System

Abstract

An object of the system according to the embodiment is to provide means for reproducing the figure and voice of the deceased and remembering the deceased.SOLUTION: A system according to an embodiment includes a data collection unit, a data analysis unit, a model generation unit, and a moving image generation unit. The data collection part collects photographs, voice data, and text data of the deceased. The data analysis unit analyzes the data collected by the data collection unit. The model generation unit generates a model for reproducing the figure and voice of the deceased based on the data analyzed by the data analysis unit. The moving image generation unit generates a moving image that reproduces the figure and voice of the deceased based on the model generated by the model generation 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 made it difficult to recreate the appearance and voice of the deceased, limiting the means by which people can remember them.

[0005] The system according to the embodiment aims to provide a means for remembering the deceased by recreating their appearance and voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a model generation unit, and a video generation unit. The data collection unit collects photographs, audio data, and text data of the deceased. The data analysis unit analyzes the data collected by the data collection unit. The model generation unit generates a model for recreating the appearance and voice of the deceased based on the data analyzed by the data analysis unit. The video generation unit generates a video that recreates the appearance and voice of the deceased based on the model generated by the model generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the appearance and voice of the deceased, providing a means of remembering the deceased. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The deceased person video creation system according to an embodiment of the present invention is a system that collects photos, audio data, and text data of the deceased, analyzes this data using a generation AI, and generates a video that recreates the appearance and voice of the deceased. This allows the deceased person video creation system to recreate the appearance and voice of the deceased, allowing family and friends to preserve precious memories in a tangible form.

[0029] A deceased person video creation system according to an embodiment includes a data collection unit, a data analysis unit, a model generation unit, and a video generation unit. The data collection unit collects photographs, audio data, and text data of the deceased. For example, it scans photographs from the deceased's photo album and digitizes audio data containing the deceased's voice. It also collects text data, such as letters and diaries written by the deceased. The data analysis unit analyzes the collected data. For example, it uses image analysis technology to extract facial features of the deceased from the photo data. It also uses audio analysis technology to analyze vocal features of the deceased from the audio data. It also uses text analysis technology to learn the deceased's speaking style and vocabulary from the text data. The model generation unit generates a model for recreating the appearance and voice of the deceased based on the analyzed data. For example, it uses a machine learning algorithm to generate models of the deceased's face and voice. The video generation unit generates a video that recreates the appearance and voice of the deceased based on the generated model. For example, the generation AI creates a video that recreates what the deceased said during their lifetime or a video that conveys a message to family and friends. As a result, the deceased person video creation system according to the embodiment can recreate the appearance and voice of the deceased person, allowing family and friends to preserve precious memories in a tangible form.

[0030] The data collection unit can collect posts and comments from the deceased's social media accounts and input them into the generation AI. For example, the data collection unit automatically collects posts and comments from the deceased's social media accounts and inputs them into the generation AI. For example, it analyzes posts on Facebook and Twitter to reflect the deceased's hobbies and interests. The data collection unit also collects photos and videos from the deceased's social media accounts and inputs them into the generation AI. For example, it analyzes images posted on Instagram and YouTube videos to recreate the deceased's appearance and behavior. The data collection unit also collects interactions with friends and family from the deceased's social media accounts and inputs them into the generation AI. For example, it analyzes conversations in messengers and comment sections to reflect the deceased's relationships and communication style. This makes it possible to recreate a more detailed image of the deceased based on information from the deceased's social media accounts.

[0031] The data collection unit can analyze the deceased's video call history, extract facial and gesture features, and input them into the generation AI. For example, the data collection unit analyzes the deceased's video call history and extracts facial and gesture features. For example, it analyzes Zoom or Skype call data to reproduce the deceased's natural facial expressions and movements. The data collection unit also analyzes audio data during video calls and extracts the deceased's tone of voice and speaking characteristics. For example, it reflects laughter and emotional remarks during the call. The data collection unit also analyzes background and environmental sounds during the video call and inputs them into the generation AI. For example, it reproduces the background and ambient sounds of the room during the call to generate more realistic videos. This makes it possible to extract facial and gesture features from the deceased's video call history and generate more realistic videos.

[0032] The data collection unit can collect data related to the hobbies and interests of the deceased and input it into the generation AI. For example, the data collection unit can collect the deceased's reading history and input it into the generation AI. For example, it can analyze the e-book library and library borrowing history to reflect the deceased's interests. The data collection unit can also collect the deceased's music playlists and input it into the generation AI. For example, it can analyze Spotify or Apple Music playlists to reflect the deceased's musical preferences. The data collection unit can also collect data related to the deceased's hobbies and input it into the generation AI. For example, it can analyze sports results and photos of artwork to reflect the deceased's hobbies. This makes it possible to generate a video that reflects the deceased's hobbies and interests.

[0033] The data collection unit can record interviews with the deceased's family and friends and input the audio data into the generation AI. For example, the data collection unit can record interviews with the deceased's family and friends and input the audio data into the generation AI. For example, it can record reminiscences and episodes to reflect the deceased's personality. The data collection unit can also analyze the interview audio data and extract characteristics and episodes of the deceased. For example, it can reflect specific events and emotions. The data collection unit can also convert the interview audio data into text and input it into the generation AI. For example, it can analyze the interview content as text data to reflect the deceased's speaking style and choice of words. This makes it possible to recreate a more detailed portrait of the deceased based on interviews with the deceased's family and friends.

[0034] The data analysis unit can analyze changes in the age and health condition of the deceased from the photo data and input the results into the generation AI. For example, the data analysis unit can analyze the photo data of the deceased in chronological order to extract changes in age and health condition. For example, it can analyze photos from a young age to later years to reflect changes in age. The data analysis unit can also analyze changes in health condition from the photo data and input the results into the generation AI. For example, it can reflect weight gain or loss and signs of illness. The data analysis unit can also analyze the photo data of the deceased and quantify changes in age and health condition. For example, it can analyze changes in facial wrinkles and hair color and input the results into the generation AI. This makes it possible to generate a video that reflects changes in the age and health condition of the deceased.

[0035] The data analysis unit can analyze the deceased's handwriting and input the characteristics of the handwriting and writing style into the generation AI. For example, the data analysis unit scans the deceased's handwriting and analyzes the characteristics of the handwriting and writing style. For example, it analyzes the characters in letters or memos and reflects the deceased's handwriting. The data analysis unit also quantifies the characteristics of the handwriting and inputs them into the generation AI. For example, it analyzes the size, angle, and changes in writing pressure of the characters and reflects them in the generation AI. The data analysis unit also analyzes the deceased's handwriting and inputs its characteristics into the generation AI. For example, it reflects the writing style of specific characters or phrases. This makes it possible to generate a video that reflects the characteristics of the deceased's handwriting.

[0036] The data analysis unit can analyze the characteristics of the deceased's walking and movements and input them into the generation AI. The data analysis unit, for example, analyzes video data of the deceased and extracts characteristics of their walking and movements. For example, it analyzes the way they walk and their hand movements and reflects the deceased's movements. The data analysis unit also quantifies the movement characteristics and inputs them into the generation AI. For example, it analyzes the walking speed and rhythm of their movements and reflects them in the generation AI. The data analysis unit also analyzes the characteristics of the deceased's walking and movements and inputs the data into the generation AI. For example, it reflects specific movements and gestures. This makes it possible to generate a video that reflects the characteristics of the deceased's walking and movements.

[0037] The video generation unit allows the generation AI to automatically add background sounds and environmental sounds to the video of the deceased, enabling a more realistic reproduction. For example, the video generation unit allows the generation AI to automatically add background sounds to the video of the deceased. For example, natural sounds and city sounds are added to enhance the realism of the video. In addition, the video generation unit allows the generation AI to automatically adjust the environmental sounds according to the scene in the video. For example, indoor sounds are added to indoor scenes, and wind sounds are added to outdoor scenes. In addition, the video generation unit allows the generation AI to automatically add sound effects to the video of the deceased. For example, sound effects are added to specific scenes to enhance the realism of the video. In this way, adding background sounds and environmental sounds to the video of the deceased enables a more realistic reproduction.

[0038] The video generation unit uses the generation AI to automatically adjust the lighting and camera angle for videos of the deceased, resulting in a professional finish. For example, the video generation unit uses the generation AI to automatically adjust the lighting for videos of the deceased. For example, it adjusts the light intensity and color according to the scene to improve the quality of the video. The video generation unit also uses the generation AI to automatically adjust the camera angle for the video. For example, it selects the optimal angle to enhance visual appeal. The video generation unit also uses the generation AI to automatically adjust the lighting and camera angle for videos of the deceased. For example, it sets the optimal settings for each scene to achieve a professional finish. This allows the lighting and camera angle to be adjusted for videos of the deceased to achieve a professional finish.

[0039] The video generation unit allows the generation AI to automatically add subtitles to videos of the deceased, complementing visual information. For example, the video generation unit allows the generation AI to automatically add subtitles to videos of the deceased. For example, what the deceased says is converted into text and displayed as subtitles. The video generation unit also allows the generation AI to automatically adjust the style of the subtitles depending on the scene in the video. For example, it uses larger letters in important scenes to emphasize visual information. The video generation unit also allows the generation AI to automatically add multilingual subtitles to videos of the deceased. For example, it displays subtitles in multiple languages, such as English and Spanish. This makes it possible to complement visual information by adding subtitles to videos of the deceased.

[0040] The video generation unit allows the generation AI to automatically add animations and graphics to videos of the deceased, enhancing their visual appeal. For example, the video generation unit allows the generation AI to automatically add animations to videos of the deceased. For example, it displays animations related to what the deceased is saying. The video generation unit also allows the generation AI to automatically add graphics according to the scenes in the video. For example, it displays graphics related to the hobbies and interests of the deceased. The video generation unit also allows the generation AI to automatically add a combination of animations and graphics to videos of the deceased. For example, it applies the optimal visual effects for each scene. This allows the visual appeal to be enhanced by adding animations and graphics to videos of the deceased.

[0041] The video generation unit uses the generation AI to automatically adjust the tempo and key of the music for videos of the deceased, enhancing the emotional effect. For example, the video generation unit uses the generation AI to automatically adjust the tempo of the music for videos of the deceased. For example, the tempo may be slowed down in moving scenes to increase tension. The video generation unit also uses the generation AI to automatically adjust the key of the music depending on the scene in the video. For example, a lower key may be used in sad scenes to enhance the emotional effect. The video generation unit also uses the generation AI to automatically adjust the tempo and key of the music for videos of the deceased. For example, optimal music settings are set for each scene to maximize the emotional effect. This allows the emotional effect to be enhanced by adjusting the tempo and key of the music for videos of the deceased.

[0042] The video generation unit allows the generation AI to automatically apply color tones and filters to videos of the deceased, thereby ensuring visual consistency. For example, the video generation unit allows the generation AI to automatically adjust color tones to videos of the deceased. For example, the overall color tone is unified to a warm tone to ensure visual consistency. The video generation unit also allows the generation AI to automatically apply filters depending on the scene in the video. For example, a sepia filter can be used in certain scenes to create a nostalgic feeling. The video generation unit also allows the generation AI to automatically apply a combination of color tones and filters to videos of the deceased. For example, optimal settings are set for each scene to ensure visual consistency. This allows the generation AI to automatically apply color tones and filters to videos of the deceased, thereby ensuring visual consistency.

[0043] The video generation unit allows the generation AI to automatically add 3D effects to videos of the deceased, enhancing the visual impact. For example, the video generation unit automatically adds 3D effects to videos of the deceased. For example, the deceased's appearance is recreated as a 3D model and incorporated into the video. The video generation unit also automatically adjusts the 3D effects according to the scenes in the video. For example, 3D objects are added in specific scenes to enhance the visual impact. The video generation unit also allows the generation AI to automatically add a combination of 3D effects to videos of the deceased. For example, the optimal 3D effect is applied to each scene to enhance visual appeal. In this way, adding 3D effects to videos of the deceased can enhance the visual impact.

[0044] The video generation unit allows the generation AI to automatically add narration to videos of the deceased, enhancing storytelling. For example, the video generation unit allows the generation AI to automatically add narration to videos of the deceased. For example, narration recounting the life of the deceased can be added to enhance storytelling. The video generation unit also allows the generation AI to automatically adjust the content of the narration according to the scene in the video. For example, narration recounting a moving episode can be added to a specific scene. The video generation unit also allows the generation AI to automatically combine and add narration to videos of the deceased. For example, the optimal narration can be applied to each scene to enhance storytelling. In this way, storytelling can be enhanced by adding narration to videos of the deceased.

[0045] When saving a video of the deceased, the generation AI can automatically select the optimal file format and resolution. For example, when saving a video of the deceased, the generation AI automatically selects the optimal file format. For example, it selects a format such as MP4 or AVI depending on the viewing device. In addition, the video generation unit automatically selects the optimal resolution depending on the content of the video. For example, it selects high resolution for detailed scenes and low resolution for general scenes. In addition, when saving a video of the deceased, the generation AI automatically selects a combination of file format and resolution. For example, it sets the optimal settings for the viewing environment and saves the video. As a result, by selecting the optimal file format and resolution when saving a video of the deceased, it is possible to optimally save the video according to the viewing environment.

[0046] When saving videos of the deceased, the generation AI automatically adds metadata, improving searchability. When saving videos of the deceased, the generation AI automatically adds metadata. For example, information about the content and scenes of the video is saved as metadata. The video generation unit also automatically adjusts the metadata according to the scenes in the video. For example, keywords related to specific scenes are added to improve searchability. When saving videos of the deceased, the generation AI automatically combines and adds metadata. For example, optimal metadata is set for each scene to improve searchability. In this way, adding metadata to videos of the deceased can improve searchability.

[0047] When saving a video of the deceased, the generation AI automatically creates a backup, ensuring data safety. For example, when saving a video of the deceased, the generation AI automatically creates a backup. For example, the backup is saved in cloud storage to ensure data safety. The video generation unit also automatically adjusts the frequency of backups depending on the content of the video. For example, videos with many important scenes will be backed up more frequently. When saving a video of the deceased, the generation AI automatically creates multiple backups and saves them in different locations. For example, backups are saved simultaneously in local storage and cloud storage. This makes it possible to ensure data safety by creating backups when saving a video of the deceased.

[0048] When saving a video of the deceased, the generation AI can automatically apply privacy settings and limit the scope of sharing. For example, when saving a video of the deceased, the generation AI automatically applies privacy settings. For example, it can set the video so that only specific family and friends can access it. The video generation unit also automatically adjusts the privacy settings depending on the content of the video. For example, strict privacy settings are applied to videos with many personal scenes. The video generation unit also automatically applies a combination of privacy settings when saving a video of the deceased. For example, different privacy settings are set for each scene to limit the scope of sharing. In this way, by applying privacy settings when saving a video of the deceased, the scope of sharing can be limited.

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

[0050] The data collection unit can collect data related to the deceased's hobbies and interests and input it into the generation AI. For example, it can collect the deceased's reading history and input it into the generation AI. For example, it can analyze the deceased's e-book library and library borrowing history to reflect the deceased's interests. The data collection unit can also collect the deceased's music playlists and input it into the generation AI. For example, it can analyze Spotify or Apple Music playlists to reflect the deceased's musical preferences. The data collection unit can also collect data related to the deceased's hobbies and input it into the generation AI. For example, it can analyze sports results and photos of artwork to reflect the deceased's hobbies. This makes it possible to generate videos that reflect the deceased's hobbies and interests.

[0051] The data collection unit can record interviews with the deceased's family and friends and input the audio data into the generation AI. For example, interviews with the deceased's family and friends can be recorded and the audio data can be input into the generation AI. For example, reminiscences and episodes can be recorded to reflect the deceased's personality. The data collection unit can also analyze the interview audio data and extract characteristics and episodes of the deceased. For example, specific events and emotions can be reflected. The data collection unit can also convert the interview audio data into text and input it into the generation AI. For example, the interview content can be analyzed as text data to reflect the deceased's speaking style and choice of words. This makes it possible to recreate a more detailed portrait of the deceased based on interviews with the deceased's family and friends.

[0052] The data analysis unit can analyze changes in the age and health condition of the deceased from the photo data and input the results into the generation AI. For example, it can analyze the photo data of the deceased over time to extract changes in age and health condition. For example, it can analyze photos from a young age to later years to reflect changes in age. The data analysis unit can also analyze changes in health condition from the photo data and input the results into the generation AI. For example, it can reflect weight gain or loss and signs of illness. The data analysis unit can also analyze the photo data of the deceased and quantify changes in age and health condition. For example, it can analyze changes in facial wrinkles and hair color and input the results into the generation AI. This makes it possible to generate a video that reflects changes in the age and health condition of the deceased.

[0053] The data analysis unit can analyze the deceased's handwriting and input the characteristics of the handwriting and writing style into the generation AI. For example, it can scan the deceased's handwriting and analyze the characteristics of the handwriting and writing style. For example, it can analyze the characters in letters or memos and reflect the deceased's handwriting. The data analysis unit also quantifies the characteristics of the handwriting and inputs them into the generation AI. For example, it can analyze the size, angle, and changes in writing pressure of the characters and reflect these in the generation AI. The data analysis unit also analyzes the deceased's handwriting and inputs these characteristics into the generation AI. For example, it can reflect the writing style of specific characters or phrases. This makes it possible to generate a video that reflects the characteristics of the deceased's handwriting.

[0054] The video generation unit uses the generation AI to automatically add background sounds and environmental sounds to videos of the deceased, allowing for more realistic reproductions. For example, the generation AI automatically adds background sounds to videos of the deceased. For example, natural or city sounds can be added to enhance the realism of the video. The video generation unit also uses the generation AI to automatically adjust environmental sounds according to the video scene. For example, indoor sounds can be added to indoor scenes, and wind sounds can be added to outdoor scenes. The video generation unit also uses the generation AI to automatically add sound effects to videos of the deceased. For example, sound effects can be added to specific scenes to enhance the realism of the video. This allows for more realistic reproductions by adding background sounds and environmental sounds to videos of the deceased.

[0055] The video generation unit uses the generation AI to automatically adjust the lighting and camera angle for videos of the deceased, resulting in a professional finish. For example, the generation AI automatically adjusts the lighting for videos of the deceased. For example, it adjusts the light intensity and color according to the scene, improving the quality of the video. The video generation unit also automatically adjusts the camera angle for the video. For example, it selects the optimal angle to enhance visual appeal. The video generation unit also uses the generation AI to automatically adjust a combination of lighting and camera angle for videos of the deceased. For example, it sets the optimal settings for each scene to achieve a professional finish. This allows the lighting and camera angle to be adjusted for videos of the deceased, resulting in a professional finish.

[0056] The video generation unit allows the generation AI to automatically add subtitles to videos of the deceased, complementing visual information. For example, the generation AI automatically adds subtitles to videos of the deceased. For example, what the deceased says is converted into text and displayed as subtitles. The video generation unit also automatically adjusts the style of the subtitles according to the scenes in the video. For example, it uses larger letters in important scenes to emphasize visual information. The video generation unit also allows the generation AI to automatically add multilingual subtitles to videos of the deceased. For example, it displays subtitles in multiple languages, such as English and Spanish. This makes it possible to complement visual information by adding subtitles to videos of the deceased.

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

[0058] Step 1: The data collection department collects photographs, audio data, and text data of the deceased. For example, they scan photos from the deceased's photo albums and digitize audio recordings of the deceased's voice. They also collect text data such as letters and diaries written by the deceased. Step 2: The data analysis unit analyzes the collected data. For example, image analysis technology is used to extract the facial features of the deceased from the photo data. Voice analysis technology is used to analyze the voice characteristics of the deceased from the audio data. Furthermore, text analysis technology is used to learn the speaking style and vocabulary of the deceased from the text data. Step 3: The model generation unit generates a model to recreate the appearance and voice of the deceased based on the analyzed data. For example, it generates a model of the face and voice of the deceased using a machine learning algorithm. Step 4: The video generator generates a video that recreates the appearance and voice of the deceased based on the generated model. For example, the generator AI can create a video that recreates what the deceased said while they were alive, or a video that conveys a message to family and friends.

[0059] (Example 2) The deceased person video creation system according to an embodiment of the present invention is a system that collects photos, audio data, and text data of the deceased, analyzes this data using a generation AI, and generates a video that recreates the appearance and voice of the deceased. This allows the deceased person video creation system to recreate the appearance and voice of the deceased, allowing family and friends to preserve precious memories in a tangible form.

[0060] A deceased person video creation system according to an embodiment includes a data collection unit, a data analysis unit, a model generation unit, and a video generation unit. The data collection unit collects photographs, audio data, and text data of the deceased. For example, it scans photographs from the deceased's photo album and digitizes audio data containing the deceased's voice. It also collects text data, such as letters and diaries written by the deceased. The data analysis unit analyzes the collected data. For example, it uses image analysis technology to extract facial features of the deceased from the photo data. It also uses audio analysis technology to analyze vocal features of the deceased from the audio data. It also uses text analysis technology to learn the deceased's speaking style and vocabulary from the text data. The model generation unit generates a model for recreating the appearance and voice of the deceased based on the analyzed data. For example, it uses a machine learning algorithm to generate models of the deceased's face and voice. The video generation unit generates a video that recreates the appearance and voice of the deceased based on the generated model. For example, the generation AI creates a video that recreates what the deceased said during their lifetime or a video that conveys a message to family and friends. As a result, the deceased person video creation system according to the embodiment can recreate the appearance and voice of the deceased person, allowing family and friends to preserve precious memories in a tangible form.

[0061] The data collection unit can collect posts and comments from the deceased's social media accounts and input them into the generation AI. For example, the data collection unit automatically collects posts and comments from the deceased's social media accounts and inputs them into the generation AI. For example, it analyzes posts on Facebook and Twitter to reflect the deceased's hobbies and interests. The data collection unit also collects photos and videos from the deceased's social media accounts and inputs them into the generation AI. For example, it analyzes images posted on Instagram and YouTube videos to recreate the deceased's appearance and behavior. The data collection unit also collects interactions with friends and family from the deceased's social media accounts and inputs them into the generation AI. For example, it analyzes conversations in messengers and comment sections to reflect the deceased's relationships and communication style. This makes it possible to recreate a more detailed image of the deceased based on information from the deceased's social media accounts.

[0062] The data collection unit can analyze the deceased's video call history, extract facial and gesture features, and input them into the generation AI. For example, the data collection unit analyzes the deceased's video call history and extracts facial and gesture features. For example, it analyzes Zoom or Skype call data to reproduce the deceased's natural facial expressions and movements. The data collection unit also analyzes audio data during video calls and extracts the deceased's tone of voice and speaking characteristics. For example, it reflects laughter and emotional remarks during the call. The data collection unit also analyzes background and environmental sounds during the video call and inputs them into the generation AI. For example, it reproduces the background and ambient sounds of the room during the call to generate more realistic videos. This makes it possible to extract facial and gesture features from the deceased's video call history and generate more realistic videos.

[0063] The data collection unit can use the emotion estimation function to analyze changes in emotions from photographs and audio data of the deceased and input the results into the generation AI. For example, the data collection unit analyzes photographic data of the deceased and estimates changes in emotions from facial expressions. For example, it analyzes photos of smiling and crying and reflects changes in emotions. The data collection unit also analyzes audio data of the deceased and estimates changes in emotions from the tone of voice and speaking style. For example, it reflects emotions such as joy and sadness. The data collection unit also uses the emotion estimation function to analyze changes in emotions from video data of the deceased and input the results into the generation AI. For example, it reflects changes in facial expressions and voice in the video. This makes it possible to generate videos that reflect changes in the deceased's emotions.

[0064] The data collection unit can collect data related to the hobbies and interests of the deceased and input it into the generation AI. For example, the data collection unit can collect the deceased's reading history and input it into the generation AI. For example, it can analyze the e-book library and library borrowing history to reflect the deceased's interests. The data collection unit can also collect the deceased's music playlists and input it into the generation AI. For example, it can analyze Spotify or Apple Music playlists to reflect the deceased's musical preferences. The data collection unit can also collect data related to the deceased's hobbies and input it into the generation AI. For example, it can analyze sports results and photos of artwork to reflect the deceased's hobbies. This makes it possible to generate a video that reflects the deceased's hobbies and interests.

[0065] The data collection unit can record interviews with the deceased's family and friends and input the audio data into the generation AI. For example, the data collection unit can record interviews with the deceased's family and friends and input the audio data into the generation AI. For example, it can record reminiscences and episodes to reflect the deceased's personality. The data collection unit can also analyze the interview audio data and extract characteristics and episodes of the deceased. For example, it can reflect specific events and emotions. The data collection unit can also convert the interview audio data into text and input it into the generation AI. For example, it can analyze the interview content as text data to reflect the deceased's speaking style and choice of words. This makes it possible to recreate a more detailed portrait of the deceased based on interviews with the deceased's family and friends.

[0066] The data collection unit can use the emotion estimation function to evaluate the emotional value of data provided by the deceased's family and friends and input the data into the generation AI. The data collection unit, for example, analyzes photos and audio data provided by the deceased's family and friends and evaluates the emotional value using the emotion estimation function. For example, it quantifies the intensity and type of emotion and inputs the data into the generation AI. The data collection unit also analyzes provided text data and evaluates the emotional value using the emotion estimation function. For example, it reflects the emotion in letters and messages. The data collection unit also uses the emotion estimation function to evaluate the emotional value of the provided data and input the data into the generation AI. For example, it analyzes changes and intensity of emotions and reflects them in videos of the deceased. This makes it possible to generate videos that reflect the emotional value of the data provided by the deceased's family and friends.

[0067] The data analysis unit can analyze changes in the age and health condition of the deceased from the photo data and input the results into the generation AI. For example, the data analysis unit can analyze the photo data of the deceased in chronological order to extract changes in age and health condition. For example, it can analyze photos from a young age to later years to reflect changes in age. The data analysis unit can also analyze changes in health condition from the photo data and input the results into the generation AI. For example, it can reflect weight gain or loss and signs of illness. The data analysis unit can also analyze the photo data of the deceased and quantify changes in age and health condition. For example, it can analyze changes in facial wrinkles and hair color and input the results into the generation AI. This makes it possible to generate a video that reflects changes in the age and health condition of the deceased.

[0068] The data analysis unit can analyze the emotional intensity and tone from the voice data of the deceased and input the results to the generation AI. For example, the data analysis unit analyzes the voice data of the deceased and extracts the emotional intensity and tone. For example, it can reflect emotions such as joy and sadness. The data analysis unit can also analyze changes in emotion from the voice data and input the results to the generation AI. For example, it can reflect changes in voice tone and pitch. The data analysis unit can also analyze the voice data of the deceased and quantify the emotional intensity and tone. For example, it can score the emotional intensity and input the score to the generation AI. This makes it possible to generate a video that reflects the emotional intensity and tone of the deceased.

[0069] The data analysis unit can use the emotion estimation function to analyze emotional tendencies from the deceased's text data and input the results into the generation AI. The data analysis unit analyzes text data such as the deceased's letters and diaries, and uses the emotion estimation function to extract emotional tendencies. For example, it reflects positive and negative emotions. The data analysis unit also analyzes changes in emotion from the text data and inputs the results into the generation AI. For example, it reflects changes in the tone and expression of the text. The data analysis unit also uses the emotion estimation function to quantify emotional tendencies from the deceased's text data. For example, it scores the intensity and type of emotion and inputs the results into the generation AI. This makes it possible to generate videos that reflect the emotional tendencies of the deceased from the text data.

[0070] The data analysis unit can analyze the deceased's handwriting and input the characteristics of the handwriting and writing style into the generation AI. For example, the data analysis unit scans the deceased's handwriting and analyzes the characteristics of the handwriting and writing style. For example, it analyzes the characters in letters or memos and reflects the deceased's handwriting. The data analysis unit also quantifies the characteristics of the handwriting and inputs them into the generation AI. For example, it analyzes the size, angle, and changes in writing pressure of the characters and reflects them in the generation AI. The data analysis unit also analyzes the deceased's handwriting and inputs its characteristics into the generation AI. For example, it reflects the writing style of specific characters or phrases. This makes it possible to generate a video that reflects the characteristics of the deceased's handwriting.

[0071] The data analysis unit can analyze the characteristics of the deceased's walking and movements and input them into the generation AI. The data analysis unit, for example, analyzes video data of the deceased and extracts characteristics of their walking and movements. For example, it analyzes the way they walk and their hand movements and reflects the deceased's movements. The data analysis unit also quantifies the movement characteristics and inputs them into the generation AI. For example, it analyzes the walking speed and rhythm of their movements and reflects them in the generation AI. The data analysis unit also analyzes the characteristics of the deceased's walking and movements and inputs the data into the generation AI. For example, it reflects specific movements and gestures. This makes it possible to generate a video that reflects the characteristics of the deceased's walking and movements.

[0072] The data analysis unit can use the emotion estimation function to analyze the emotional nuances in the speech and language of the deceased and input the results into the generation AI. For example, the data analysis unit can analyze the audio data of the deceased and extract the emotional nuances in the speech and language. For example, it can reflect emotional statements and specific expressions. The data analysis unit can also use the emotion estimation function to quantify the emotional nuances in the speech and language of the deceased. For example, it can score the intensity and type of emotion and input the score into the generation AI. The data analysis unit can also analyze the text data of the deceased and extract the emotional nuances in the speech and language. For example, it can reflect the emotions of specific phrases and expressions. This makes it possible to generate a video that reflects the emotional nuances in the speech and language of the deceased.

[0073] The video generation unit allows the generation AI to automatically add background sounds and environmental sounds to the video of the deceased, enabling a more realistic reproduction. For example, the video generation unit allows the generation AI to automatically add background sounds to the video of the deceased. For example, natural sounds and city sounds are added to enhance the realism of the video. In addition, the video generation unit allows the generation AI to automatically adjust the environmental sounds according to the scene in the video. For example, indoor sounds are added to indoor scenes, and wind sounds are added to outdoor scenes. In addition, the video generation unit allows the generation AI to automatically add sound effects to the video of the deceased. For example, sound effects are added to specific scenes to enhance the realism of the video. In this way, adding background sounds and environmental sounds to the video of the deceased enables a more realistic reproduction.

[0074] The video generation unit uses the generation AI to automatically adjust the lighting and camera angle for videos of the deceased, resulting in a professional finish. For example, the video generation unit uses the generation AI to automatically adjust the lighting for videos of the deceased. For example, it adjusts the light intensity and color according to the scene to improve the quality of the video. The video generation unit also uses the generation AI to automatically adjust the camera angle for the video. For example, it selects the optimal angle to enhance visual appeal. The video generation unit also uses the generation AI to automatically adjust the lighting and camera angle for videos of the deceased. For example, it sets the optimal settings for each scene to achieve a professional finish. This allows the lighting and camera angle to be adjusted for videos of the deceased to achieve a professional finish.

[0075] The video generation unit can use the emotion estimation function to add emotional effects to videos of the deceased to elicit emotions from viewers. The video generation unit can, for example, use the emotion estimation function to add emotional effects to videos of the deceased. For example, a tear effect can be added to a moving scene to elicit emotions from viewers. The video generation unit can also add emotional music to specific scenes in the video to enhance viewers' emotions. For example, melancholy music can be added to sad scenes. The video generation unit can also use the emotion estimation function to apply emotional filters to videos of the deceased. For example, a warm color filter can be added to give viewers a sense of security. In this way, adding emotional effects to videos of the deceased can elicit emotions from viewers.

[0076] The video generation unit allows the generation AI to automatically add subtitles to videos of the deceased, complementing visual information. For example, the video generation unit allows the generation AI to automatically add subtitles to videos of the deceased. For example, what the deceased says is converted into text and displayed as subtitles. The video generation unit also allows the generation AI to automatically adjust the style of the subtitles depending on the scene in the video. For example, it uses larger letters in important scenes to emphasize visual information. The video generation unit also allows the generation AI to automatically add multilingual subtitles to videos of the deceased. For example, it displays subtitles in multiple languages, such as English and Spanish. This makes it possible to complement visual information by adding subtitles to videos of the deceased.

[0077] The video generation unit allows the generation AI to automatically add animations and graphics to videos of the deceased, enhancing their visual appeal. For example, the video generation unit allows the generation AI to automatically add animations to videos of the deceased. For example, it displays animations related to what the deceased is saying. The video generation unit also allows the generation AI to automatically add graphics according to the scenes in the video. For example, it displays graphics related to the hobbies and interests of the deceased. The video generation unit also allows the generation AI to automatically add a combination of animations and graphics to videos of the deceased. For example, it applies the optimal visual effects for each scene. This allows the visual appeal to be enhanced by adding animations and graphics to videos of the deceased.

[0078] The video generation unit can use the emotion estimation function to analyze viewers' emotional reactions to videos of the deceased in real time and dynamically adjust the content of the video. For example, the video generation unit analyzes viewers' emotional reactions to videos of the deceased in real time and dynamically adjusts the content of the video based on the data. For example, it extends scenes that move the viewer. The video generation unit also uses the emotion estimation function to analyze viewers' emotional reactions and dynamically change scenes in the video. For example, it emphasizes scenes that interest the viewer. The video generation unit also builds a system that adjusts the content of the video in real time based on the viewer's emotional reaction data. For example, it changes video effects and music according to the viewer's emotions. This makes it possible to analyze viewers' emotional reactions in real time and dynamically adjust the content of the video.

[0079] The video generation unit uses the generation AI to automatically adjust the tempo and key of the music for videos of the deceased, enhancing the emotional effect. For example, the video generation unit uses the generation AI to automatically adjust the tempo of the music for videos of the deceased. For example, the tempo may be slowed down in moving scenes to increase tension. The video generation unit also uses the generation AI to automatically adjust the key of the music depending on the scene in the video. For example, a lower key may be used in sad scenes to enhance the emotional effect. The video generation unit also uses the generation AI to automatically adjust the tempo and key of the music for videos of the deceased. For example, optimal music settings are set for each scene to maximize the emotional effect. This allows the emotional effect to be enhanced by adjusting the tempo and key of the music for videos of the deceased.

[0080] The video generation unit allows the generation AI to automatically apply color tones and filters to videos of the deceased, thereby ensuring visual consistency. For example, the video generation unit allows the generation AI to automatically adjust color tones to videos of the deceased. For example, the overall color tone is unified to a warm tone to ensure visual consistency. The video generation unit also allows the generation AI to automatically apply filters depending on the scene in the video. For example, a sepia filter can be used in certain scenes to create a nostalgic feeling. The video generation unit also allows the generation AI to automatically apply a combination of color tones and filters to videos of the deceased. For example, optimal settings are set for each scene to ensure visual consistency. This allows the generation AI to automatically apply color tones and filters to videos of the deceased, thereby ensuring visual consistency.

[0081] The video generation unit can use the emotion estimation function to analyze viewers' emotional reactions to videos of the deceased and optimize the editing content. The video generation unit, for example, analyzes viewers' emotional reactions to videos of the deceased and optimizes the editing content based on the data. For example, it can emphasize moving scenes. The video generation unit also uses the emotion estimation function to analyze viewers' emotional reactions and dynamically change scenes in the video. For example, it can emphasize scenes that interest the viewer. The video generation unit also builds a system that adjusts the editing content of the video in real time based on the viewer's emotional reaction data. For example, it can change video effects and music according to the viewer's emotions. In this way, by analyzing viewers' emotional reactions and optimizing the editing content, it is possible to generate more moving videos.

[0082] The video generation unit allows the generation AI to automatically add 3D effects to videos of the deceased, enhancing the visual impact. For example, the video generation unit automatically adds 3D effects to videos of the deceased. For example, the deceased's appearance is recreated as a 3D model and incorporated into the video. The video generation unit also automatically adjusts the 3D effects according to the scenes in the video. For example, 3D objects are added in specific scenes to enhance the visual impact. The video generation unit also allows the generation AI to automatically add a combination of 3D effects to videos of the deceased. For example, the optimal 3D effect is applied to each scene to enhance visual appeal. In this way, adding 3D effects to videos of the deceased can enhance the visual impact.

[0083] The video generation unit allows the generation AI to automatically add narration to videos of the deceased, enhancing storytelling. For example, the video generation unit allows the generation AI to automatically add narration to videos of the deceased. For example, narration recounting the life of the deceased can be added to enhance storytelling. The video generation unit also allows the generation AI to automatically adjust the content of the narration according to the scene in the video. For example, narration recounting a moving episode can be added to a specific scene. The video generation unit also allows the generation AI to automatically combine and add narration to videos of the deceased. For example, the optimal narration can be applied to each scene to enhance storytelling. In this way, storytelling can be enhanced by adding narration to videos of the deceased.

[0084] The video generation unit can use the emotion estimation function to add a customized message based on the viewer's emotional reaction to the video of the deceased. The video generation unit, for example, analyzes the viewer's emotional reaction to the video of the deceased and adds a customized message based on that data. For example, a message of gratitude can be added to a touching scene. The video generation unit also uses the emotion estimation function to analyze the viewer's emotional reaction and dynamically change the scenes in the video. For example, a special message can be added to a scene that the viewer is interested in. The video generation unit also builds a system that adjusts the content of the video in real time based on the viewer's emotional reaction data. For example, a customized message can be added depending on the viewer's emotions. In this way, by adding a customized message based on the viewer's emotional reaction, a more touching video can be generated.

[0085] When saving a video of the deceased, the generation AI can automatically select the optimal file format and resolution. For example, when saving a video of the deceased, the generation AI automatically selects the optimal file format. For example, it selects a format such as MP4 or AVI depending on the viewing device. In addition, the video generation unit automatically selects the optimal resolution depending on the content of the video. For example, it selects high resolution for detailed scenes and low resolution for general scenes. In addition, when saving a video of the deceased, the generation AI automatically selects a combination of file format and resolution. For example, it sets the optimal settings for the viewing environment and saves the video. As a result, by selecting the optimal file format and resolution when saving a video of the deceased, it is possible to optimally save the video according to the viewing environment.

[0086] When saving videos of the deceased, the generation AI automatically adds metadata, improving searchability. When saving videos of the deceased, the generation AI automatically adds metadata. For example, information about the content and scenes of the video is saved as metadata. The video generation unit also automatically adjusts the metadata according to the scenes in the video. For example, keywords related to specific scenes are added to improve searchability. When saving videos of the deceased, the generation AI automatically combines and adds metadata. For example, optimal metadata is set for each scene to improve searchability. In this way, adding metadata to videos of the deceased can improve searchability.

[0087] The video generation unit can use the emotion estimation function to analyze viewers' emotional reactions to videos of the deceased and optimize the sharing method. The video generation unit, for example, analyzes viewers' emotional reactions to videos of the deceased and optimizes the sharing method based on the data. For example, it selects a sharing method that emphasizes moving scenes. The video generation unit also uses the emotion estimation function to analyze viewers' emotional reactions and dynamically change video scenes. For example, it selects a sharing method that emphasizes scenes that interest the viewer. The video generation unit also builds a system that adjusts the video sharing method in real time based on viewers' emotional reaction data. For example, it selects the optimal sharing method depending on the viewer's emotions. In this way, by analyzing viewers' emotional reactions and optimizing the sharing method, more effective sharing is possible.

[0088] When saving a video of the deceased, the generation AI automatically creates a backup, ensuring data safety. For example, when saving a video of the deceased, the generation AI automatically creates a backup. For example, the backup is saved in cloud storage to ensure data safety. The video generation unit also automatically adjusts the frequency of backups depending on the content of the video. For example, videos with many important scenes will be backed up more frequently. When saving a video of the deceased, the generation AI automatically creates multiple backups and saves them in different locations. For example, backups are saved simultaneously in local storage and cloud storage. This makes it possible to ensure data safety by creating backups when saving a video of the deceased.

[0089] When saving a video of the deceased, the generation AI can automatically apply privacy settings and limit the scope of sharing. For example, when saving a video of the deceased, the generation AI automatically applies privacy settings. For example, it can set the video so that only specific family and friends can access it. The video generation unit also automatically adjusts the privacy settings depending on the content of the video. For example, strict privacy settings are applied to videos with many personal scenes. The video generation unit also automatically applies a combination of privacy settings when saving a video of the deceased. For example, different privacy settings are set for each scene to limit the scope of sharing. In this way, by applying privacy settings when saving a video of the deceased, the scope of sharing can be limited.

[0090] The video generation unit can use the emotion estimation function to select the optimal sharing platform based on the emotional reactions of viewers to the videos of the deceased. The video generation unit, for example, analyzes the emotional reactions of viewers to the videos of the deceased and selects the optimal sharing platform based on that data. For example, videos with many moving scenes are shared on social media. The video generation unit also uses the emotion estimation function to analyze the emotional reactions of viewers and dynamically change the scenes in the video. For example, it selects a sharing platform that emphasizes scenes that interest the viewer. The video generation unit also builds a system that adjusts the video sharing platform in real time based on the viewer's emotional reaction data. For example, it selects the optimal sharing platform depending on the viewer's emotions. This allows for more effective sharing by selecting the optimal sharing platform based on the viewer's emotional reaction.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The data collection unit can collect data related to the deceased's hobbies and interests and input it into the generation AI. For example, it can collect the deceased's reading history and input it into the generation AI. For example, it can analyze the deceased's e-book library and library borrowing history to reflect the deceased's interests. The data collection unit can also collect the deceased's music playlists and input it into the generation AI. For example, it can analyze Spotify or Apple Music playlists to reflect the deceased's musical preferences. The data collection unit can also collect data related to the deceased's hobbies and input it into the generation AI. For example, it can analyze sports results and photos of artwork to reflect the deceased's hobbies. This makes it possible to generate videos that reflect the deceased's hobbies and interests.

[0093] The data collection unit can record interviews with the deceased's family and friends and input the audio data into the generation AI. For example, interviews with the deceased's family and friends can be recorded and the audio data can be input into the generation AI. For example, reminiscences and episodes can be recorded to reflect the deceased's personality. The data collection unit can also analyze the interview audio data and extract characteristics and episodes of the deceased. For example, specific events and emotions can be reflected. The data collection unit can also convert the interview audio data into text and input it into the generation AI. For example, the interview content can be analyzed as text data to reflect the deceased's speaking style and choice of words. This makes it possible to recreate a more detailed portrait of the deceased based on interviews with the deceased's family and friends.

[0094] The data collection unit can use the emotion estimation function to evaluate the emotional value of data provided by the deceased's family and friends and input the data into the generation AI. For example, it can analyze photos and audio data provided by the deceased's family and friends and evaluate the emotional value using the emotion estimation function. For example, it can quantify the intensity and type of emotion and input the data into the generation AI. The data collection unit can also analyze provided text data and evaluate the emotional value using the emotion estimation function. For example, it can reflect the emotion in letters and messages. The data collection unit can also use the emotion estimation function to evaluate the emotional value of the provided data and input the data into the generation AI. For example, it can analyze changes and intensity of emotions and reflect them in videos of the deceased. This makes it possible to generate videos that reflect the emotional value of the data provided by the deceased's family and friends.

[0095] The data analysis unit can analyze changes in the age and health condition of the deceased from the photo data and input the results into the generation AI. For example, it can analyze the photo data of the deceased over time to extract changes in age and health condition. For example, it can analyze photos from a young age to later years to reflect changes in age. The data analysis unit can also analyze changes in health condition from the photo data and input the results into the generation AI. For example, it can reflect weight gain or loss and signs of illness. The data analysis unit can also analyze the photo data of the deceased and quantify changes in age and health condition. For example, it can analyze changes in facial wrinkles and hair color and input the results into the generation AI. This makes it possible to generate a video that reflects changes in the age and health condition of the deceased.

[0096] The data analysis unit can analyze the emotional intensity and tone from the voice data of the deceased and input the results into the generation AI. For example, it can analyze the voice data of the deceased and extract the emotional intensity and tone. For example, it can reflect emotions such as joy and sadness. The data analysis unit can also analyze changes in emotion from the voice data and input the results into the generation AI. For example, it can reflect changes in voice tone and pitch. The data analysis unit can also analyze the voice data of the deceased and quantify the emotional intensity and tone. For example, it can score the emotional intensity and input the score into the generation AI. This makes it possible to generate a video that reflects the emotional intensity and tone of the deceased.

[0097] The data analysis unit can analyze the deceased's handwriting and input the characteristics of the handwriting and writing style into the generation AI. For example, it can scan the deceased's handwriting and analyze the characteristics of the handwriting and writing style. For example, it can analyze the characters in letters or memos and reflect the deceased's handwriting. The data analysis unit also quantifies the characteristics of the handwriting and inputs them into the generation AI. For example, it can analyze the size, angle, and changes in writing pressure of the characters and reflect these in the generation AI. The data analysis unit also analyzes the deceased's handwriting and inputs these characteristics into the generation AI. For example, it can reflect the writing style of specific characters or phrases. This makes it possible to generate a video that reflects the characteristics of the deceased's handwriting.

[0098] The video generation unit uses the generation AI to automatically add background sounds and environmental sounds to videos of the deceased, allowing for more realistic reproductions. For example, the generation AI automatically adds background sounds to videos of the deceased. For example, natural or city sounds can be added to enhance the realism of the video. The video generation unit also uses the generation AI to automatically adjust environmental sounds according to the video scene. For example, indoor sounds can be added to indoor scenes, and wind sounds can be added to outdoor scenes. The video generation unit also uses the generation AI to automatically add sound effects to videos of the deceased. For example, sound effects can be added to specific scenes to enhance the realism of the video. This allows for more realistic reproductions by adding background sounds and environmental sounds to videos of the deceased.

[0099] The video generation unit uses the generation AI to automatically adjust the lighting and camera angle for videos of the deceased, resulting in a professional finish. For example, the generation AI automatically adjusts the lighting for videos of the deceased. For example, it adjusts the light intensity and color according to the scene, improving the quality of the video. The video generation unit also automatically adjusts the camera angle for the video. For example, it selects the optimal angle to enhance visual appeal. The video generation unit also uses the generation AI to automatically adjust a combination of lighting and camera angle for videos of the deceased. For example, it sets the optimal settings for each scene to achieve a professional finish. This allows the lighting and camera angle to be adjusted for videos of the deceased, resulting in a professional finish.

[0100] The video generation unit can use the emotion estimation function to add emotional effects to videos of the deceased to elicit emotions from viewers. For example, the emotion estimation function can be used to add emotional effects to videos of the deceased. For example, a tear effect can be added to a moving scene to elicit emotions from viewers. The video generation unit can also add emotional music to specific scenes in the video to enhance the viewers' emotions. For example, melancholy music can be added to sad scenes. The video generation unit can also use the emotion estimation function to apply emotional filters to videos of the deceased. For example, a warm color filter can be added to give the viewer a sense of security. In this way, adding emotional effects to videos of the deceased can elicit emotions from viewers.

[0101] The video generation unit allows the generation AI to automatically add subtitles to videos of the deceased, complementing visual information. For example, the generation AI automatically adds subtitles to videos of the deceased. For example, what the deceased says is converted into text and displayed as subtitles. The video generation unit also automatically adjusts the style of the subtitles according to the scenes in the video. For example, it uses larger letters in important scenes to emphasize visual information. The video generation unit also allows the generation AI to automatically add multilingual subtitles to videos of the deceased. For example, it displays subtitles in multiple languages, such as English and Spanish. This makes it possible to complement visual information by adding subtitles to videos of the deceased.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data collection department collects photographs, audio data, and text data of the deceased. For example, they scan photos from the deceased's photo albums and digitize audio recordings of the deceased's voice. They also collect text data such as letters and diaries written by the deceased. Step 2: The data analysis unit analyzes the collected data. For example, image analysis technology is used to extract the facial features of the deceased from the photo data. Voice analysis technology is used to analyze the voice characteristics of the deceased from the audio data. Furthermore, text analysis technology is used to learn the speaking style and vocabulary of the deceased from the text data. Step 3: The model generation unit generates a model to recreate the appearance and voice of the deceased based on the analyzed data. For example, it generates a model of the face and voice of the deceased using a machine learning algorithm. Step 4: The video generator generates a video that recreates the appearance and voice of the deceased based on the generated model. For example, the generator AI can create a video that recreates what the deceased said while they were alive, or a video that conveys a message to family and friends.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects photographs, audio data, and text data of the deceased; a data analysis unit that analyzes the data collected by the data collection unit; a model generation unit that generates a model for reproducing the appearance and voice of the deceased based on the data analyzed by the data analysis unit; a moving image generating unit that generates a moving image that reproduces the appearance and voice of the deceased based on the model generated by the model generating unit. A system characterized by:

2. The data collection unit Collect posts and comments from the deceased person's social media accounts and input them into the generation AI.

2. The system of claim 1.

3. The data collection unit Analyze the video call history of the deceased person, extract facial expressions and gesture characteristics, and input them into the generation AI.

2. The system of claim 1.

4. The data collection unit Analyze changes in emotions from photos and audio data of the deceased and input them into the generation AI.

2. The system of claim 1.

5. The data collection unit Collect data related to the deceased's hobbies and interests and input it into the AI ​​generator.

2. The system of claim 1.

6. The data collection unit Interviews with family and friends of the deceased are recorded, and the audio data is input into the AI.

2. The system of claim 1.

Images