System

Abstract

An object of the system according to the embodiment is to deepen emotional connections by organizing memories of the deceased and reproducing voices.SOLUTION: A system includes an upload unit, an estimation unit, an organization unit, an optimization unit, and a reproduction unit. The upload unit uploads a photograph or a video from a user. The estimation part estimates a travel destination and a favorite object from the landscape photograph uploaded by the upload part. The organization part organizes memories on the basis of the travel destination and the favorite object estimated by the estimation part. The optimization unit extracts and optimizes the voice of the deceased from the video uploaded by the upload unit. The reproduction unit reproduces the voice of the deceased based on the voice data optimized by the optimization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to organize memories of the deceased and reproduce their voices, leaving a lack of means to deepen emotional connections.

[0005] The system of the embodiment aims to organize memories of the deceased and deepen emotional connections by recreating their voices. [Means for solving the problem]

[0006] The system according to the embodiment includes an uploading unit, an estimation unit, an organizing unit, an optimization unit, and a reproduction unit. The uploading unit uploads photos and videos from the user. The estimation unit estimates travel destinations and favorite things from landscape photos uploaded by the uploading unit. The organizing unit organizes memories based on the travel destinations and favorite things estimated by the estimation unit. The optimization unit extracts and optimizes the voice of the deceased from the video uploaded by the uploading unit. The reproduction unit reproduces the voice of the deceased based on the audio data optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for organizing memories of the deceased and recreating their voice, deepening the emotional connection. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The memory organizing system according to an embodiment of the present invention analyzes photos and videos uploaded by users, organizes memories of the deceased, and deepens emotional ties. In this way, the memory organizing system can organize memories of the deceased and deepen emotional ties.

[0029] A memory organization system according to an embodiment includes an upload unit, an estimation unit, an organization unit, an optimization unit, and a reproduction unit. The upload unit uploads photos and videos from a user. For example, the user may upload photos and videos through a web interface. Photos and videos can also be uploaded using a mobile app. Photos and videos can also be uploaded using cloud storage. The estimation unit estimates travel destinations and favorite things from landscape photos uploaded by the upload unit. For example, the generation AI analyzes landscape photos using image recognition technology to estimate travel destinations. The generation AI can also estimate favorite things using a machine learning algorithm. The generation AI can also estimate travel destinations and favorite things by referencing a database. The organization unit organizes memories based on the travel destinations and favorite things estimated by the estimation unit. For example, the generation AI organizes photos chronologically. The generation AI can also organize photos by event. The generation AI can also organize photos based on emotion scores. The optimization unit extracts and optimizes the voice of the deceased from the video uploaded by the upload unit. For example, the generation AI extracts audio data from video using audio analysis technology. The generation AI can also optimize the audio data using noise reduction technology. The generation AI can also optimize the audio data using audio synthesis technology. The reproduction unit reproduces the deceased's voice based on the audio data optimized by the optimization unit. For example, the generation AI reproduces the deceased's voice using audio synthesis technology. The generation AI can also reproduce the deceased's voice using past audio data. The generation AI can also reproduce the deceased's voice using emotional expression technology. In this way, the memory organization system according to the embodiment can organize memories of the deceased and deepen emotional connections. For example, the user can reminisce about the deceased's travel destinations and favorite things. The user can also hear the deceased's voice, deepening emotional connections. This allows the user to cherish memories of the deceased and find comfort in their own memory.

[0030] The upload unit can automatically extract metadata from photos and videos and use it to organize memories. For example, when a user uploads photos or videos, the system automatically extracts the metadata and registers the date and time of shooting and the location in a database. For example, the location of the photo can be identified based on GPS information and displayed on a map. The upload unit can also extract camera setting information and record the conditions under which the photo was taken. For example, it can extract camera exposure settings and white balance information and register it in a database. The upload unit can also classify photos and videos based on the metadata and use it to organize memories. This allows for efficient organization of memories by utilizing metadata.

[0031] The upload unit can analyze the content of photos and videos in real time and automatically generate and assign relevant tags. For example, when a user uploads photos or videos, the generation AI in the upload unit analyzes the content in real time and automatically generates tags for scenery, people, events, etc. For example, a photo of the ocean is assigned a "Ocean" tag. The generation AI in the upload unit can also analyze the content of photos and videos and assign tags related to specific places or objects. For example, a photo of a specific tourist spot is tagged with the name of the tourist spot. The generation AI in the upload unit can also analyze the content of photos and videos and assign tags related to emotions. For example, a photo of a smile is tagged with a "Smile" tag. This allows for real-time tag generation, allowing users to quickly organize their memories.

[0032] The upload unit can provide an interface that allows a user to upload photos and videos using voice commands or gesture recognition. The upload unit provides, for example, an interface that allows a user to upload photos and videos using voice commands. For example, by issuing a voice command such as "start upload," the upload of photos and videos begins. The upload unit can also provide an interface that allows a user to upload photos and videos using gesture recognition. For example, by performing a specific gesture, the upload of photos and videos begins. The upload unit can also check the progress of the upload using voice commands or gesture recognition. For example, by issuing a voice command such as "check upload status," the progress of the upload is displayed. This allows a user to intuitively upload photos and videos using voice commands or gesture recognition.

[0033] The uploading unit may provide a collaboration function for sharing uploaded photos and videos with other family members and organizing memories together. The uploading unit may provide, for example, a collaboration function for sharing uploaded photos and videos with family members and organizing memories together. For example, a shared album that all family members can access may be created. The uploading unit may also provide a function for family members to edit photos and videos in real time. For example, all family members may edit the album simultaneously and add comments. The uploading unit may also provide a function for family members to add tags to photos and videos. For example, all family members may add tags related to photos and videos to organize memories. This allows all family members to share and organize memories together.

[0034] When analyzing landscape photos, the estimation unit can refer to external data such as the deceased's social media posts and blog articles to more accurately infer travel destinations and favorite things. For example, when the generation AI analyzes landscape photos, the estimation unit can refer to the deceased's social media posts and blog articles to infer travel destinations and favorite things. For example, it can identify locations that match photos posted on social media. The estimation unit can also have the generation AI refer to external data to complement the results of the landscape photo analysis. For example, it can complement the results of the landscape photo analysis based on travel destination information described in blog articles. The estimation unit can also have the generation AI refer to external data to improve the accuracy of the landscape photo analysis. For example, it can improve the accuracy of the landscape photo analysis based on tourist destination information registered in a database. This allows more accurate inferences to be made by referencing external data.

[0035] The inference unit can automatically generate a map of the places the deceased visited based on the analysis results of the landscape photos, visually displaying the travel trail. For example, the generation AI of the inference unit analyzes landscape photos and automatically generates a map of the places the deceased visited. For example, it places a pin at each travel destination and visually displays the travel trail. The inference unit can also display the travel trail in chronological order based on the analysis results of the landscape photos by the generation AI. For example, it can draw lines on the map according to the order of the trip and display the travel trail. The inference unit can also display the travel trail interactively based on the analysis results of the landscape photos by the generation AI. For example, clicking on a pin on the map will display photos and videos of that location. This visual display of the travel trail allows users to look back on memories of the deceased more specifically.

[0036] The inference unit can automatically provide historical and cultural information about places visited by the deceased based on the analysis results of the landscape photo. For example, the generation AI analyzes the landscape photo and automatically provides historical and cultural information about places visited by the deceased. For example, the history and cultural background of a specific tourist destination can be displayed. The inference unit can also provide historical and cultural information by referring to related literature and materials based on the analysis results of the landscape photo. For example, academic papers and books related to tourist destinations can be referenced. The inference unit can also provide historical and cultural information by collecting information on the Internet based on the analysis results of the landscape photo. For example, websites and blog articles related to tourist destinations can be referenced. This allows for a deeper understanding of the places visited by the deceased by providing historical and cultural information.

[0037] The inference unit can automatically recommend music and videos related to places visited by the deceased based on the analysis of landscape photos. For example, the generation AI analyzes landscape photos and automatically recommends music and videos related to places visited by the deceased. For example, it displays music and videos related to specific tourist spots. The inference unit can also search a database for related music and videos based on the analysis of landscape photos by the generation AI. For example, it can recommend music albums or documentary videos related to tourist spots. The inference unit can also collect and recommend music and videos from the Internet based on the analysis of landscape photos by the generation AI. For example, it can recommend YouTube videos or Spotify playlists related to tourist spots. This allows users to feel a richer sense of memories of the deceased by recommending music and videos.

[0038] The organizing unit can automatically detect the deceased's life events (wedding, birthday, graduation, etc.) and organize them by event. For example, the generating AI analyzes the photos and videos of the deceased and automatically detects life events such as weddings and birthdays. For example, wedding photos are grouped together in one album. The organizing unit can also detect life events and classify photos and videos by event. For example, birthday photos are grouped together in one album. The organizing unit can also detect life events and organize memories by event. For example, graduation photos are grouped together in one album. By organizing them by life event, the deceased's life can be looked back on more specifically.

[0039] The organizing unit can automatically generate and visually display the organized memories as a storyboard of the deceased's life. For example, the organizing unit automatically generates a storyboard of the deceased's life based on the memories organized by the generation AI. For example, it displays important life events in chronological order. The organizing unit can also automatically generate a storyboard using the generation AI and visually display it. For example, it can create a storyboard by combining photos and videos. The organizing unit can also automatically generate a storyboard using the generation AI and display it interactively. For example, clicking on a photo on the storyboard will play a video related to that photo. This allows the deceased's life to be visually looked back on by displaying it in storyboard format.

[0040] The organizing unit can provide an interface that allows all family members to collaboratively edit organized memories. The organizing unit, for example, provides an interface that allows all family members to collaboratively edit organized memories. For example, a shared album that all family members can access can be created. The organizing unit can also provide a function that allows family members to edit photos and videos in real time. For example, all family members can edit the album at the same time and add comments. The organizing unit can also provide a function that allows family members to tag photos and videos. For example, all family members can add tags related to photos and videos to organize memories. This allows all family members to collaboratively edit, deepening the sharing and organization of memories.

[0041] The organizing unit can combine the organized memories with the deceased's favorite music and videos to provide a multimedia album. The organizing unit, for example, automatically generates a multimedia album by combining the organized memories with the deceased's favorite music. For example, the deceased's favorite music can be played in the background of a photo slideshow. The organizing unit can also automatically generate a multimedia album by combining the organized memories with the deceased's favorite videos. For example, the organizing unit can combine a photo slideshow with the deceased's favorite videos. The organizing unit can also provide an interactive multimedia album by combining the organized memories with the deceased's favorite music and videos. For example, clicking on a photo plays a video related to that photo. This allows the memories to be experienced more richly by combining them with music and videos.

[0042] When extracting the voice of the deceased from video, the optimization unit can learn the characteristics of the deceased's voice (tone, pitch, rhythm, etc.) and optimize it to a more natural sound. For example, the optimization unit has the generation AI extract the voice of the deceased from video and learn characteristics such as tone, pitch, and rhythm. For example, it optimizes the sound based on the characteristics of the deceased's voice. The optimization unit can also have the generation AI learn the characteristics of the deceased's voice and optimize it to a more natural sound using voice synthesis technology. For example, it can reproduce the tone and pitch of the deceased's voice. The optimization unit can also have the generation AI learn the characteristics of the deceased's voice and optimize the sound using noise reduction technology. For example, it can remove background noise and convert it to clearer sound. In this way, by learning the characteristics of the deceased's voice, it can optimize it to a more natural sound.

[0043] The optimization unit analyzes the audio data of the video and can optimize not only the voice of the deceased but also background and environmental sounds. For example, the optimization unit uses a generation AI to analyze the audio data of the video and optimize not only the voice of the deceased but also background and environmental sounds. For example, it removes background noise and converts into clear audio. The optimization unit can also analyze the audio data using the generation AI and optimize background and environmental sounds using voice synthesis technology. For example, it can reproduce environmental sounds and convert them into realistic audio. The optimization unit can also analyze the audio data using noise reduction technology. For example, it can remove environmental sounds such as wind noise and traffic noise. This optimizes background and environmental sounds, providing a more realistic audio experience.

[0044] The optimization unit can combine the optimized audio with music or environmental sounds that the deceased liked to deepen the emotional connection. For example, the optimization unit can combine the optimized audio with music that the deceased liked to deepen the emotional connection. For example, the optimization unit can play the deceased's voice and their favorite music together. The optimization unit can also combine the optimized audio with environmental sounds that the deceased liked to deepen the emotional connection. For example, the optimization unit can play the deceased's favorite nature sounds in the background. The optimization unit can also combine the optimized audio with music or environmental sounds that the deceased liked to provide an interactive experience. For example, an interactive album can be created that combines the audio with music or environmental sounds. This allows the combination with music or environmental sounds to deepen the emotional connection.

[0045] The optimization unit can combine the optimized audio with messages and comments from family members to provide an interactive audio message. For example, the optimization unit can combine the optimized audio with messages and comments from family members to provide an interactive audio message. For example, the voice of the deceased and the messages from family members can be played back alternately. The optimization unit can also combine the optimized audio with comments from family members to provide an interactive experience. For example, the optimization unit can display comments from family members on the voice of the deceased in real time. The optimization unit can also create an interactive album by combining the optimized audio with messages and comments from family members. For example, an interactive album can be created that combines audio with messages and comments. This allows the audio to be combined with messages and comments from family members to provide an interactive audio message.

[0046] The reproduction unit can learn the deceased's past voice data and generate more natural voice. For example, the generation AI of the reproduction unit learns the deceased's past voice data and generates more natural voice. For example, it reproduces voice based on the characteristics of the deceased's voice. The reproduction unit can also learn the generation AI's past voice data and generate more natural voice using voice synthesis technology. For example, it can reproduce the tone and pitch of the deceased's voice. The reproduction unit can also learn the generation AI's past voice data and optimize the voice using noise reduction technology. For example, it can remove background noise and convert it into clear voice. This makes it possible to generate more natural voice by learning from past voice data.

[0047] The reproduction unit can reproduce not only what the deceased said in the reproduced audio, but also their emotions and facial expressions. For example, when the generation AI reproduces the voice of the deceased, the reproduction unit reproduces not only what was said, but also their emotions and facial expressions. For example, in scenes where the deceased was laughing, it reproduces the sound of laughter. In addition, when the generation AI reproduces the voice of the deceased, the reproduction unit can also reproduce emotions using emotional expression technology. For example, in scenes where the deceased was emotional, it reproduces an emotional voice. In addition, when the generation AI reproduces the voice of the deceased, the reproduction unit can also reproduce facial expressions using facial expression recognition technology. For example, in scenes where the deceased was smiling, it reproduces a smiling voice. This allows for the reproduction of emotions and facial expressions, thereby providing a more emotionally rich audio.

[0048] The re-creation unit can combine the reproduced audio with music or environmental sounds that the deceased liked to deepen the emotional connection. For example, the re-creation unit can combine the reproduced audio with music that the deceased liked to deepen the emotional connection. For example, the re-creation unit can play the deceased's voice together with their favorite music. The re-creation unit can also combine the reproduced audio with environmental sounds that the deceased liked to deepen the emotional connection. For example, the re-creation unit can play the deceased's favorite nature sounds in the background. The re-creation unit can also combine the reproduced audio with music or environmental sounds that the deceased liked to provide an interactive experience. For example, an interactive album can be created that combines the audio with music or environmental sounds. This allows the combination with music or environmental sounds to deepen the emotional connection.

[0049] The reproducing unit can combine the reproduced audio with messages and comments from family members to provide an interactive audio message. For example, the reproducing unit can alternately play the voice of the deceased and the messages from family members. The reproducing unit can also combine the reproduced audio with the comments from family members to provide an interactive experience. For example, the reproducing unit can display comments from family members on the voice of the deceased in real time. The reproducing unit can also create an interactive album by combining the reproduced audio with messages and comments from family members. For example, an interactive album can be created that combines audio with messages and comments. This allows the audio to be combined with messages and comments from family members to provide an interactive audio message.

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

[0051] The memory organization system may further include a suggestion unit that suggests related activities based on the hobbies and interests of the deceased. For example, if the deceased enjoyed gardening, the suggestion unit may suggest gardening events or workshops. If the deceased loved music, the suggestion unit may provide information about concerts and music festivals. Furthermore, if the deceased loved to travel, the suggestion unit may provide recommended spots and tourist information for travel destinations. By suggesting new activities based on the hobbies and interests of the deceased, the user can feel a deeper connection to their memories of the deceased.

[0052] The memory organization system can also include an art generation unit that generates artwork based on photos and videos of the deceased. For example, digital art can be generated based on photos of the deceased and provided to the user. Animations can also be created based on videos of the deceased. Furthermore, a 3D model can be generated based on photos and videos of the deceased, allowing users to experience the deceased in a new way by preserving their memories as artwork.

[0053] The memory organization system can also include a game unit that provides a puzzle game based on photos and videos of the deceased. For example, a photo of the deceased can be divided into puzzle pieces, and the user can reminisce about memories by assembling the pieces. A quiz game based on videos of the deceased can also be provided. Furthermore, a storytelling game based on photos and videos of the deceased can also be provided. This allows users to enjoy memories of the deceased through the game.

[0054] The memory organizing system can also include a recipe section that provides cooking recipes based on photos and videos of the deceased. For example, it can provide recipes for dishes that the deceased liked, allowing users to remember the deceased by recreating them. It can also provide recipes for dishes that the deceased ate while traveling. It can also create original recipes based on photos and videos of the deceased. This allows people to share memories of the deceased through cooking.

[0055] The memory organizing system may further include a crafting section that provides ideas for handmade crafts based on photos and videos of the deceased. For example, it may provide instructions on how to make an album or scrapbook using photos of the deceased. It may also provide instructions on how to make a video message using videos of the deceased. It may also provide instructions on how to make decorative items using photos and videos of the deceased. This allows memories of the deceased to be preserved in a tangible form through handmade crafts.

[0056] The memory organization system can also include a tour unit that provides a virtual tour based on photos and videos of the deceased. For example, places visited by the deceased can be recreated in virtual reality (VR), allowing users to virtually visit those places. It can also display the deceased's travel destinations on a 3D map, allowing users to explore those places. It can also provide an interactive tour guide based on photos and videos of the deceased. This allows users to relive the deceased's memories through a virtual tour.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The upload unit uploads photos and videos from the user. For example, the user can upload photos and videos through a web interface. Alternatively, the user can upload photos and videos using a mobile app. Furthermore, the user can upload photos and videos using cloud storage. Step 2: The prediction unit predicts travel destinations and favorite things from the landscape photos uploaded by the upload unit. For example, the generation AI analyzes landscape photos using image recognition technology and predicts travel destinations. The generation AI can also predict favorite things using machine learning algorithms. The generation AI can also predict travel destinations and favorite things by referring to a database. Step 3: The organizer organizes memories based on the travel destinations and favorite things predicted by the predictor. For example, the generator organizes photos based on chronological order. The generator can also organize photos by event. The generator can also organize photos based on emotion scores. Step 4: The optimization unit extracts and optimizes the voice of the deceased from the video uploaded by the uploading unit. For example, the generation AI extracts audio data from the video using audio analysis technology. The generation AI can also optimize the audio data using noise reduction technology. The generation AI can also optimize the audio data using voice synthesis technology. Step 5: The reproduction unit reproduces the voice of the deceased based on the voice data optimized by the optimization unit. For example, the generation AI reproduces the voice of the deceased using voice synthesis technology. The generation AI can also reproduce the voice of the deceased using past voice data. The generation AI can also reproduce the voice of the deceased using emotion expression technology.

[0059] (Example 2) The memory organizing system according to an embodiment of the present invention analyzes photos and videos uploaded by users, organizes memories of the deceased, and deepens emotional ties. In this way, the memory organizing system can organize memories of the deceased and deepen emotional ties.

[0060] A memory organization system according to an embodiment includes an upload unit, an estimation unit, an organization unit, an optimization unit, and a reproduction unit. The upload unit uploads photos and videos from a user. For example, the user may upload photos and videos through a web interface. Photos and videos can also be uploaded using a mobile app. Photos and videos can also be uploaded using cloud storage. The estimation unit estimates travel destinations and favorite things from landscape photos uploaded by the upload unit. For example, the generation AI analyzes landscape photos using image recognition technology to estimate travel destinations. The generation AI can also estimate favorite things using a machine learning algorithm. The generation AI can also estimate travel destinations and favorite things by referencing a database. The organization unit organizes memories based on the travel destinations and favorite things estimated by the estimation unit. For example, the generation AI organizes photos chronologically. The generation AI can also organize photos by event. The generation AI can also organize photos based on emotion scores. The optimization unit extracts and optimizes the voice of the deceased from the video uploaded by the upload unit. For example, the generation AI extracts audio data from video using audio analysis technology. The generation AI can also optimize the audio data using noise reduction technology. The generation AI can also optimize the audio data using audio synthesis technology. The reproduction unit reproduces the deceased's voice based on the audio data optimized by the optimization unit. For example, the generation AI reproduces the deceased's voice using audio synthesis technology. The generation AI can also reproduce the deceased's voice using past audio data. The generation AI can also reproduce the deceased's voice using emotional expression technology. In this way, the memory organization system according to the embodiment can organize memories of the deceased and deepen emotional connections. For example, the user can reminisce about the deceased's travel destinations and favorite things. The user can also hear the deceased's voice, deepening emotional connections. This allows the user to cherish memories of the deceased and find comfort in their own memory.

[0061] The upload unit can automatically extract metadata from photos and videos and use it to organize memories. For example, when a user uploads photos or videos, the system automatically extracts the metadata and registers the date and time of shooting and the location in a database. For example, the location of the photo can be identified based on GPS information and displayed on a map. The upload unit can also extract camera setting information and record the conditions under which the photo was taken. For example, it can extract camera exposure settings and white balance information and register it in a database. The upload unit can also classify photos and videos based on the metadata and use it to organize memories. This allows for efficient organization of memories by utilizing metadata.

[0062] The upload unit can analyze the content of photos and videos in real time and automatically generate and assign relevant tags. For example, when a user uploads photos or videos, the generation AI in the upload unit analyzes the content in real time and automatically generates tags for scenery, people, events, etc. For example, a photo of the ocean is assigned a "Ocean" tag. The generation AI in the upload unit can also analyze the content of photos and videos and assign tags related to specific places or objects. For example, a photo of a specific tourist spot is tagged with the name of the tourist spot. The generation AI in the upload unit can also analyze the content of photos and videos and assign tags related to emotions. For example, a photo of a smile is tagged with a "Smile" tag. This allows for real-time tag generation, allowing users to quickly organize their memories.

[0063] The uploading unit can use the emotion estimation function to analyze the user's emotions regarding uploaded photos and videos in real time and prioritize organizing emotionally significant photos and videos. For example, when a user uploads photos and videos, the uploading unit uses the emotion estimation function to analyze the user's facial expressions and voice and calculate an emotion score. For example, a photo of a smiling face is assigned a high emotion score. The uploading unit can also use the emotion estimation function to categorize photos and videos based on the user's emotions. For example, it prioritizes organizing moving photos and videos. The uploading unit can also use the emotion estimation function to create albums based on the user's emotions. For example, it can create an album that collects emotionally significant photos and videos. By prioritizing organizing emotionally significant photos and videos, it becomes possible to organize memories that are in line with the user's emotions.

[0064] The upload unit can provide an interface that allows a user to upload photos and videos using voice commands or gesture recognition. The upload unit provides, for example, an interface that allows a user to upload photos and videos using voice commands. For example, by issuing a voice command such as "start upload," the upload of photos and videos begins. The upload unit can also provide an interface that allows a user to upload photos and videos using gesture recognition. For example, by performing a specific gesture, the upload of photos and videos begins. The upload unit can also check the progress of the upload using voice commands or gesture recognition. For example, by issuing a voice command such as "check upload status," the progress of the upload is displayed. This allows a user to intuitively upload photos and videos using voice commands or gesture recognition.

[0065] The uploading unit may provide a collaboration function for sharing uploaded photos and videos with other family members and organizing memories together. The uploading unit may provide, for example, a collaboration function for sharing uploaded photos and videos with family members and organizing memories together. For example, a shared album that all family members can access may be created. The uploading unit may also provide a function for family members to edit photos and videos in real time. For example, all family members may edit the album simultaneously and add comments. The uploading unit may also provide a function for family members to add tags to photos and videos. For example, all family members may add tags related to photos and videos to organize memories. This allows all family members to share and organize memories together.

[0066] The uploading unit can use the emotion estimation function to analyze the emotions of all family members regarding the uploaded photos and videos, and prioritize organizing memories that are likely to evoke emotional empathy. The uploading unit, for example, can use the emotion estimation function to analyze the emotions of all family members regarding the uploaded photos and videos, and calculate an emotion score. For example, a photo in which all family members are smiling is assigned a high emotion score. The uploading unit can also use the emotion estimation function to categorize photos and videos based on the emotions of all family members. For example, photos and videos that are likely to evoke emotional empathy are prioritized for organization. The uploading unit can also use the emotion estimation function to create albums based on the emotions of all family members. For example, an album can be created that collects photos and videos that are likely to evoke emotional empathy. This makes it possible to organize memories that are in line with the emotions of all family members.

[0067] When analyzing landscape photos, the estimation unit can refer to external data such as the deceased's social media posts and blog articles to more accurately infer travel destinations and favorite things. For example, when the generation AI analyzes landscape photos, the estimation unit can refer to the deceased's social media posts and blog articles to infer travel destinations and favorite things. For example, it can identify locations that match photos posted on social media. The estimation unit can also have the generation AI refer to external data to complement the results of the landscape photo analysis. For example, it can complement the results of the landscape photo analysis based on travel destination information described in blog articles. The estimation unit can also have the generation AI refer to external data to improve the accuracy of the landscape photo analysis. For example, it can improve the accuracy of the landscape photo analysis based on tourist destination information registered in a database. This allows more accurate inferences to be made by referencing external data.

[0068] The inference unit can automatically generate a map of the places the deceased visited based on the analysis results of the landscape photos, visually displaying the travel trail. For example, the generation AI of the inference unit analyzes landscape photos and automatically generates a map of the places the deceased visited. For example, it places a pin at each travel destination and visually displays the travel trail. The inference unit can also display the travel trail in chronological order based on the analysis results of the landscape photos by the generation AI. For example, it can draw lines on the map according to the order of the trip and display the travel trail. The inference unit can also display the travel trail interactively based on the analysis results of the landscape photos by the generation AI. For example, clicking on a pin on the map will display photos and videos of that location. This visual display of the travel trail allows users to look back on memories of the deceased more specifically.

[0069] The inference unit can automatically provide historical and cultural information about places visited by the deceased based on the analysis results of the landscape photo. For example, the generation AI analyzes the landscape photo and automatically provides historical and cultural information about places visited by the deceased. For example, the history and cultural background of a specific tourist destination can be displayed. The inference unit can also provide historical and cultural information by referring to related literature and materials based on the analysis results of the landscape photo. For example, academic papers and books related to tourist destinations can be referenced. The inference unit can also provide historical and cultural information by collecting information on the Internet based on the analysis results of the landscape photo. For example, websites and blog articles related to tourist destinations can be referenced. This allows for a deeper understanding of the places visited by the deceased by providing historical and cultural information.

[0070] The inference unit can automatically recommend music and videos related to places visited by the deceased based on the analysis of landscape photos. For example, the generation AI analyzes landscape photos and automatically recommends music and videos related to places visited by the deceased. For example, it displays music and videos related to specific tourist spots. The inference unit can also search a database for related music and videos based on the analysis of landscape photos by the generation AI. For example, it can recommend music albums or documentary videos related to tourist spots. The inference unit can also collect and recommend music and videos from the Internet based on the analysis of landscape photos by the generation AI. For example, it can recommend YouTube videos or Spotify playlists related to tourist spots. This allows users to feel a richer sense of memories of the deceased by recommending music and videos.

[0071] The estimation unit can use the emotion estimation function to analyze family members' emotions toward a landscape photo and identify places that are likely to evoke emotional empathy. For example, the estimation unit uses the generation AI to analyze the landscape photo and the emotion estimation function to analyze the family members' emotions. For example, a high emotion score is assigned to a photo in which all family members are smiling. The estimation unit can also use the emotion estimation function to classify landscape photos based on family members' emotions. For example, it can identify places that are likely to evoke emotional empathy. The estimation unit can also use the emotion estimation function to identify travel destinations based on family members' emotions. For example, it can identify places with a high positive emotion score. This allows for deeper sharing of memories by identifying places that are likely to evoke empathy based on family members' emotions.

[0072] The organizing unit can automatically detect the deceased's life events (wedding, birthday, graduation, etc.) and organize them by event. For example, the generating AI analyzes the photos and videos of the deceased and automatically detects life events such as weddings and birthdays. For example, wedding photos are grouped together in one album. The organizing unit can also detect life events and classify photos and videos by event. For example, birthday photos are grouped together in one album. The organizing unit can also detect life events and organize memories by event. For example, graduation photos are grouped together in one album. By organizing them by life event, the deceased's life can be looked back on more specifically.

[0073] The organizing unit can automatically generate and visually display the organized memories as a storyboard of the deceased's life. For example, the organizing unit automatically generates a storyboard of the deceased's life based on the memories organized by the generation AI. For example, it displays important life events in chronological order. The organizing unit can also automatically generate a storyboard using the generation AI and visually display it. For example, it can create a storyboard by combining photos and videos. The organizing unit can also automatically generate a storyboard using the generation AI and display it interactively. For example, clicking on a photo on the storyboard will play a video related to that photo. This allows the deceased's life to be visually looked back on by displaying it in storyboard format.

[0074] The organizing unit can use the emotion estimation function to analyze the family's emotions toward the organized memories and prioritize displaying emotionally significant memories. For example, the organizing unit analyzes the family's emotions toward the memories organized by the generation AI and calculates an emotion score. For example, a high emotion score is assigned to a photo in which all family members are smiling. The organizing unit can also use the emotion estimation function to categorize memories based on the family's emotions. For example, emotionally significant memories are prioritized for display. The organizing unit can also use the emotion estimation function to create albums based on the family's emotions. For example, an album is created that collects emotionally significant memories. This makes it possible to organize memories in line with the family's emotions by prioritize displaying emotionally significant memories.

[0075] The organizing unit can provide an interface that allows all family members to collaboratively edit organized memories. The organizing unit, for example, provides an interface that allows all family members to collaboratively edit organized memories. For example, a shared album that all family members can access can be created. The organizing unit can also provide a function that allows family members to edit photos and videos in real time. For example, all family members can edit the album at the same time and add comments. The organizing unit can also provide a function that allows family members to tag photos and videos. For example, all family members can add tags related to photos and videos to organize memories. This allows all family members to collaboratively edit, deepening the sharing and organization of memories.

[0076] The organizing unit can combine the organized memories with the deceased's favorite music and videos to provide a multimedia album. The organizing unit, for example, automatically generates a multimedia album by combining the organized memories with the deceased's favorite music. For example, the deceased's favorite music can be played in the background of a photo slideshow. The organizing unit can also automatically generate a multimedia album by combining the organized memories with the deceased's favorite videos. For example, the organizing unit can combine a photo slideshow with the deceased's favorite videos. The organizing unit can also provide an interactive multimedia album by combining the organized memories with the deceased's favorite music and videos. For example, clicking on a photo plays a video related to that photo. This allows the memories to be experienced more richly by combining them with music and videos.

[0077] The organizing unit can use the emotion estimation function to analyze the emotions of all family members regarding the organized memories and identify memories that are likely to be emotionally relatable. The organizing unit, for example, uses the emotion estimation function to analyze the emotions of all family members regarding the organized memories and calculate an emotion score. For example, a high emotion score is assigned to a photo in which all family members are smiling. The organizing unit can also use the emotion estimation function to classify memories based on the emotions of all family members. For example, memories that are likely to be emotionally relatable are identified. The organizing unit can also use the emotion estimation function to create an album based on the emotions of all family members. For example, an album is created that collects memories that are likely to be emotionally relatable. This allows memories that are likely to be relatable to be identified based on the emotions of all family members, thereby deepening the sharing of memories.

[0078] When extracting the voice of the deceased from video, the optimization unit can learn the characteristics of the deceased's voice (tone, pitch, rhythm, etc.) and optimize it to a more natural sound. For example, the optimization unit has the generation AI extract the voice of the deceased from video and learn characteristics such as tone, pitch, and rhythm. For example, it optimizes the sound based on the characteristics of the deceased's voice. The optimization unit can also have the generation AI learn the characteristics of the deceased's voice and optimize it to a more natural sound using voice synthesis technology. For example, it can reproduce the tone and pitch of the deceased's voice. The optimization unit can also have the generation AI learn the characteristics of the deceased's voice and optimize the sound using noise reduction technology. For example, it can remove background noise and convert it to clearer sound. In this way, by learning the characteristics of the deceased's voice, it can optimize it to a more natural sound.

[0079] The optimization unit analyzes the audio data of the video and can optimize not only the voice of the deceased but also background and environmental sounds. For example, the optimization unit uses a generation AI to analyze the audio data of the video and optimize not only the voice of the deceased but also background and environmental sounds. For example, it removes background noise and converts into clear audio. The optimization unit can also analyze the audio data using the generation AI and optimize background and environmental sounds using voice synthesis technology. For example, it can reproduce environmental sounds and convert them into realistic audio. The optimization unit can also analyze the audio data using noise reduction technology. For example, it can remove environmental sounds such as wind noise and traffic noise. This optimizes background and environmental sounds, providing a more realistic audio experience.

[0080] The optimization unit can use the emotion estimation function to analyze the emotions of family members regarding the optimized audio and provide audio that is emotionally significant with priority. For example, the optimization unit analyzes the emotions of family members regarding the audio optimized by the generation AI and calculates an emotion score. For example, audio that moves all family members is assigned a high emotion score. The optimization unit can also use the emotion estimation function to classify audio based on the emotions of family members. For example, audio that is emotionally significant is provided with priority. The optimization unit can also use the emotion estimation function to determine the order in which audio is played back based on the emotions of family members. For example, audio that is emotionally significant is played first. This makes it possible to provide audio that is in line with the emotions of family members by providing audio that is emotionally significant with priority.

[0081] The optimization unit can combine the optimized audio with music or environmental sounds that the deceased liked to deepen the emotional connection. For example, the optimization unit can combine the optimized audio with music that the deceased liked to deepen the emotional connection. For example, the optimization unit can play the deceased's voice and their favorite music together. The optimization unit can also combine the optimized audio with environmental sounds that the deceased liked to deepen the emotional connection. For example, the optimization unit can play the deceased's favorite nature sounds in the background. The optimization unit can also combine the optimized audio with music or environmental sounds that the deceased liked to provide an interactive experience. For example, an interactive album can be created that combines the audio with music or environmental sounds. This allows the combination with music or environmental sounds to deepen the emotional connection.

[0082] The optimization unit can combine the optimized audio with messages and comments from family members to provide an interactive audio message. For example, the optimization unit can combine the optimized audio with messages and comments from family members to provide an interactive audio message. For example, the voice of the deceased and the messages from family members can be played back alternately. The optimization unit can also combine the optimized audio with comments from family members to provide an interactive experience. For example, the optimization unit can display comments from family members on the voice of the deceased in real time. The optimization unit can also create an interactive album by combining the optimized audio with messages and comments from family members. For example, an interactive album can be created that combines audio with messages and comments. This allows the audio to be combined with messages and comments from family members to provide an interactive audio message.

[0083] The optimization unit can use the emotion estimation function to analyze the emotions of all family members regarding the optimized audio and identify audio that is likely to be emotionally relatable. The optimization unit, for example, uses the emotion estimation function to analyze the emotions of all family members regarding the optimized audio and calculate an emotion score. For example, a high emotion score is assigned to audio that moves all family members. The optimization unit can also use the emotion estimation function to classify audio based on the emotions of all family members. For example, audio that is likely to be emotionally relatable is identified. The optimization unit can also use the emotion estimation function to determine the order in which audio is played based on the emotions of all family members. For example, audio that is likely to be emotionally relatable is played first. In this way, audio that is likely to be relatable is identified based on the emotions of all family members, thereby deepening the sharing of audio.

[0084] The reproduction unit can learn the deceased's past voice data and generate more natural voice. For example, the generation AI of the reproduction unit learns the deceased's past voice data and generates more natural voice. For example, it reproduces voice based on the characteristics of the deceased's voice. The reproduction unit can also learn the generation AI's past voice data and generate more natural voice using voice synthesis technology. For example, it can reproduce the tone and pitch of the deceased's voice. The reproduction unit can also learn the generation AI's past voice data and optimize the voice using noise reduction technology. For example, it can remove background noise and convert it into clear voice. This makes it possible to generate more natural voice by learning from past voice data.

[0085] The reproduction unit can reproduce not only what the deceased said in the reproduced audio, but also their emotions and facial expressions. For example, when the generation AI reproduces the voice of the deceased, the reproduction unit reproduces not only what was said, but also their emotions and facial expressions. For example, in scenes where the deceased was laughing, it reproduces the sound of laughter. In addition, when the generation AI reproduces the voice of the deceased, the reproduction unit can also reproduce emotions using emotional expression technology. For example, in scenes where the deceased was emotional, it reproduces an emotional voice. In addition, when the generation AI reproduces the voice of the deceased, the reproduction unit can also reproduce facial expressions using facial expression recognition technology. For example, in scenes where the deceased was smiling, it reproduces a smiling voice. This allows for the reproduction of emotions and facial expressions, thereby providing a more emotionally rich audio.

[0086] The reproduction unit can use the emotion estimation function to analyze the emotions of the family members regarding the reproduced audio and provide audio that is emotionally significant with priority. For example, the reproduction unit analyzes the emotions of the family members regarding the audio reproduced by the generation AI and calculates an emotion score. For example, audio that moves all family members is assigned a high emotion score. The reproduction unit can also use the emotion estimation function to classify audio based on the emotions of the family members. For example, it can provide audio that is emotionally significant with priority. The reproduction unit can also use the emotion estimation function to determine the order in which audio is played back based on the emotions of the family members. For example, it can play audio that is emotionally significant first. This makes it possible to provide audio that is in line with the emotions of the family members by providing audio that is emotionally significant with priority.

[0087] The re-creation unit can combine the reproduced audio with music or environmental sounds that the deceased liked to deepen the emotional connection. For example, the re-creation unit can combine the reproduced audio with music that the deceased liked to deepen the emotional connection. For example, the re-creation unit can play the deceased's voice together with their favorite music. The re-creation unit can also combine the reproduced audio with environmental sounds that the deceased liked to deepen the emotional connection. For example, the re-creation unit can play the deceased's favorite nature sounds in the background. The re-creation unit can also combine the reproduced audio with music or environmental sounds that the deceased liked to provide an interactive experience. For example, an interactive album can be created that combines the audio with music or environmental sounds. This allows the combination with music or environmental sounds to deepen the emotional connection.

[0088] The reproducing unit can combine the reproduced audio with messages and comments from family members to provide an interactive audio message. For example, the reproducing unit can alternately play the voice of the deceased and the messages from family members. The reproducing unit can also combine the reproduced audio with the comments from family members to provide an interactive experience. For example, the reproducing unit can display comments from family members on the voice of the deceased in real time. The reproducing unit can also create an interactive album by combining the reproduced audio with messages and comments from family members. For example, an interactive album can be created that combines audio with messages and comments. This allows the audio to be combined with messages and comments from family members to provide an interactive audio message.

[0089] The reproduction unit can use the emotion estimation function to analyze the emotions of all family members regarding the reproduced audio and identify audio that is likely to be emotionally relatable. The reproduction unit, for example, uses the emotion estimation function to analyze the emotions of all family members regarding the reproduced audio and calculate an emotion score. For example, a high emotion score is assigned to audio that moves all family members. The reproduction unit can also use the emotion estimation function to classify audio based on the emotions of all family members. For example, audio that is likely to be emotionally relatable is identified. The reproduction unit can also use the emotion estimation function to determine the order in which audio is played based on the emotions of all family members. For example, audio that is likely to be emotionally relatable is played first. In this way, audio that is likely to be relatable is identified based on the emotions of all family members, thereby deepening the sharing of audio.

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

[0091] The memory organization system may further include a suggestion unit that suggests related activities based on the hobbies and interests of the deceased. For example, if the deceased enjoyed gardening, the suggestion unit may suggest gardening events or workshops. If the deceased loved music, the suggestion unit may provide information about concerts and music festivals. Furthermore, if the deceased loved to travel, the suggestion unit may provide recommended spots and tourist information for travel destinations. By suggesting new activities based on the hobbies and interests of the deceased, the user can feel a deeper connection to their memories of the deceased.

[0092] The memory organization system can also include an art generation unit that generates artwork based on photos and videos of the deceased. For example, digital art can be generated based on photos of the deceased and provided to the user. Animations can also be created based on videos of the deceased. Furthermore, a 3D model can be generated based on photos and videos of the deceased, allowing users to experience the deceased in a new way by preserving their memories as artwork.

[0093] The memory organization system can also include a game unit that provides a puzzle game based on photos and videos of the deceased. For example, a photo of the deceased can be divided into puzzle pieces, and the user can reminisce about memories by assembling the pieces. A quiz game based on videos of the deceased can also be provided. Furthermore, a storytelling game based on photos and videos of the deceased can also be provided. This allows users to enjoy memories of the deceased through the game.

[0094] The memory organizing system can also include a recipe section that provides cooking recipes based on photos and videos of the deceased. For example, it can provide recipes for dishes that the deceased liked, allowing users to remember the deceased by recreating them. It can also provide recipes for dishes that the deceased ate while traveling. It can also create original recipes based on photos and videos of the deceased. This allows people to share memories of the deceased through cooking.

[0095] The memory organizing system may further include a crafting section that provides ideas for handmade crafts based on photos and videos of the deceased. For example, it may provide instructions on how to make an album or scrapbook using photos of the deceased. It may also provide instructions on how to make a video message using videos of the deceased. It may also provide instructions on how to make decorative items using photos and videos of the deceased. This allows memories of the deceased to be preserved in a tangible form through handmade crafts.

[0096] The memory organization system can also include a tour unit that provides a virtual tour based on photos and videos of the deceased. For example, places visited by the deceased can be recreated in virtual reality (VR), allowing users to virtually visit those places. It can also display the deceased's travel destinations on a 3D map, allowing users to explore those places. It can also provide an interactive tour guide based on photos and videos of the deceased. This allows users to relive the deceased's memories through a virtual tour.

[0097] The memory organizing system may further include a message unit that generates an emotional message based on a photograph or video of the deceased. For example, an emotional message may be generated based on a photograph of the deceased and provided to the user. An emotional video message may also be generated based on a video of the deceased. Furthermore, an emotional letter may be generated based on a photograph or video of the deceased. This allows memories of the deceased to be shared through emotional messages.

[0098] The memory organizing system may further include a music unit that generates emotional music based on photos and videos of the deceased. For example, emotional music may be generated based on photos of the deceased and provided to the user. An emotional video message may also be generated based on videos of the deceased. Furthermore, an emotional letter may be generated based on photos and videos of the deceased. This allows memories of the deceased to be shared through emotional music.

[0099] The memory organization system may further include a poetry unit that generates emotional poetry based on photos and videos of the deceased. For example, an emotional poem may be generated based on a photo of the deceased and provided to the user. An emotional video message may also be generated based on a video of the deceased. Furthermore, an emotional letter may be generated based on the photos and videos of the deceased. This allows memories of the deceased to be shared through emotional poetry.

[0100] The memory organization system may further include a story unit that generates an emotional story based on photos and videos of the deceased. For example, an emotional story may be generated based on photos of the deceased and provided to the user. An emotional video message may also be generated based on videos of the deceased. Furthermore, an emotional letter may be generated based on photos and videos of the deceased. This allows memories of the deceased to be shared through an emotional story.

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

[0102] Step 1: The upload unit uploads photos and videos from the user. For example, the user can upload photos and videos through a web interface. Alternatively, the user can upload photos and videos using a mobile app. Furthermore, the user can upload photos and videos using cloud storage. Step 2: The prediction unit predicts travel destinations and favorite things from the landscape photos uploaded by the upload unit. For example, the generation AI analyzes landscape photos using image recognition technology and predicts travel destinations. The generation AI can also predict favorite things using machine learning algorithms. The generation AI can also predict travel destinations and favorite things by referring to a database. Step 3: The organizer organizes memories based on the travel destinations and favorite things predicted by the predictor. For example, the generator organizes photos based on chronological order. The generator can also organize photos by event. The generator can also organize photos based on emotion scores. Step 4: The optimization unit extracts and optimizes the voice of the deceased from the video uploaded by the uploading unit. For example, the generation AI extracts audio data from the video using audio analysis technology. The generation AI can also optimize the audio data using noise reduction technology. The generation AI can also optimize the audio data using voice synthesis technology. Step 5: The reproduction unit reproduces the voice of the deceased based on the voice data optimized by the optimization unit. For example, the generation AI reproduces the voice of the deceased using voice synthesis technology. The generation AI can also reproduce the voice of the deceased using past voice data. The generation AI can also reproduce the voice of the deceased using emotion expression technology.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0131] 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.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an uploading section for uploading photos and videos from users; an estimation unit that estimates travel destinations and favorite things from landscape photos uploaded by the upload unit; an organizing unit that organizes memories based on the travel destinations and favorite things estimated by the estimation unit; an optimization unit that extracts and optimizes the voice of the deceased from the video uploaded by the upload unit; a reproduction unit that reproduces the voice of the deceased based on the voice data optimized by the optimization unit. A system characterized by:

2. The upload unit Analyzes user emotions about uploaded photos and videos in real time and prioritizes emotionally significant photos and videos.

2. The system of claim 1.

3. The estimation unit When analyzing landscape photos, the system references external data such as the deceased's social media posts and blog posts to more accurately predict travel destinations and favorite things.

2. The system of claim 1.

4. The organizing unit Automatically detect the deceased's life events (weddings, birthdays, graduations, etc.) and organize them by event 2. The system of claim 1.

5. The optimization unit When extracting the voice of the deceased from video, the system learns the characteristics of the deceased's voice (tone, pitch, rhythm, etc.) and optimizes it to produce a more natural sound.

2. The system of claim 1.

6. The reproducing section Learn from past voice data of the deceased to generate more natural voices 2. The system of claim 1.

7. The estimation unit Estimating the deceased's feelings toward landscape photographs and identifying emotionally significant places 2. The system of claim 1.

8. The reproducing section Analyzes family members' emotions toward the reproduced audio and prioritizes the emotionally significant audio 2. The system of claim 1.

Images