System

Abstract

An object of the system according to the embodiment is to reproduce a figure and a voice of a deceased parent or family and to perform a conversation.SOLUTION: A system includes a data collection part, a data analysis part, a reproduction part, and an interaction part. The data collection unit collects past photographs, videos, and audio data. The data analysis unit analyzes the data collected by the data collection unit. The reproduction unit reproduces the figure and voice of the deceased parent or family based on the data analyzed by the data analysis unit. The interaction unit performs interaction with the user using the figure and the voice reproduced by the reproduction 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 cannot reproduce the appearance or voice of deceased parents or family members or hold a dialogue with them, so there is room for improvement.

[0005] The system according to the embodiment aims to recreate the appearance and voice of a deceased parent or family member and engage in a dialogue with them. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a reproduction unit, and a dialogue unit. The data collection unit collects past photo, video, and audio data. The data analysis unit analyzes the data collected by the data collection unit. The reproduction unit reproduces the appearance and voice of a deceased parent or family member based on the data analyzed by the data analysis unit. The dialogue unit engages in dialogue with a user using the appearance and voice reproduced by the reproduction unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the appearance and voice of deceased parents or family members and engage in dialogue with them. [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 reproduction system according to the embodiment of the present invention is a system that reproduces the appearance and voice of a deceased parent or family member and engages in a dialogue with the user. As a result, the reproduction system can provide emotional healing by reproducing the appearance and voice of the deceased parent or family member and engaging in a dialogue with the user.

[0029] The reproduction system according to the embodiment includes a data collection unit, a data analysis unit, a reproduction unit, and a dialogue unit. The data collection unit collects past photo, video, and audio data. For example, it collects digital or analog data provided by a user. The data collection unit can also collect data provided in a specific format. The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes past photo data and automatically repairs degraded parts. The generation AI can also analyze past video data and fill in missing frames. The generation AI can also analyze past audio data to remove noise and improve sound quality. The reproduction unit recreates the appearance and voice of a deceased parent or family member based on the data analyzed by the data analysis unit. For example, the generation AI recreates the appearance of a mother who died 20 years ago, allowing the user to see the face of her grown-up son. The generation AI can also interact with the user using the recreated appearance and voice. The dialogue unit interacts with the user using the appearance and voice recreated by the reproduction unit. For example, the mother recreated by the generation AI responds by saying, "Thank you for showing me your son's growth." As a result, the reproduction system according to the embodiment can provide emotional healing by reproducing the appearance and voice of a deceased parent or family member and engaging in a dialogue with the user.

[0030] The data analysis unit automatically complements degraded or missing parts of the data, enabling clearer images and audio to be reproduced. For example, the data analysis unit uses the generative AI to analyze past photo data and automatically repair degraded parts. For example, it can restore the color of faded photos and remove scratches and stains. The generative AI can also analyze past video data and complement missing frames. For example, it can enable smooth playback of interrupted video. The generative AI can also analyze past audio data, remove noise, and improve sound quality. For example, it can convert noisy recordings into clear audio. This complements degraded or missing parts of the data, enabling clearer reproduction.

[0031] The data analysis unit takes into account changes in the data over time and can emphasize the characteristics of specific periods. For example, the data analysis unit uses a generative AI to analyze past photo data and emphasize the fashion and background of a specific period. For example, photos from the 1970s can recreate the trends of that time. The generative AI can also analyze past video data and recreate the visual effects and color palette of a specific period. For example, it can recreate the texture of old film. The generative AI can also analyze past audio data and recreate the sound quality and recording technology of a specific period. For example, it can recreate the sound quality of an old radio broadcast. This allows for more realistic reproductions by emphasizing the characteristics of a specific period.

[0032] The data collection unit also collects online data from the user's social media or blog, allowing it to provide more multifaceted information. For example, the data collection unit uses a generation AI to analyze the user's social media posts and complement past events and emotions. For example, it analyzes comments posted along with photos of a family trip. The generation AI also analyzes the user's blog posts and complements details of past events. For example, it extracts detailed descriptions of specific events. The generation AI also analyzes the user's online album and complements past photos and videos. For example, it collects family group photos and video clips. This allows it to provide more multifaceted information by collecting online data.

[0033] The data collection unit can also integrate data provided by other family members and friends to achieve richer reproductions. For example, the data collection unit allows the generation AI to analyze photo data provided by other family members and integrate it with the user's data. For example, it can recreate a photo of the entire family. The generation AI can also analyze video data provided by friends and integrate it with the user's data. For example, it can recreate a family event from multiple perspectives. The generation AI can also analyze audio data provided by other family members and integrate it with the user's data. For example, it can recreate the voices of all family members. This allows for richer reproductions by integrating data from other family members and friends.

[0034] The reenactment unit can perform reenactments based on the user's memories and episodes. For example, the generation AI may reenact the appearance of a deceased parent based on the user's memories. For example, it may reflect the memories the user recounts. The generation AI may also reenact the voice of a deceased family member based on the user's episodes. For example, it may reenact a conversation about a specific event. The generation AI may also reenact the facial expressions and gestures of a deceased parent based on the user's memories. For example, it may reenact a facial expression that expresses a specific emotion. This allows for reenactments based on the user's memories and episodes, providing a more personalized and emotional experience.

[0035] The reproduction unit can generate multiple patterns of appearances at different ages and in different situations, allowing the user to select from them. For example, the generation AI can reproduce the appearance of a deceased parent at different ages, allowing the user to select from them. For example, it can provide an appearance in their younger years and an appearance in their later years. The generation AI can also reproduce the appearance of a deceased family member in different situations, allowing the user to select from them. For example, it can provide an appearance in everyday life and an appearance at a special event. The generation AI can also reproduce different clothing and hairstyles of the deceased parent, allowing the user to select from them. For example, it can provide everyday clothes and formal clothes. This allows the user to have a variety of options by providing appearances at different ages and in different situations.

[0036] The reproduction unit can use the 3D model to enable reproduction in a VR or AR environment. For example, the generation AI creates a 3D model of a deceased parent and reproduces it in a VR environment. For example, a user interacts with the parent using a VR headset. The generation AI also creates 3D models of deceased family members and reproduces them in an AR environment. For example, the image of the family member is overlaid on the real world via a smartphone. The generation AI also creates a 3D model of the deceased parent to provide an interactive experience. For example, the user interacts with the parent in a virtual space. This enables reproduction in a VR or AR environment, providing a more immersive experience.

[0037] The re-enactment unit can also recreate conversations with other family members and friends, providing a more realistic experience. For example, the re-enactment unit may use the generation AI to analyze data from other family members and recreate a conversation with a deceased parent. For example, it may recreate a scene where the whole family gets together. The generation AI may also analyze data from friends and recreate conversations with deceased family members. For example, it may recreate a conversation with a best friend. The generation AI may also integrate data from multiple family members and friends to provide a realistic conversation experience. For example, it may recreate a family gathering or party. This allows for a more realistic experience by recreating conversations with other family members and friends.

[0038] The dialogue unit can analyze data from the user's daily life and understand the user's current situation. For example, the generation AI analyzes the user's health data to understand the current situation. For example, it analyzes heart rate and sleep patterns. The generation AI also analyzes the user's activity log to understand the current situation. For example, it analyzes exercise volume and movement history. The generation AI also integrates data from the user's daily life to understand the overall situation. For example, it analyzes food records and stress levels. In this way, by analyzing the user's daily life data, the current situation can be understood more accurately.

[0039] The dialogue unit can understand the user's current situation by taking into account environmental data around the user. For example, the generation AI in the dialogue unit analyzes weather data around the user to understand the current situation. For example, it takes into account differences in emotions on rainy days and sunny days. The generation AI also analyzes the user's seasonal data to understand the current situation. For example, it identifies patterns in which emotions change with the change of seasons. The generation AI also integrates environmental data around the user to understand the overall situation. For example, it analyzes changes in temperature and humidity. This allows the dialogue unit to more accurately understand the user's current situation by taking into account environmental data around the user.

[0040] The dialogue unit can understand the user's emotions by referring to the user's past dialogue history and behavioral patterns. For example, the generation AI in the dialogue unit analyzes the user's past dialogue history to understand emotions. For example, it identifies changes in emotions from past conversation content. The generation AI also analyzes the user's behavioral patterns to understand emotions. For example, it identifies emotional patterns related to specific actions or habits. The generation AI also integrates the user's past dialogue history and behavioral patterns to understand overall emotions. For example, it analyzes the relationship between past actions and emotions. This allows the user's emotions to be understood more accurately by referring to past dialogue history and behavioral patterns.

[0041] The dialogue unit can refer to the user's past dialogue history to achieve a consistent dialogue. For example, the dialogue unit's generation AI analyzes the user's past dialogue history to achieve a consistent dialogue. For example, it generates an appropriate response based on the content of the past conversation. The generation AI also refers to the user's past dialogue history to maintain the flow of the dialogue. For example, it starts from a continuation of the previous conversation. The generation AI also integrates the user's past dialogue history to achieve a comprehensive dialogue. For example, it provides new topics based on the content of the past conversation. In this way, a consistent dialogue can be achieved by referring to the past dialogue history.

[0042] The dialogue unit can provide topics based on the user's interests and concerns. For example, the generation AI in the dialogue unit analyzes the user's interests and provides topics for the dialogue. For example, it can provide topics related to the user's favorite hobbies and activities. The generation AI can also analyze the user's interests and customize the content of the dialogue. For example, it can provide news and topics that interest the user. The generation AI can also refer to the user's past dialogue history and provide topics based on the user's interests and concerns. For example, it can provide topics related to what the user has previously said. This allows for more interesting dialogue by providing topics based on the user's interests and concerns.

[0043] The dialogue unit can provide a function that allows simultaneous dialogue with multiple recreated family members and friends. For example, the dialogue unit provides a function that allows the generation AI to simultaneously dialogue with multiple recreated family members. For example, it recreates a scene where the whole family gets together. It also provides a function that allows the generation AI to simultaneously dialogue with multiple recreated friends. For example, it recreates a conversation with a group of friends. It also provides a function that allows the generation AI to simultaneously dialogue with multiple recreated family members and friends, providing a realistic dialogue experience. For example, it recreates a family gathering or a party. This allows simultaneous dialogue with multiple recreated family members and friends, providing a more realistic dialogue experience.

[0044] The dialogue unit can provide options that allow the user to customize the content of the dialogue. For example, the dialogue unit may provide options that allow the generation AI to customize the content of the dialogue. For example, the user may select the topic they want to talk about. The dialogue unit may also provide options that allow the generation AI to customize the tone and style of the dialogue. For example, the user may select formal dialogue or casual dialogue. The dialogue unit may also provide options that allow the generation AI to customize the length and frequency of the dialogue. For example, the user may select short conversations or long conversations. This allows the user to customize the content of the dialogue, thereby enabling more personalized dialogue.

[0045] The dialogue unit can refer to the user's past healing experiences and select an effective method. In the dialogue unit, for example, the generation AI analyzes the user's past healing experiences and selects an effective method. For example, it suggests relaxation methods that have been effective in the past. The generation AI also refers to the user's past healing experiences and selects the most appropriate healing method. For example, it provides music or videos that have been effective in the past. The generation AI also integrates the user's past healing experiences and selects a comprehensive healing method. For example, it suggests the most appropriate healing method based on past data. In this way, an effective healing method can be selected by referring to past healing experiences.

[0046] The dialogue unit can suggest relaxation methods based on the user's hobbies and interests. For example, the dialogue unit's generation AI analyzes the user's hobbies and suggests relaxation methods based on the hobbies. For example, gardening is suggested for a user who likes gardening. The generation AI also analyzes the user's interests and suggests relaxation methods based on the interests. For example, relaxing books are suggested for a user who likes reading. The generation AI also integrates the user's hobbies and interests to suggest comprehensive relaxation methods. For example, activities based on the hobbies and interests are suggested. This makes it possible to provide more effective healing by suggesting relaxation methods based on hobbies and interests.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reproduction system can also collect the user's health data and adjust the dialogue content based on the user's health condition. For example, it can monitor the user's heart rate and blood pressure and provide advice on how to relax when stress levels are high. It can also analyze the user's sleep patterns and encourage rest when sleep deprivation is present. It can also collect the user's exercise data and suggest light exercise when exercise is lacking. This allows for dialogue tailored to the user's health condition, providing more personalized and effective support.

[0049] The reproduction system can also conduct dialogue based on the user's hobbies and interests. For example, if the user likes music, the reproduced parents or family members can provide topics related to music. If the user is interested in sports, the reproduced family members can talk about the latest sports news and game results. Furthermore, if the user likes traveling, the reproduced parents can share memories of past trips and suggest new travel destinations. This allows for dialogue based on the user's hobbies and interests, providing a more familiar experience.

[0050] The replay system can also refer to the user's past dialogue history to ensure consistent dialogue. For example, the replayed parent can remember the content of the previous dialogue and continue the conversation from where it left off. It can also suggest new topics based on what the user previously discussed. Furthermore, it can analyze the user's past dialogue history and identify specific patterns and interests to provide more appropriate dialogue content. By referencing the past dialogue history, it is possible to achieve consistent dialogue and provide more natural communication.

[0051] The reenactment system can also perform reenactments based on the user's memories and episodes. For example, it can reenact the appearance of a deceased parent by reflecting the memories the user recounts. It can also reenact conversations related to specific events. It can also reenact facial expressions and gestures that express specific emotions. This allows for a more personalized and emotional experience by reenacting events based on the user's memories and episodes.

[0052] The reproduction system can also generate multiple patterns of appearances at different ages and in different situations, allowing the user to select from them. For example, it can provide a younger appearance and an older appearance. It can also provide an everyday appearance and an appearance for a special event. It can also provide casual clothes and formal attire. This allows the user to have a variety of options by providing appearances at different ages and in different situations.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data collection unit collects historical photo, video, and audio data. For example, it can collect digital or analog data provided by the user. It can also collect data provided in a specific format. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI can analyze past photo data and automatically repair degraded parts. The generation AI can also analyze past video data and fill in missing frames. Furthermore, the generation AI can analyze past audio data to remove noise and improve sound quality. Step 3: The reconstructor recreates the appearance and voice of a deceased parent or family member based on the data analyzed by the data analyzer. For example, the generator AI can recreate the appearance of a mother who died 20 years ago, allowing the user to see the face of their grown-up son. Step 4: The dialogue unit uses the appearance and voice reproduced by the reproduction unit to converse with the user. For example, a mother reproduced by the generation AI might respond by saying, "Thank you for showing me how my son has grown."

[0055] (Example 2) The reproduction system according to the embodiment of the present invention is a system that reproduces the appearance and voice of a deceased parent or family member and engages in a dialogue with the user. As a result, the reproduction system can provide emotional healing by reproducing the appearance and voice of the deceased parent or family member and engaging in a dialogue with the user.

[0056] The reproduction system according to the embodiment includes a data collection unit, a data analysis unit, a reproduction unit, and a dialogue unit. The data collection unit collects past photo, video, and audio data. For example, it collects digital or analog data provided by a user. The data collection unit can also collect data provided in a specific format. The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes past photo data and automatically repairs degraded parts. The generation AI can also analyze past video data and fill in missing frames. The generation AI can also analyze past audio data to remove noise and improve sound quality. The reproduction unit recreates the appearance and voice of a deceased parent or family member based on the data analyzed by the data analysis unit. For example, the generation AI recreates the appearance of a mother who died 20 years ago, allowing the user to see the face of her grown-up son. The generation AI can also interact with the user using the recreated appearance and voice. The dialogue unit interacts with the user using the appearance and voice recreated by the reproduction unit. For example, the mother recreated by the generation AI responds by saying, "Thank you for showing me your son's growth." As a result, the reproduction system according to the embodiment can provide emotional healing by reproducing the appearance and voice of a deceased parent or family member and engaging in a dialogue with the user.

[0057] The data analysis unit automatically complements degraded or missing parts of the data, enabling clearer images and audio to be reproduced. For example, the data analysis unit uses the generative AI to analyze past photo data and automatically repair degraded parts. For example, it can restore the color of faded photos and remove scratches and stains. The generative AI can also analyze past video data and complement missing frames. For example, it can enable smooth playback of interrupted video. The generative AI can also analyze past audio data, remove noise, and improve sound quality. For example, it can convert noisy recordings into clear audio. This complements degraded or missing parts of the data, enabling clearer reproduction.

[0058] The data analysis unit takes into account changes in the data over time and can emphasize the characteristics of specific periods. For example, the data analysis unit uses a generative AI to analyze past photo data and emphasize the fashion and background of a specific period. For example, photos from the 1970s can recreate the trends of that time. The generative AI can also analyze past video data and recreate the visual effects and color palette of a specific period. For example, it can recreate the texture of old film. The generative AI can also analyze past audio data and recreate the sound quality and recording technology of a specific period. For example, it can recreate the sound quality of an old radio broadcast. This allows for more realistic reproductions by emphasizing the characteristics of a specific period.

[0059] The data analysis unit uses the emotion estimation function to analyze emotional elements contained in the data and identify emotionally significant moments. For example, the data analysis unit allows the generation AI to analyze past photo data and identify emotionally significant moments. For example, it can highlight moments of family smiles or tears. The generation AI can also analyze past video data and extract emotionally significant scenes. For example, it can identify scenes of family reunions or farewells. The generation AI can also analyze past audio data and identify emotionally significant statements and conversations. For example, it can extract moving speeches and messages. This allows for the identification of emotionally significant moments, making it possible to recreate more moving scenes.

[0060] The data collection unit also collects online data from the user's social media or blog, allowing it to provide more multifaceted information. For example, the data collection unit uses a generation AI to analyze the user's social media posts and complement past events and emotions. For example, it analyzes comments posted along with photos of a family trip. The generation AI also analyzes the user's blog posts and complements details of past events. For example, it extracts detailed descriptions of specific events. The generation AI also analyzes the user's online album and complements past photos and videos. For example, it collects family group photos and video clips. This allows it to provide more multifaceted information by collecting online data.

[0061] The data collection unit can also integrate data provided by other family members and friends to achieve richer reproductions. For example, the data collection unit allows the generation AI to analyze photo data provided by other family members and integrate it with the user's data. For example, it can recreate a photo of the entire family. The generation AI can also analyze video data provided by friends and integrate it with the user's data. For example, it can recreate a family event from multiple perspectives. The generation AI can also analyze audio data provided by other family members and integrate it with the user's data. For example, it can recreate the voices of all family members. This allows for richer reproductions by integrating data from other family members and friends.

[0062] The data analysis unit can use the emotion estimation function to generate emotional stories based on the data and provide them to the user. For example, the data analysis unit uses a generation AI to analyze past photo data and generate emotional stories. For example, it can provide a story depicting family growth and bonds. The generation AI can also analyze past video data and generate emotional stories. For example, it can provide a documentary looking back on family memories. The generation AI can also analyze past audio data and generate emotional stories. For example, it can provide a story that connects family conversations and messages. In this way, by generating emotional stories, it is possible to provide a moving experience to the user.

[0063] The reenactment unit can perform reenactments based on the user's memories and episodes. For example, the generation AI may reenact the appearance of a deceased parent based on the user's memories. For example, it may reflect the memories the user recounts. The generation AI may also reenact the voice of a deceased family member based on the user's episodes. For example, it may reenact a conversation about a specific event. The generation AI may also reenact the facial expressions and gestures of a deceased parent based on the user's memories. For example, it may reenact a facial expression that expresses a specific emotion. This allows for reenactments based on the user's memories and episodes, providing a more personalized and emotional experience.

[0064] The reproduction unit can generate multiple patterns of appearances at different ages and in different situations, allowing the user to select from them. For example, the generation AI can reproduce the appearance of a deceased parent at different ages, allowing the user to select from them. For example, it can provide an appearance in their younger years and an appearance in their later years. The generation AI can also reproduce the appearance of a deceased family member in different situations, allowing the user to select from them. For example, it can provide an appearance in everyday life and an appearance at a special event. The generation AI can also reproduce different clothing and hairstyles of the deceased parent, allowing the user to select from them. For example, it can provide everyday clothes and formal clothes. This allows the user to have a variety of options by providing appearances at different ages and in different situations.

[0065] The reproduction unit uses the emotion estimation function to enable the reproduced parent or family member to change their facial expression and tone of voice according to the user's emotions. For example, the generation AI analyzes the user's emotions and changes the facial expression of the reproduced parent. For example, when the user is sad, it reproduces a comforting expression. The generation AI also analyzes the user's emotions and changes the tone of the reproduced family member's voice. For example, when the user is happy, it speaks in a joyful tone. The generation AI also analyzes the user's emotions and changes the gestures of the reproduced parent. For example, when the user is nervous, it reproduces a relaxing gesture. This allows for more natural conversations by changing the facial expression and tone of voice according to the user's emotions.

[0066] The reproduction unit can use the 3D model to enable reproduction in a VR or AR environment. For example, the generation AI creates a 3D model of a deceased parent and reproduces it in a VR environment. For example, a user interacts with the parent using a VR headset. The generation AI also creates 3D models of deceased family members and reproduces them in an AR environment. For example, the image of the family member is overlaid on the real world via a smartphone. The generation AI also creates a 3D model of the deceased parent to provide an interactive experience. For example, the user interacts with the parent in a virtual space. This enables reproduction in a VR or AR environment, providing a more immersive experience.

[0067] The re-enactment unit can also recreate conversations with other family members and friends, providing a more realistic experience. For example, the re-enactment unit may use the generation AI to analyze data from other family members and recreate a conversation with a deceased parent. For example, it may recreate a scene where the whole family gets together. The generation AI may also analyze data from friends and recreate conversations with deceased family members. For example, it may recreate a conversation with a best friend. The generation AI may also integrate data from multiple family members and friends to provide a realistic conversation experience. For example, it may recreate a family gathering or party. This allows for a more realistic experience by recreating conversations with other family members and friends.

[0068] The reproduction unit uses the emotion estimation function to enable the reproduced parent or family member to give advice or encouragement according to the user's emotions. For example, the reproduction unit has the generation AI analyze the user's emotions and the reproduced parent give advice. For example, it may offer words of encouragement when the user is worried. The generation AI may also analyze the user's emotions and the reproduced family member may offer encouragement. For example, it may offer words of encouragement when the user is feeling down. The generation AI may also analyze the user's emotions and the reproduced parent may offer specific advice. For example, it may suggest a solution when the user is facing a difficult situation. This allows for more effective emotional healing by providing advice and encouragement according to the user's emotions.

[0069] The dialogue unit can analyze data from the user's daily life and understand the user's current situation. For example, the generation AI analyzes the user's health data to understand the current situation. For example, it analyzes heart rate and sleep patterns. The generation AI also analyzes the user's activity log to understand the current situation. For example, it analyzes exercise volume and movement history. The generation AI also integrates data from the user's daily life to understand the overall situation. For example, it analyzes food records and stress levels. In this way, by analyzing the user's daily life data, the current situation can be understood more accurately.

[0070] The dialogue unit monitors changes in the user's facial expressions and voice in real time, and is able to understand the user's emotions. For example, the dialogue unit's generation AI analyzes the user's facial expressions in real time to understand their emotions. For example, it uses a camera to detect smiles and tears. The generation AI also analyzes the user's voice in real time to understand their emotions. For example, it uses a microphone to analyze the tone and tempo of the voice. The generation AI also integrates the user's facial expressions and voice to understand their overall emotions. For example, it analyzes changes in facial expressions and voice simultaneously. This allows for a more accurate understanding of emotions by monitoring changes in the user's facial expressions and voice in real time.

[0071] The dialogue unit can use the emotion estimation function to track changes in a user's emotions over the long term and identify emotional patterns. For example, the dialogue unit's generation AI collects the user's emotional data over the long term and identifies emotional patterns. For example, it records daily emotion scores. The generation AI also analyzes changes in the user's emotions and identifies emotional patterns associated with specific events or situations. For example, it finds patterns in which emotions change on specific days of the week or at specific times of the day. The generation AI also integrates the user's emotional data and identifies long-term emotional trends. For example, it analyzes emotional fluctuations that accompany changes in seasons or lifestyle. This makes it possible to identify emotional patterns by tracking emotional changes over the long term.

[0072] The dialogue unit can understand the user's current situation by taking into account environmental data around the user. For example, the generation AI in the dialogue unit analyzes weather data around the user to understand the current situation. For example, it takes into account differences in emotions on rainy days and sunny days. The generation AI also analyzes the user's seasonal data to understand the current situation. For example, it identifies patterns in which emotions change with the change of seasons. The generation AI also integrates environmental data around the user to understand the overall situation. For example, it analyzes changes in temperature and humidity. This allows the dialogue unit to more accurately understand the user's current situation by taking into account environmental data around the user.

[0073] The dialogue unit can understand the user's emotions by referring to the user's past dialogue history and behavioral patterns. For example, the generation AI in the dialogue unit analyzes the user's past dialogue history to understand emotions. For example, it identifies changes in emotions from past conversation content. The generation AI also analyzes the user's behavioral patterns to understand emotions. For example, it identifies emotional patterns related to specific actions or habits. The generation AI also integrates the user's past dialogue history and behavioral patterns to understand overall emotions. For example, it analyzes the relationship between past actions and emotions. This allows the user's emotions to be understood more accurately by referring to past dialogue history and behavioral patterns.

[0074] The dialogue unit can use the emotion estimation function to suggest relaxation and mental care methods according to the user's emotions. For example, the generation AI in the dialogue unit analyzes the user's emotions and suggests relaxation methods. For example, it may suggest meditation or deep breathing when stress is high. The generation AI can also analyze the user's emotions and suggest mental care methods. For example, it may suggest counseling or therapy when feeling depressed. The generation AI can also analyze the user's emotions and provide music or images according to the emotions. For example, it may suggest calming music when you want to relax. In this way, the dialogue unit can provide mental healing by suggesting relaxation and mental care methods according to the user's emotions.

[0075] The dialogue unit can refer to the user's past dialogue history to achieve a consistent dialogue. For example, the dialogue unit's generation AI analyzes the user's past dialogue history to achieve a consistent dialogue. For example, it generates an appropriate response based on the content of the past conversation. The generation AI also refers to the user's past dialogue history to maintain the flow of the dialogue. For example, it starts from a continuation of the previous conversation. The generation AI also integrates the user's past dialogue history to achieve a comprehensive dialogue. For example, it provides new topics based on the content of the past conversation. In this way, a consistent dialogue can be achieved by referring to the past dialogue history.

[0076] The dialogue unit can provide topics based on the user's interests and concerns. For example, the generation AI in the dialogue unit analyzes the user's interests and provides topics for the dialogue. For example, it can provide topics related to the user's favorite hobbies and activities. The generation AI can also analyze the user's interests and customize the content of the dialogue. For example, it can provide news and topics that interest the user. The generation AI can also refer to the user's past dialogue history and provide topics based on the user's interests and concerns. For example, it can provide topics related to what the user has previously said. This allows for more interesting dialogue by providing topics based on the user's interests and concerns.

[0077] The dialogue unit can use the emotion estimation function to adjust the tone and content of the dialogue according to the user's emotions. For example, the generation AI analyzes the user's emotions and adjusts the tone of the dialogue. For example, if the user is sad, it will speak in a gentle tone. The generation AI also analyzes the user's emotions and adjusts the content of the dialogue. For example, if the user is angry, it will speak in a calm manner. The generation AI also analyzes the user's emotions and integrates and adjusts the tone and content of the dialogue. For example, if the user is nervous, it will speak in a relaxing manner. This allows for more appropriate dialogue by adjusting the tone and content of the dialogue according to the user's emotions.

[0078] The dialogue unit can provide a function that allows simultaneous dialogue with multiple recreated family members and friends. For example, the dialogue unit provides a function that allows the generation AI to simultaneously dialogue with multiple recreated family members. For example, it recreates a scene where the whole family gets together. It also provides a function that allows the generation AI to simultaneously dialogue with multiple recreated friends. For example, it recreates a conversation with a group of friends. It also provides a function that allows the generation AI to simultaneously dialogue with multiple recreated family members and friends, providing a realistic dialogue experience. For example, it recreates a family gathering or a party. This allows simultaneous dialogue with multiple recreated family members and friends, providing a more realistic dialogue experience.

[0079] The dialogue unit can provide options that allow the user to customize the content of the dialogue. For example, the dialogue unit may provide options that allow the generation AI to customize the content of the dialogue. For example, the user may select the topic they want to talk about. The dialogue unit may also provide options that allow the generation AI to customize the tone and style of the dialogue. For example, the user may select formal dialogue or casual dialogue. The dialogue unit may also provide options that allow the generation AI to customize the length and frequency of the dialogue. For example, the user may select short conversations or long conversations. This allows the user to customize the content of the dialogue, thereby enabling more personalized dialogue.

[0080] The dialogue unit can use the emotion estimation function to automatically generate dialogue scenarios according to the user's emotions. For example, the dialogue unit uses a generation AI to analyze the user's emotions and automatically generate dialogue scenarios. For example, when the user is sad, it generates a scenario to comfort the user. The generation AI also analyzes the user's emotions and adjusts the dialogue scenario. For example, when the user is happy, it generates a scenario to congratulate the user. The generation AI also analyzes the user's emotions and integrates and adjusts the dialogue scenarios. For example, when the user is nervous, it generates a scenario to relax the user. This allows for more appropriate dialogue by automatically generating dialogue scenarios according to the user's emotions.

[0081] The dialogue unit can refer to the user's past healing experiences and select an effective method. In the dialogue unit, for example, the generation AI analyzes the user's past healing experiences and selects an effective method. For example, it suggests relaxation methods that have been effective in the past. The generation AI also refers to the user's past healing experiences and selects the most appropriate healing method. For example, it provides music or videos that have been effective in the past. The generation AI also integrates the user's past healing experiences and selects a comprehensive healing method. For example, it suggests the most appropriate healing method based on past data. In this way, an effective healing method can be selected by referring to past healing experiences.

[0082] The dialogue unit uses the emotion estimation function to provide music and images that correspond to the user's emotions, promoting mental healing. For example, the dialogue unit uses a generation AI to analyze the user's emotions and provide music that corresponds to the emotions. For example, when you want to relax, it will suggest calm music. The generation AI also analyzes the user's emotions and provides images that correspond to the emotions. For example, it will provide encouraging images when you want to cheer up. The generation AI also analyzes the user's emotions and provides an integrated version of music and images. For example, it will combine music and images according to changes in emotions. In this way, it is possible to promote mental healing by providing music and images that correspond to the emotions.

[0083] The dialogue unit can suggest relaxation methods based on the user's hobbies and interests. For example, the dialogue unit's generation AI analyzes the user's hobbies and suggests relaxation methods based on the hobbies. For example, gardening is suggested for a user who likes gardening. The generation AI also analyzes the user's interests and suggests relaxation methods based on the interests. For example, relaxing books are suggested for a user who likes reading. The generation AI also integrates the user's hobbies and interests to suggest comprehensive relaxation methods. For example, activities based on the hobbies and interests are suggested. This makes it possible to provide more effective healing by suggesting relaxation methods based on hobbies and interests.

[0084] The dialogue unit can provide a community function that allows users to share their emotions with other users. For example, the dialogue unit uses a generation AI to analyze users' emotions and provide a community function that allows emotions to be shared. For example, it connects users who have the same emotions. The generation AI can also analyze users' emotions and provide an online forum where emotions can be shared. For example, it can create topics based on emotions and provide a place where users can interact with each other. The generation AI can also analyze users' emotions and provide a group chat function where emotions can be shared. For example, it can provide a place where users who have the same emotions can interact in real time. In this way, by providing a community function that allows emotions to be shared, it is possible to promote interaction between users.

[0085] The dialogue unit can use the emotion estimation function to suggest collaboration with a mental care specialist based on the user's emotions. For example, the generation AI in the dialogue unit analyzes the user's emotions and suggests a mental care specialist based on the emotions. For example, it may introduce a counselor when stress is high. The generation AI can also analyze the user's emotions and suggest a mental care method based on the emotions. For example, it may introduce a therapist when the user is feeling depressed. The generation AI can also analyze the user's emotions and suggest collaboration with a mental care specialist based on the emotions. For example, it may suggest a session with a specialist based on changes in emotions. This makes it possible to provide more appropriate mental care by suggesting collaboration with a mental care specialist based on the emotions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The reproduction system can also collect the user's health data and adjust the dialogue content based on the user's health condition. For example, it can monitor the user's heart rate and blood pressure and provide advice on how to relax when stress levels are high. It can also analyze the user's sleep patterns and encourage rest when sleep deprivation is present. It can also collect the user's exercise data and suggest light exercise when exercise is lacking. This allows for dialogue tailored to the user's health condition, providing more personalized and effective support.

[0088] The reproduction system can also conduct dialogue based on the user's hobbies and interests. For example, if the user likes music, the reproduced parents or family members can provide topics related to music. If the user is interested in sports, the reproduced family members can talk about the latest sports news and game results. Furthermore, if the user likes traveling, the reproduced parents can share memories of past trips and suggest new travel destinations. This allows for dialogue based on the user's hobbies and interests, providing a more familiar experience.

[0089] The replay system can also refer to the user's past dialogue history to ensure consistent dialogue. For example, the replayed parent can remember the content of the previous dialogue and continue the conversation from where it left off. It can also suggest new topics based on what the user previously discussed. Furthermore, it can analyze the user's past dialogue history and identify specific patterns and interests to provide more appropriate dialogue content. By referencing the past dialogue history, it is possible to achieve consistent dialogue and provide more natural communication.

[0090] The reproduction system can also estimate the user's emotions and provide dialogue content that corresponds to the emotions. For example, if the user is sad, it can offer comforting words. If the user is happy, it can also offer words of joy to share the user's happiness. It can also provide advice to help the user relax when they are tense. This allows for more effective emotional healing by providing dialogue content that corresponds to the user's emotions.

[0091] The reproduction system can also adjust the tone and content of the dialogue based on the user's emotions. For example, if the user is angry, it can speak in a calm tone. If the user is sad, it can speak in a gentle tone. If the user is happy, it can speak in a bright tone. This allows for more appropriate dialogue by adjusting the tone and content of the dialogue according to the user's emotions.

[0092] The reproduction system can also suggest relaxation methods according to the user's emotions. For example, if the user is feeling stressed, it can suggest meditation or deep breathing. It can also provide relaxing music or images when the user is feeling down. It can also suggest light exercise or stretching when the user is tense. In this way, suggesting relaxation methods according to the user's emotions can promote mental healing.

[0093] The reproduction system can also suggest collaboration with mental health professionals based on the user's emotions. For example, if the user is feeling stressed, it can introduce the user to a counselor. If the user is feeling depressed, it can also introduce the user to a therapist. It can also suggest sessions with professionals based on the user's emotional changes. This allows the system to provide more appropriate mental health care by suggesting collaboration with mental health professionals based on the user's emotions.

[0094] The reenactment system can also generate emotional stories based on the user's emotions and provide them to the user. For example, it can provide a story depicting the growth and bonds of a family. It can also provide a documentary looking back on family memories. It can also provide a story that connects family conversations and messages. By generating emotional stories, it can provide the user with a moving experience.

[0095] The reenactment system can also perform reenactments based on the user's memories and episodes. For example, it can reenact the appearance of a deceased parent by reflecting the memories the user recounts. It can also reenact conversations related to specific events. It can also reenact facial expressions and gestures that express specific emotions. This allows for a more personalized and emotional experience by reenacting events based on the user's memories and episodes.

[0096] The reproduction system can also generate multiple patterns of appearances at different ages and in different situations, allowing the user to select from them. For example, it can provide a younger appearance and an older appearance. It can also provide an everyday appearance and an appearance for a special event. It can also provide casual clothes and formal attire. This allows the user to have a variety of options by providing appearances at different ages and in different situations.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The data collection unit collects historical photo, video, and audio data. For example, it can collect digital or analog data provided by the user. It can also collect data provided in a specific format. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI can analyze past photo data and automatically repair degraded parts. The generation AI can also analyze past video data and fill in missing frames. Furthermore, the generation AI can analyze past audio data to remove noise and improve sound quality. Step 3: The reconstructor recreates the appearance and voice of a deceased parent or family member based on the data analyzed by the data analyzer. For example, the generator AI can recreate the appearance of a mother who died 20 years ago, allowing the user to see the face of their grown-up son. Step 4: The dialogue unit uses the appearance and voice reproduced by the reproduction unit to converse with the user. For example, a mother reproduced by the generation AI might respond by saying, "Thank you for showing me how my son has grown."

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 past photos, videos, and audio data; a data analysis unit that analyzes the data collected by the data collection unit; a reproducing unit that reproduces the appearance and voice of the deceased parent or family member based on the data analyzed by the data analyzing unit; a dialogue unit that dialogues with a user using the appearance and voice reproduced by the reproduction unit; A system characterized by:

2. The data analysis unit The system automatically compensates for any degradation or loss of the data, reproducing clearer images and sounds.

2. The system of claim 1.

3. The data analysis unit Considering the time evolution of the data, highlighting the characteristics of a specific period 2. The system of claim 1.

4. The data analysis unit Analyzing the emotional content of the data to identify emotionally significant moments 2. The system of claim 1.

5. The data collection unit We also collect online data from users' social media accounts or blogs to provide more comprehensive information.

2. The system of claim 1.

6. The data collection unit Integrate data provided by other family members and friends to create a richer reconstruction 2. The system of claim 1.

Images