. The system of claim 1.

7. The conversion unit Using the emotion estimation function, the design that the user most emotionally sympathizes with is proposed and a souvenir is created based on that.

2. The system of claim 1.

8. The conversion unit Using emotion estimation capabilities, the user is offered suggestions for integrating the memories of the deceased into daily life in a way that most resonates with them emotionally.

2. The system of claim 1.

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