System

Abstract

An object of the system according to the embodiment is to reproduce a dialogue with the deceased so that the user can feel a connection with the deceased.SOLUTION: A system according to an embodiment includes a voice data collection unit, a learning unit, and a dialogue generation unit. The voice data collection unit collects voice data of the deceased. The learning unit learns the voice data collected by the voice data collection unit. The dialogue generation unit generates a dialogue with the user on the basis of the data learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to recreate conversations with the deceased, and there are limited ways to feel connected to the deceased.

[0005] The system according to the embodiment aims to recreate a conversation with a deceased person, allowing the user to feel a connection with the deceased. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice data collection unit, a learning unit, and a dialogue generation unit. The voice data collection unit collects voice data of the deceased. The learning unit learns the voice data collected by the voice data collection unit. The dialogue generation unit generates a dialogue with a user based on the data learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can recreate a conversation with the deceased, allowing the user to feel a connection with the deceased. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Eternal Voice AI system according to an embodiment of the present invention is a system that allows people who have lost family members to re-experience conversations with their deceased loved ones in a digital space. This allows users to re-experience conversations with their deceased loved ones and find peace of mind.

[0029] The Eternal Voice AI system according to the embodiment includes a voice data collection unit, a learning unit, and a dialogue generation unit. The voice data collection unit collects voice data of the deceased. For example, it collects previously recorded conversations of the deceased. The voice data collection unit can also extract voice from video. For example, it extracts the deceased's voice from home videos. The voice data collection unit can also collect voice messages left by the deceased. For example, it collects voice messages left by the deceased. The learning unit learns the voice data collected by the voice data collection unit. For example, the generation AI can learn the tone of voice and speaking style of the deceased using deep learning. The generation AI can also learn the words used by the deceased using a neural network. For example, the generation AI can learn phrases frequently used by the deceased. Furthermore, the generation AI can learn the deceased's unique intonation. For example, it learns the characteristics of the deceased's speaking style. The dialogue generation unit generates a dialogue with a user based on the data learned by the learning unit. For example, when a user asks, "How was your day?", the generation AI generates an answer that the deceased would have said. Also, when a user asks, "Do you have any advice?", the generation AI can generate advice that the deceased would have said. For example, the generation AI generates an answer based on advice that the deceased often gave. Furthermore, when a user asks, "Tell me a memory," the generation AI can recreate a memory that the deceased shared. For example, the generation AI can have the deceased talk about memories with their family. In this way, the Eternal Voice AI system according to the embodiment allows the user to relive a conversation with the deceased. For example, the user can find peace of mind through conversations with the deceased. Furthermore, the user can ease their emotional burden by speaking what they wanted to say to the deceased. Furthermore, by recreating memories with the deceased, the user can feel a connection with the deceased.

[0030] The voice data collection unit collects not only the deceased's voice data, but also text data such as handwritten notes and diaries. The learning unit learns the deceased's thought patterns and emotions based on the voice data and text data. The voice data collection unit, for example, scans the deceased's handwritten notes and diaries and collects them as text data. The generation AI analyzes this text data to learn the deceased's thought patterns and emotional changes. For example, it analyzes the deceased's emotions in response to certain events from the contents of the diary. The voice data collection unit can also collect the deceased's emails. For example, it collects emails sent by the deceased and analyzes them as text data. The voice data collection unit can also collect the deceased's letters. For example, it scans letters written by the deceased and analyzes them as text data. This allows the generation AI to learn the deceased's thought patterns and emotions, thereby generating more realistic dialogue. For example, the generation AI can reproduce the emotions the deceased felt in specific situations. It can also reproduce how the deceased reacted to certain events. Furthermore, the generative AI can also recreate the kind of advice the deceased would have given based on their thought patterns.

[0031] The audio data collection unit also takes into account background and environmental sounds when analyzing the audio data of the deceased. The learning unit reproduces more realistic audio based on the background and environmental sounds. For example, when analyzing the audio data of the deceased, the audio data collection unit takes into account the background and environmental sounds at the time of recording. For example, for audio data of the deceased speaking in a park, it reproduces the chirping of birds and the sound of the wind. The audio data collection unit can also reproduce environmental sounds inside the home for audio data of the deceased speaking inside the home. For example, it reproduces the sound of the television and the voices of family members talking. Furthermore, for audio data of the deceased speaking inside the car, it can reproduce the sound of a car running. For example, it reproduces the sound of an engine and road sounds. The learning unit reproduces more realistic audio based on the background and environmental sounds. For example, the generation AI reproduces more realistic audio by adding background sounds to the audio data of the deceased. The generation AI can also reproduce more realistic audio by adding environmental sounds to the audio data of the deceased. For example, the generation AI reproduces the environmental sounds of the location where the deceased is speaking. Furthermore, the generative AI can also recreate more realistic voices by adding sounds from specific situations to the voice data of the deceased. For example, the generative AI can recreate the voice of the deceased speaking at a specific event. This allows for more realistic voice reproduction by taking into account background and environmental sounds. For example, the user can feel the location and situation in which the deceased is speaking more realistically. The user can also feel the deceased's voice sound more natural. Furthermore, the user can experience a more immersive conversation with the deceased.

[0032] The dialogue generation unit recreates the deceased's favorite music and movie lines and provides them to the user. For example, the dialogue generation unit analyzes the deceased's audio data to identify the deceased's favorite music and movie lines. The generation AI recreates these music and lines and provides them to the user. For example, it recreates famous scenes from movies that the deceased loved. The dialogue generation unit can also recreate the deceased's favorite music. For example, it recreates songs that the deceased loved. Furthermore, the dialogue generation unit can also recreate movie lines that the deceased often quoted. For example, it recreates famous movie lines that the deceased often quoted. In this way, recreating the deceased's favorite music and movie lines can move the user. For example, by listening to the deceased's favorite music, the user can feel memories of the deceased. Furthermore, by listening to movie lines that the deceased often quoted, the user can feel a connection with the deceased. Furthermore, by recreating famous movie scenes that the deceased often quoted, the user can relive special moments with the deceased.

[0033] The dialogue generation unit generates an audio guide for places and events frequently visited by the deceased and provides it to the user. For example, the dialogue generation unit analyzes the voice data of the deceased to identify places and events frequently visited by the deceased. The generation AI generates audio guides for these places and events and provides them to the user. For example, it recreates an audio guide for a park frequently visited by the deceased. The dialogue generation unit can also recreate an audio guide for an event frequently attended by the deceased. For example, it recreates an audio guide for a concert frequently attended by the deceased. Furthermore, the dialogue generation unit can also recreate an audio guide for a tourist spot frequently visited by the deceased. For example, it recreates an audio guide for a tourist spot frequently visited by the deceased. In this way, generating an audio guide for places and events frequently visited by the deceased allows the user to relive memories of the deceased. For example, by listening to an audio guide for a place frequently visited by the deceased, the user can feel memories of the deceased. Furthermore, by listening to an audio guide for an event frequently attended by the deceased, the user can feel a connection with the deceased. Furthermore, by listening to an audio guide for a tourist spot frequently visited by the deceased, the user can relive special moments with the deceased.

[0034] The dialogue generation unit analyzes the user's past dialogue history and performs dialogue simulations based on the user's preferences and interests. For example, the dialogue generation unit collects the user's past dialogue history, which the generation AI analyzes. The dialogue generation unit identifies the user's preferences and interests and performs dialogue simulations based on them. For example, the dialogue generation unit generates dialogues based on themes frequently discussed by the user. The dialogue generation unit can also generate dialogues based on topics the user has shown interest in in the past. For example, the dialogue generation unit can generate dialogues based on the user's past hobbies and interests. Furthermore, the dialogue generation unit can reproduce the user's preferred dialogue style based on the user's past dialogue history. For example, it can reproduce the user's preferred dialogue tempo and tone. This enables more personalized dialogues by performing dialogue simulations based on the user's preferences and interests. For example, the user can enjoy dialogues that match their interests. The user can also experience dialogues with a deceased loved one in a dialogue style that matches their preferences. Furthermore, the user can gain new discoveries and insights through dialogues based on their interests.

[0035] The dialogue generation unit learns the user's voice tone and speaking style and adjusts the voice of the deceased so that it blends naturally with the user's voice. For example, the dialogue generation unit collects the user's voice tone and speaking style, and the generation AI learns it. The dialogue generation unit adjusts the voice of the deceased so that it blends naturally with the user's voice. For example, the dialogue generation unit adjusts the voice of the deceased to match the tone of the user's voice. The dialogue generation unit can also adjust the voice of the deceased to match the user's speaking style. For example, the dialogue generation unit adjusts the voice of the deceased to match the user's speaking style. For example, the dialogue generation unit adjusts the voice of the deceased to match the pitch and tone of the user's voice. This allows the dialogue generation unit to learn the user's voice tone and speaking style and adjust the voice to blend naturally with the voice of the deceased, enabling a more natural dialogue. For example, the user can feel that the voice of the deceased is in harmony with their own voice. Furthermore, the user can experience a more realistic dialogue with the deceased.

[0036] The dialogue generation unit adds a function that allows the user to record what the user learned through the dialogue with the deceased and review it later. The dialogue generation unit adds, for example, a function that automatically records what the user learned through the dialogue with the deceased. For example, the dialogue content may be saved as text data so that the user can review it later. The dialogue generation unit may also summarize and record what the user learned. For example, the dialogue generation unit may extract key points from the dialogue and save them as a summary. The dialogue generation unit may also organize and record what the user learned. For example, the dialogue generation unit may organize what the user learned by category so that the user can easily review it later. This allows the user to record what they learned through the dialogue with the deceased and review it later, thereby deepening their learning. For example, the user may review the knowledge and insights they gained through the dialogue later. Furthermore, by organizing and recording what they learned, the user can efficiently deepen their learning. Furthermore, by summarizing and recording the key points of the dialogue, the user can easily review important points.

[0037] The dialogue generation unit provides a function that allows a user to share a dialogue with the deceased with other family members and friends. The dialogue generation unit provides, for example, a function that allows a user to share a dialogue with the deceased with other family members and friends. For example, the dialogue content can be generated as a sharing link and shared with other people. The dialogue generation unit can also provide a function to share the dialogue content on social media. For example, the dialogue content can be posted on social media and shared with other people. The dialogue generation unit can also provide a function to share the dialogue content by email. For example, the dialogue content can be sent by email and shared with other people. This allows a user to share a dialogue with the deceased with other family members and friends, thereby allowing them to share empathy and memories. For example, a user can share with other people the emotions and realizations they gained through a dialogue with the deceased. Furthermore, a user can gain empathy by sharing memories they gained through a dialogue with the deceased with other people. Furthermore, a user can feel a connection with the deceased by sharing a dialogue with the deceased with other people.

[0038] The dialogue generation unit utilizes the knowledge and experience of the deceased to generate a dialogue that allows the user to make new discoveries and gain new insights. The dialogue generation unit generates a dialogue that allows the user to make new discoveries and gain new insights, for example, based on the knowledge and experience of the deceased. For example, the dialogue generation unit utilizes the specialized knowledge of the deceased to provide the user with new information. The dialogue generation unit can also generate a dialogue that allows the user to gain a new perspective based on the experiences of the deceased. For example, it recreates events experienced by the deceased and explains the background to the user. Furthermore, the dialogue generation unit can also generate a dialogue that allows the user to obtain hints for problem-solving based on the knowledge and experience of the deceased. For example, it recreates problems that the deceased faced in the past and how they were solved, and provides advice to the user. In this way, the user can make new discoveries and gain new insights by utilizing the knowledge and experience of the deceased. For example, the user can learn new information through the specialized knowledge of the deceased. The user can also gain a new perspective through the experiences of the deceased. The user can also obtain hints for problem-solving based on the knowledge and experience of the deceased.

[0039] The dialogue generation unit recreates the philosophy and beliefs of the deceased and generates dialogue that allows the user to gain a deeper understanding of the deceased's values ​​and outlook on life. The dialogue generation unit generates dialogue that allows the user to gain a deeper understanding of the deceased's values, for example, based on the deceased's philosophy and beliefs. For example, the dialogue generation unit recreates the beliefs and values ​​that the deceased held dear and explains the background to the user. The dialogue generation unit can also generate dialogue that allows the user to gain a deeper understanding of the deceased's outlook on life, based on the deceased's outlook on life. For example, the dialogue generation unit recreates the outlook on life that the deceased held and explains its significance to the user. Furthermore, the dialogue generation unit can also generate dialogue that allows the user to understand the deceased's values, based on the deceased's philosophy and beliefs. For example, the dialogue generation unit explains the values ​​to the user based on the philosophy and beliefs that the deceased held. In this way, by recreating the deceased's philosophy and beliefs, the user can gain a deeper understanding of the deceased's values ​​and outlook on life. For example, the user can understand the deceased's outlook on life through the deceased's beliefs and values. The user can also understand the deceased's way of life through the deceased's philosophy and beliefs.

[0040] The dialogue generation unit adds a function to rediscover the hobbies and interests of the deceased and suggest information and activities related to them. The dialogue generation unit generates dialogue to enable the user to discover new information and activities based on, for example, the hobbies and interests of the deceased. For example, it provides the latest information related to the hobbies that the deceased enjoyed. The dialogue generation unit can also suggest new activities to the user based on the interests of the deceased. For example, it can suggest events and activities related to fields that the deceased was interested in. Furthermore, the dialogue generation unit can generate dialogue to enable the user to acquire new knowledge based on the hobbies and interests of the deceased. For example, it provides knowledge related to the hobbies that the deceased had. This allows the user to rediscover the hobbies and interests of the deceased and suggest information and activities related to them, thereby developing new interests. For example, the user can acquire new knowledge through the latest information related to the hobbies of the deceased. The user can also have new experiences through activities related to the interests of the deceased. The user can also make new discoveries through the hobbies and interests of the deceased.

[0041] The dialogue generation unit recreates important events in the deceased's life in detail and generates dialogues that allow the user to relive those events. For example, the dialogue generation unit generates dialogues based on important events in the deceased's life to allow the user to relive those events. For example, the dialogue generation unit recreates what the deceased said at their wedding, allowing the user to relive that scene. The dialogue generation unit can also allow the user to relive important events experienced by the deceased in detail based on those events. For example, the dialogue generation unit recreates an important project the deceased completed at work. Furthermore, the dialogue generation unit can allow the user to relive a moving event experienced by the deceased. For example, the dialogue generation unit recreates a special moment the deceased spent with their family. In this way, by recreating important events in the deceased's life in detail, the user can relive those events. For example, by reliving the deceased's wedding scene, the user can feel the special moments with the deceased. Furthermore, by reliving an important project the deceased completed, the user can feel the deceased's efforts. Furthermore, by reliving special moments the deceased spent with their family, the user can feel a connection with the deceased.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Eternal Voice AI system may also include a health management unit that monitors the user's health. For example, it may measure the user's heart rate and stress level and monitor the user's health in real time while interacting with the deceased. The health management unit may also provide relaxation guidance in the deceased's voice to help the user relax. For example, the deceased's voice may guide the user through deep breathing or meditation. The health management unit may also provide health advice in the deceased's voice based on the user's health. For example, when the user is feeling stressed, the deceased's voice may advise the user on how to relax. This allows the user to not only find peace of mind through interaction with the deceased, but also manage their health. For example, the user may reduce stress by relaxing through the deceased's voice. Furthermore, the user may improve their health by receiving health advice through the deceased's voice. Furthermore, by managing their health through interaction with the deceased, the user may live a more fulfilling life.

[0044] The Eternal Voice AI system can also include a lifestyle rhythm adjustment unit that analyzes the user's lifestyle rhythm and provides advice to regulate the lifestyle rhythm in the voice of the deceased. For example, if the user tends to stay up late, the voice of the deceased can provide advice encouraging early bedtime and early rise. Furthermore, if the user has dietary problems, the lifestyle rhythm adjustment unit can also provide advice encouraging a balanced diet in the voice of the deceased. For example, if the user has an unbalanced diet, the voice of the deceased can provide advice encouraging a balanced diet. Furthermore, if the user is not getting enough exercise, the lifestyle rhythm adjustment unit can also provide advice encouraging exercise in the voice of the deceased. For example, if the user is not getting enough exercise, the voice of the deceased can provide advice encouraging exercise. This allows the user to regulate their lifestyle rhythm through dialogue with the deceased. For example, the voice of the deceased can encourage the user to go to bed early and get up early, thereby regulating their lifestyle rhythm. Furthermore, the voice of the deceased can encourage the user to eat a balanced diet, thereby helping the user to live a healthy diet. Furthermore, the voice of the deceased can encourage the user to exercise, thereby helping the user to develop an exercise habit.

[0045] The Eternal Voice AI system may also include a hobby suggestion unit that analyzes the user's hobbies and interests and suggests new hobbies and interests using the deceased's voice. For example, if the user is looking for a new hobby, the deceased's voice may suggest recommended hobbies. The hobby suggestion unit may also provide information related to the user's areas of interest. For example, it may suggest events or activities related to the user's areas of interest. The hobby suggestion unit may also help the user rediscover hobbies that the user enjoyed in the past. For example, the deceased's voice may suggest that the user resume a hobby that the user enjoyed in the past. This allows the user to discover new hobbies and interests through dialogue with the deceased. For example, the user may find new enjoyment by hearing new hobbies suggested by the deceased's voice. The user may also gain new knowledge by hearing information related to the user's areas of interest by hearing the deceased's voice. The user may also rediscover and enjoy hobbies that the user enjoyed in the past by hearing the deceased's voice.

[0046] The Eternal Voice AI system may also include a learning support unit that supports the user's learning. For example, if the user wants to learn a new skill, the learning support unit may provide a learning guide in the deceased's voice. The learning support unit may also provide related information in the deceased's voice if the user wants to deepen their knowledge in a particular field. For example, the learning support unit may suggest learning resources related to the user's field of interest. Furthermore, the learning support unit may record the user's learning progress and provide feedback in the deceased's voice. For example, the user may record their learning progress and receive encouraging feedback in the deceased's voice. This allows the user to receive learning support through dialogue with the deceased. For example, the user may learn new skills efficiently by receiving learning guidance in the deceased's voice. The user may also deepen their knowledge by receiving related information in the deceased's voice. Furthermore, the user may maintain their motivation to learn by receiving feedback in the deceased's voice.

[0047] The Eternal Voice AI system may further include a creativity support unit that stimulates the user's creativity. For example, the unit may provide brainstorming guidance in the deceased's voice to help the user generate new ideas. The creativity support unit may also provide inspiration in the deceased's voice when the user is working on a creative project. For example, the unit may provide inspiration in the deceased's voice when the user is working on an art or design project. The creativity support unit may also provide advice in the deceased's voice when the user is solving creative problems. For example, the unit may provide advice in the deceased's voice when the user is thinking of ideas for a new project. This allows the user to be inspired by the brainstorming guidance in the deceased's voice. The user may also be inspired by the deceased's voice to progress with creative projects. The user may also be able to solve creative problems by receiving advice in the deceased's voice.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The audio data collection unit collects audio data of the deceased. For example, it collects past recorded conversations of the deceased. The audio data collection unit can also extract audio from video. For example, it can extract the voice of the deceased from home video. Furthermore, the audio data collection unit can also collect audio messages left by the deceased. For example, it can collect voice messages left by the deceased. Step 2: The learning unit learns the voice data collected by the voice data collection unit. For example, the generation AI uses deep learning to learn the tone of voice and speaking style of the deceased. The generation AI can also learn the words used by the deceased using a neural network. For example, the generation AI learns the phrases that the deceased often used. Furthermore, the generation AI can also learn the unique intonation of the deceased. For example, it learns the characteristics of the deceased's speaking style. Step 3: The dialogue generation unit generates a dialogue with the user based on the data learned by the learning unit. For example, when the user asks, "How was your day?", the generation AI generates an answer that the deceased would have said when they were alive. Also, when the user asks, "Do you have any advice?", the generation AI can generate advice that the deceased would have said when they were alive. For example, the generation AI generates an answer based on advice that the deceased often gave. Furthermore, when the user asks, "Tell me about your memories," the generation AI can recreate memories that the deceased shared when they were alive. For example, the generation AI will have the deceased talk about memories they had with their family.

[0050] (Example 2) The Eternal Voice AI system according to an embodiment of the present invention is a system that allows people who have lost family members to re-experience conversations with their deceased loved ones in a digital space. This allows users to re-experience conversations with their deceased loved ones and find peace of mind.

[0051] The Eternal Voice AI system according to the embodiment includes a voice data collection unit, a learning unit, and a dialogue generation unit. The voice data collection unit collects voice data of the deceased. For example, it collects previously recorded conversations of the deceased. The voice data collection unit can also extract voice from video. For example, it extracts the deceased's voice from home videos. The voice data collection unit can also collect voice messages left by the deceased. For example, it collects voice messages left by the deceased. The learning unit learns the voice data collected by the voice data collection unit. For example, the generation AI can learn the tone of voice and speaking style of the deceased using deep learning. The generation AI can also learn the words used by the deceased using a neural network. For example, the generation AI can learn phrases frequently used by the deceased. Furthermore, the generation AI can learn the deceased's unique intonation. For example, it learns the characteristics of the deceased's speaking style. The dialogue generation unit generates a dialogue with a user based on the data learned by the learning unit. For example, when a user asks, "How was your day?", the generation AI generates an answer that the deceased would have said. Also, when a user asks, "Do you have any advice?", the generation AI can generate advice that the deceased would have said. For example, the generation AI generates an answer based on advice that the deceased often gave. Furthermore, when a user asks, "Tell me a memory," the generation AI can recreate a memory that the deceased shared. For example, the generation AI can have the deceased talk about memories with their family. In this way, the Eternal Voice AI system according to the embodiment allows the user to relive a conversation with the deceased. For example, the user can find peace of mind through conversations with the deceased. Furthermore, the user can ease their emotional burden by speaking what they wanted to say to the deceased. Furthermore, by recreating memories with the deceased, the user can feel a connection with the deceased.

[0052] The voice data collection unit collects not only the deceased's voice data, but also text data such as handwritten notes and diaries. The learning unit learns the deceased's thought patterns and emotions based on the voice data and text data. The voice data collection unit, for example, scans the deceased's handwritten notes and diaries and collects them as text data. The generation AI analyzes this text data to learn the deceased's thought patterns and emotional changes. For example, it analyzes the deceased's emotions in response to certain events from the contents of the diary. The voice data collection unit can also collect the deceased's emails. For example, it collects emails sent by the deceased and analyzes them as text data. The voice data collection unit can also collect the deceased's letters. For example, it scans letters written by the deceased and analyzes them as text data. This allows the generation AI to learn the deceased's thought patterns and emotions, thereby generating more realistic dialogue. For example, the generation AI can reproduce the emotions the deceased felt in specific situations. It can also reproduce how the deceased reacted to certain events. Furthermore, the generative AI can also recreate the kind of advice the deceased would have given based on their thought patterns.

[0053] The audio data collection unit also takes into account background and environmental sounds when analyzing the audio data of the deceased. The learning unit reproduces more realistic audio based on the background and environmental sounds. For example, when analyzing the audio data of the deceased, the audio data collection unit takes into account the background and environmental sounds at the time of recording. For example, for audio data of the deceased speaking in a park, it reproduces the chirping of birds and the sound of the wind. The audio data collection unit can also reproduce environmental sounds inside the home for audio data of the deceased speaking inside the home. For example, it reproduces the sound of the television and the voices of family members talking. Furthermore, for audio data of the deceased speaking inside the car, it can reproduce the sound of a car running. For example, it reproduces the sound of an engine and road sounds. The learning unit reproduces more realistic audio based on the background and environmental sounds. For example, the generation AI reproduces more realistic audio by adding background sounds to the audio data of the deceased. The generation AI can also reproduce more realistic audio by adding environmental sounds to the audio data of the deceased. For example, the generation AI reproduces the environmental sounds of the location where the deceased is speaking. Furthermore, the generative AI can also recreate more realistic voices by adding sounds from specific situations to the voice data of the deceased. For example, the generative AI can recreate the voice of the deceased speaking at a specific event. This allows for more realistic voice reproduction by taking into account background and environmental sounds. For example, the user can feel the location and situation in which the deceased is speaking more realistically. The user can also feel the deceased's voice sound more natural. Furthermore, the user can experience a more immersive conversation with the deceased.

[0054] The learning unit uses the emotion estimation function to learn emotional changes from the deceased's voice data. The dialogue generation unit reproduces a speaking style that corresponds to the emotion based on the emotional changes. For example, the learning unit analyzes the deceased's voice data and identifies emotional changes using the emotion estimation function. The generation AI reproduces a speaking style that corresponds to the deceased's emotion based on this emotional data. For example, it reproduces the tone of voice and speaking style of the deceased when they are happy. The learning unit can also reproduce the tone of voice and speaking style of the deceased when they are sad. For example, it reproduces the tone of voice of the deceased when they are sad. The learning unit can also reproduce the tone of voice and speaking style of the deceased when they are angry. For example, it reproduces the tone of voice of the deceased when they are angry. The dialogue generation unit reproduces a speaking style that corresponds to the emotion based on the emotional changes. For example, the generation AI generates a dialogue based on the tone of voice and speaking style of the deceased when they are happy. The generation AI can also generate a dialogue based on the tone of voice and speaking style of the deceased when they are sad. For example, the generation AI generates a dialogue based on the tone of voice of the deceased when they are sad. Furthermore, the generation AI can also generate dialogue based on the tone of voice and speaking style of the deceased when they were angry. For example, the generation AI generates dialogue based on the tone of voice of the deceased when they were angry. This allows for more emotional dialogue by reproducing the speaking style according to changes in emotion. For example, the user can feel the emotions of the deceased more realistically. The user can also experience dialogue that is in tune with the emotions of the deceased. Furthermore, by sensing the changes in the emotions of the deceased, the user can feel a deeper connection with the deceased.

[0055] The dialogue generation unit recreates the deceased's favorite music and movie lines and provides them to the user. For example, the dialogue generation unit analyzes the deceased's audio data to identify the deceased's favorite music and movie lines. The generation AI recreates these music and lines and provides them to the user. For example, it recreates famous scenes from movies that the deceased loved. The dialogue generation unit can also recreate the deceased's favorite music. For example, it recreates songs that the deceased loved. Furthermore, the dialogue generation unit can also recreate movie lines that the deceased often quoted. For example, it recreates famous movie lines that the deceased often quoted. In this way, recreating the deceased's favorite music and movie lines can move the user. For example, by listening to the deceased's favorite music, the user can feel memories of the deceased. Furthermore, by listening to movie lines that the deceased often quoted, the user can feel a connection with the deceased. Furthermore, by recreating famous movie scenes that the deceased often quoted, the user can relive special moments with the deceased.

[0056] The dialogue generation unit generates an audio guide for places and events frequently visited by the deceased and provides it to the user. For example, the dialogue generation unit analyzes the voice data of the deceased to identify places and events frequently visited by the deceased. The generation AI generates audio guides for these places and events and provides them to the user. For example, it recreates an audio guide for a park frequently visited by the deceased. The dialogue generation unit can also recreate an audio guide for an event frequently attended by the deceased. For example, it recreates an audio guide for a concert frequently attended by the deceased. Furthermore, the dialogue generation unit can also recreate an audio guide for a tourist spot frequently visited by the deceased. For example, it recreates an audio guide for a tourist spot frequently visited by the deceased. In this way, generating an audio guide for places and events frequently visited by the deceased allows the user to relive memories of the deceased. For example, by listening to an audio guide for a place frequently visited by the deceased, the user can feel memories of the deceased. Furthermore, by listening to an audio guide for an event frequently attended by the deceased, the user can feel a connection with the deceased. Furthermore, by listening to an audio guide for a tourist spot frequently visited by the deceased, the user can relive special moments with the deceased.

[0057] The dialogue generation unit analyzes the user's past dialogue history and performs dialogue simulations based on the user's preferences and interests. For example, the dialogue generation unit collects the user's past dialogue history, which the generation AI analyzes. The dialogue generation unit identifies the user's preferences and interests and performs dialogue simulations based on them. For example, the dialogue generation unit generates dialogues based on themes frequently discussed by the user. The dialogue generation unit can also generate dialogues based on topics the user has shown interest in in the past. For example, the dialogue generation unit can generate dialogues based on the user's past hobbies and interests. Furthermore, the dialogue generation unit can reproduce the user's preferred dialogue style based on the user's past dialogue history. For example, it can reproduce the user's preferred dialogue tempo and tone. This enables more personalized dialogues by performing dialogue simulations based on the user's preferences and interests. For example, the user can enjoy dialogues that match their interests. The user can also experience dialogues with a deceased loved one in a dialogue style that matches their preferences. Furthermore, the user can gain new discoveries and insights through dialogues based on their interests.

[0058] The dialogue generation unit learns the user's voice tone and speaking style and adjusts the voice of the deceased so that it blends naturally with the user's voice. For example, the dialogue generation unit collects the user's voice tone and speaking style, and the generation AI learns it. The dialogue generation unit adjusts the voice of the deceased so that it blends naturally with the user's voice. For example, the dialogue generation unit adjusts the voice of the deceased to match the tone of the user's voice. The dialogue generation unit can also adjust the voice of the deceased to match the user's speaking style. For example, the dialogue generation unit adjusts the voice of the deceased to match the user's speaking style. For example, the dialogue generation unit adjusts the voice of the deceased to match the pitch and tone of the user's voice. This allows the dialogue generation unit to learn the user's voice tone and speaking style and adjust the voice to blend naturally with the voice of the deceased, enabling a more natural dialogue. For example, the user can feel that the voice of the deceased is in harmony with their own voice. Furthermore, the user can experience a more realistic dialogue with the deceased.

[0059] The dialogue generation unit uses the emotion estimation function to analyze the user's emotional state in real time and generate dialogue content accordingly. The dialogue generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. The generation AI uses this data to generate dialogue content according to the user's emotions. For example, it provides words of comfort when the user is sad. The dialogue generation unit can also provide words of congratulations when the user is happy. For example, it provides words of congratulations when the user is celebrating a particular event. Furthermore, the dialogue generation unit can also provide advice when the user is in trouble. For example, it provides advice when the user is facing a problem. This allows for a dialogue that is sensitive to the user's emotions by analyzing the user's emotional state in real time and generating dialogue content accordingly. For example, the user can experience a dialogue that is sensitive to their emotions. Furthermore, the user can find peace of mind through a dialogue that is sensitive to their emotions. Furthermore, the user can receive help in solving problems through a dialogue that is sensitive to their emotions.

[0060] The dialogue generation unit adds a function that allows the user to record what the user learned through the dialogue with the deceased and review it later. The dialogue generation unit adds, for example, a function that automatically records what the user learned through the dialogue with the deceased. For example, the dialogue content may be saved as text data so that the user can review it later. The dialogue generation unit may also summarize and record what the user learned. For example, the dialogue generation unit may extract key points from the dialogue and save them as a summary. The dialogue generation unit may also organize and record what the user learned. For example, the dialogue generation unit may organize what the user learned by category so that the user can easily review it later. This allows the user to record what they learned through the dialogue with the deceased and review it later, thereby deepening their learning. For example, the user may review the knowledge and insights they gained through the dialogue later. Furthermore, by organizing and recording what they learned, the user can efficiently deepen their learning. Furthermore, by summarizing and recording the key points of the dialogue, the user can easily review important points.

[0061] The dialogue generation unit provides a function that allows a user to share a dialogue with the deceased with other family members and friends. The dialogue generation unit provides, for example, a function that allows a user to share a dialogue with the deceased with other family members and friends. For example, the dialogue content can be generated as a sharing link and shared with other people. The dialogue generation unit can also provide a function to share the dialogue content on social media. For example, the dialogue content can be posted on social media and shared with other people. The dialogue generation unit can also provide a function to share the dialogue content by email. For example, the dialogue content can be sent by email and shared with other people. This allows a user to share a dialogue with the deceased with other family members and friends, thereby allowing them to share empathy and memories. For example, a user can share with other people the emotions and realizations they gained through a dialogue with the deceased. Furthermore, a user can gain empathy by sharing memories they gained through a dialogue with the deceased with other people. Furthermore, a user can feel a connection with the deceased by sharing a dialogue with the deceased with other people.

[0062] The dialogue generation unit adds a function to use the emotion estimation function to record emotions felt by the user during a dialogue and analyze changes in emotions later. The dialogue generation unit adds a function to use the emotion estimation function to record emotions felt by the user during a dialogue in real time. For example, the dialogue generation unit may analyze the user's facial expressions and voice and record an emotion score. The dialogue generation unit may also record changes in emotions felt by the user during a dialogue. For example, the dialogue generation unit may compare emotion scores at the start and end of the dialogue and record changes in emotions. The dialogue generation unit may also visualize the emotions felt by the user during a dialogue in a graph or chart. For example, the dialogue generation unit may display changes in emotions in a graph along a time axis. This allows the user to record the emotions felt during a dialogue and analyze the changes in emotions later, making it easier to understand changes in emotions. For example, the user can look back on changes in their emotions throughout the dialogue. Furthermore, by visualizing changes in emotions, the user can understand patterns of emotions. Furthermore, by analyzing changes in emotions, the user can grasp trends in their emotions.

[0063] The dialogue generation unit utilizes the knowledge and experience of the deceased to generate a dialogue that allows the user to make new discoveries and gain new insights. The dialogue generation unit generates a dialogue that allows the user to make new discoveries and gain new insights, for example, based on the knowledge and experience of the deceased. For example, the dialogue generation unit utilizes the specialized knowledge of the deceased to provide the user with new information. The dialogue generation unit can also generate a dialogue that allows the user to gain a new perspective based on the experiences of the deceased. For example, it recreates events experienced by the deceased and explains the background to the user. Furthermore, the dialogue generation unit can also generate a dialogue that allows the user to obtain hints for problem-solving based on the knowledge and experience of the deceased. For example, it recreates problems that the deceased faced in the past and how they were solved, and provides advice to the user. In this way, the user can make new discoveries and gain new insights by utilizing the knowledge and experience of the deceased. For example, the user can learn new information through the specialized knowledge of the deceased. The user can also gain a new perspective through the experiences of the deceased. The user can also obtain hints for problem-solving based on the knowledge and experience of the deceased.

[0064] The dialogue generation unit recreates the philosophy and beliefs of the deceased and generates dialogue that allows the user to gain a deeper understanding of the deceased's values ​​and outlook on life. The dialogue generation unit generates dialogue that allows the user to gain a deeper understanding of the deceased's values, for example, based on the deceased's philosophy and beliefs. For example, the dialogue generation unit recreates the beliefs and values ​​that the deceased held dear and explains the background to the user. The dialogue generation unit can also generate dialogue that allows the user to gain a deeper understanding of the deceased's outlook on life, based on the deceased's outlook on life. For example, the dialogue generation unit recreates the outlook on life that the deceased held and explains its significance to the user. Furthermore, the dialogue generation unit can also generate dialogue that allows the user to understand the deceased's values, based on the deceased's philosophy and beliefs. For example, the dialogue generation unit explains the values ​​to the user based on the philosophy and beliefs that the deceased held. In this way, by recreating the deceased's philosophy and beliefs, the user can gain a deeper understanding of the deceased's values ​​and outlook on life. For example, the user can understand the deceased's outlook on life through the deceased's beliefs and values. The user can also understand the deceased's way of life through the deceased's philosophy and beliefs.

[0065] The dialogue generation unit adds a function to rediscover the hobbies and interests of the deceased and suggest information and activities related to them. The dialogue generation unit generates dialogue to enable the user to discover new information and activities based on, for example, the hobbies and interests of the deceased. For example, it provides the latest information related to the hobbies that the deceased enjoyed. The dialogue generation unit can also suggest new activities to the user based on the interests of the deceased. For example, it can suggest events and activities related to fields that the deceased was interested in. Furthermore, the dialogue generation unit can generate dialogue to enable the user to acquire new knowledge based on the hobbies and interests of the deceased. For example, it provides knowledge related to the hobbies that the deceased had. This allows the user to rediscover the hobbies and interests of the deceased and suggest information and activities related to them, thereby developing new interests. For example, the user can acquire new knowledge through the latest information related to the hobbies of the deceased. The user can also have new experiences through activities related to the interests of the deceased. The user can also make new discoveries through the hobbies and interests of the deceased.

[0066] The dialogue generation unit recreates important events in the deceased's life in detail and generates dialogues that allow the user to relive those events. For example, the dialogue generation unit generates dialogues based on important events in the deceased's life to allow the user to relive those events. For example, the dialogue generation unit recreates what the deceased said at their wedding, allowing the user to relive that scene. The dialogue generation unit can also allow the user to relive important events experienced by the deceased in detail based on those events. For example, the dialogue generation unit recreates an important project the deceased completed at work. Furthermore, the dialogue generation unit can allow the user to relive a moving event experienced by the deceased. For example, the dialogue generation unit recreates a special moment the deceased spent with their family. In this way, by recreating important events in the deceased's life in detail, the user can relive those events. For example, by reliving the deceased's wedding scene, the user can feel the special moments with the deceased. Furthermore, by reliving an important project the deceased completed, the user can feel the deceased's efforts. Furthermore, by reliving special moments the deceased spent with their family, the user can feel a connection with the deceased.

[0067] The dialogue generation unit uses the emotion estimation function to provide a function that allows the user to share the emotions felt through the dialogue with the deceased with other family members and friends and gain sympathy. The dialogue generation unit, for example, uses the emotion estimation function to provide a function that allows the user to record the emotions felt through the dialogue with the deceased and share them with other family members and friends. For example, the dialogue generation unit generates an emotion score as a sharing link and shares it with other people. The dialogue generation unit can also provide a function that allows the user to share the emotions felt on social media. For example, the emotion score can be posted on social media and shared with other people. The dialogue generation unit can also provide a function that allows the user to share the emotions felt by email. For example, the emotion score can be sent by email and shared with other people. This allows the user to share the emotions felt through the dialogue with the deceased with other family members and friends and gain sympathy. For example, the user can gain sympathy by sharing the emotions felt through the dialogue with the deceased with other people. The user can also share memories by sharing the emotions felt through the dialogue with the deceased with other people. Furthermore, the user can feel a connection with the deceased by sharing the emotions felt through the dialogue with the deceased with other people.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The Eternal Voice AI system may also include a health management unit that monitors the user's health. For example, it may measure the user's heart rate and stress level and monitor the user's health in real time while interacting with the deceased. The health management unit may also provide relaxation guidance in the deceased's voice to help the user relax. For example, the deceased's voice may guide the user through deep breathing or meditation. The health management unit may also provide health advice in the deceased's voice based on the user's health. For example, when the user is feeling stressed, the deceased's voice may advise the user on how to relax. This allows the user to not only find peace of mind through interaction with the deceased, but also manage their health. For example, the user may reduce stress by relaxing through the deceased's voice. Furthermore, the user may improve their health by receiving health advice through the deceased's voice. Furthermore, by managing their health through interaction with the deceased, the user may live a more fulfilling life.

[0070] The Eternal Voice AI system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the dialogue content based on the estimated emotions. For example, when the user is sad, the emotion adjustment unit may provide words of comfort in the voice of the deceased. The emotion adjustment unit may also provide words of congratulations in the voice of the deceased when the user is happy. For example, when the user is celebrating a particular event, the emotion adjustment unit may provide words of congratulations in the voice of the deceased. The emotion adjustment unit may also provide advice in the voice of the deceased when the user is in trouble. For example, when the user is facing a problem, the emotion adjustment unit may provide advice in the voice of the deceased. This allows the user to experience a dialogue that is sensitive to their emotions through a dialogue with the deceased. For example, the user may find peace of mind by experiencing a dialogue that is sensitive to their emotions. Furthermore, the user may receive help in solving problems through a dialogue that is sensitive to their emotions. Furthermore, the user may feel a deeper connection with the deceased through a dialogue that is sensitive to their emotions.

[0071] The Eternal Voice AI system can also include a lifestyle rhythm adjustment unit that analyzes the user's lifestyle rhythm and provides advice to regulate the lifestyle rhythm in the voice of the deceased. For example, if the user tends to stay up late, the voice of the deceased can provide advice encouraging early bedtime and early rise. Furthermore, if the user has dietary problems, the lifestyle rhythm adjustment unit can also provide advice encouraging a balanced diet in the voice of the deceased. For example, if the user has an unbalanced diet, the voice of the deceased can provide advice encouraging a balanced diet. Furthermore, if the user is not getting enough exercise, the lifestyle rhythm adjustment unit can also provide advice encouraging exercise in the voice of the deceased. For example, if the user is not getting enough exercise, the voice of the deceased can provide advice encouraging exercise. This allows the user to regulate their lifestyle rhythm through dialogue with the deceased. For example, the voice of the deceased can encourage the user to go to bed early and get up early, thereby regulating their lifestyle rhythm. Furthermore, the voice of the deceased can encourage the user to eat a balanced diet, thereby helping the user to live a healthy diet. Furthermore, the voice of the deceased can encourage the user to exercise, thereby helping the user to develop an exercise habit.

[0072] The Eternal Voice AI system may further include a relaxation unit that estimates the user's emotions and provides relaxation guidance in the voice of the deceased based on the estimated emotions. For example, when the user is feeling stressed, the relaxation unit may guide the user through deep breathing or meditation in the voice of the deceased. The relaxation unit may also provide advice on relaxation methods in the voice of the deceased when the user is feeling anxious. For example, when the user is feeling anxious, the relaxation unit may provide advice on relaxation methods in the voice of the deceased. The relaxation unit may also provide advice on refreshing methods in the voice of the deceased when the user is tired. This allows the user to experience relaxation through dialogue with the deceased. For example, the user may be able to reduce stress by receiving guidance on deep breathing or meditation in the voice of the deceased. The user may also be able to reduce anxiety by receiving advice on relaxation methods in the voice of the deceased. The user may also be able to soothe fatigue by receiving advice on refreshing methods in the voice of the deceased.

[0073] The Eternal Voice AI system may also include a hobby suggestion unit that analyzes the user's hobbies and interests and suggests new hobbies and interests using the deceased's voice. For example, if the user is looking for a new hobby, the deceased's voice may suggest recommended hobbies. The hobby suggestion unit may also provide information related to the user's areas of interest. For example, it may suggest events or activities related to the user's areas of interest. The hobby suggestion unit may also help the user rediscover hobbies that the user enjoyed in the past. For example, the deceased's voice may suggest that the user resume a hobby that the user enjoyed in the past. This allows the user to discover new hobbies and interests through dialogue with the deceased. For example, the user may find new enjoyment by hearing new hobbies suggested by the deceased's voice. The user may also gain new knowledge by hearing information related to the user's areas of interest by hearing the deceased's voice. The user may also rediscover and enjoy hobbies that the user enjoyed in the past by hearing the deceased's voice.

[0074] The Eternal Voice AI system may further include an emotional support unit that estimates the user's emotions and provides emotional support using the deceased's voice based on the estimated emotions. For example, when the user feels lonely, the emotional support unit may provide encouraging words using the deceased's voice. The emotional support unit may also provide encouraging words using the deceased's voice when the user is feeling depressed. For example, when the user feels depressed, the emotional support unit may provide encouraging words using the deceased's voice. The emotional support unit may also provide advice on how to relax using the deceased's voice when the user is feeling nervous. For example, when the user feels nervous, the emotional support unit may provide advice on how to relax using the deceased's voice. This allows the user to receive emotional support through dialogue with the deceased. For example, the user may feel less lonely by receiving encouraging words using the deceased's voice. The user may feel more positive by receiving encouraging words using the deceased's voice. The user may also be able to relieve tension by receiving advice on how to relax using the deceased's voice.

[0075] The Eternal Voice AI system may also include a learning support unit that supports the user's learning. For example, if the user wants to learn a new skill, the learning support unit may provide a learning guide in the deceased's voice. The learning support unit may also provide related information in the deceased's voice if the user wants to deepen their knowledge in a particular field. For example, the learning support unit may suggest learning resources related to the user's field of interest. Furthermore, the learning support unit may record the user's learning progress and provide feedback in the deceased's voice. For example, the user may record their learning progress and receive encouraging feedback in the deceased's voice. This allows the user to receive learning support through dialogue with the deceased. For example, the user may learn new skills efficiently by receiving learning guidance in the deceased's voice. The user may also deepen their knowledge by receiving related information in the deceased's voice. Furthermore, the user may maintain their motivation to learn by receiving feedback in the deceased's voice.

[0076] The Eternal Voice AI system may also include an emotion recording unit that estimates the user's emotions and records emotional changes in the deceased's voice based on the estimated emotions. For example, the system may record the emotions the user felt during a conversation in real time and later analyze the emotional changes. The emotion recording unit may also visualize the emotional changes the user felt during the conversation in graphs or charts. For example, it may display the emotional changes in a graph along a timeline. The emotion recording unit may also organize the emotions the user felt during the conversation by category, allowing for easy review later. For example, it may classify the emotional changes into positive and negative emotions, allowing for later review. This allows the user to record and later analyze the emotional changes throughout the conversation with the deceased. For example, the user can review the changes in their emotions throughout the conversation. The user can also understand emotional patterns by visualizing the changes in emotions. Furthermore, the user can understand their own emotional trends by analyzing the changes in emotions.

[0077] The Eternal Voice AI system may further include a creativity support unit that stimulates the user's creativity. For example, the unit may provide brainstorming guidance in the deceased's voice to help the user generate new ideas. The creativity support unit may also provide inspiration in the deceased's voice when the user is working on a creative project. For example, the unit may provide inspiration in the deceased's voice when the user is working on an art or design project. The creativity support unit may also provide advice in the deceased's voice when the user is solving creative problems. For example, the unit may provide advice in the deceased's voice when the user is thinking of ideas for a new project. This allows the user to be inspired by the brainstorming guidance in the deceased's voice. The user may also be inspired by the deceased's voice to progress with creative projects. The user may also be able to solve creative problems by receiving advice in the deceased's voice.

[0078] The Eternal Voice AI system may further include an emotion sharing unit that estimates the user's emotions and shares changes in the deceased's voice based on the estimated emotions. For example, the emotion sharing unit may record the emotions felt by the user during a conversation in real time and share them with other family and friends. The emotion sharing unit may also provide a function for sharing the emotions felt by the user on social media. For example, the emotion score may be posted on social media and shared with other people. The emotion sharing unit may also provide a function for sharing the emotions felt by the user via email. For example, the emotion score may be sent via email and shared with other people. This allows the user to share the emotions felt through conversations with the deceased with other family and friends and gain sympathy. For example, the user may gain sympathy by sharing the emotions felt through conversations with the deceased with other people. The user may also share memories by sharing the emotions felt through conversations with the deceased with other people. The user may also feel a connection with the deceased by sharing the emotions felt through conversations with the deceased with other people.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The audio data collection unit collects audio data of the deceased. For example, it collects past recorded conversations of the deceased. The audio data collection unit can also extract audio from video. For example, it can extract the voice of the deceased from home video. Furthermore, the audio data collection unit can also collect audio messages left by the deceased. For example, it can collect voice messages left by the deceased. Step 2: The learning unit learns the voice data collected by the voice data collection unit. For example, the generation AI uses deep learning to learn the tone of voice and speaking style of the deceased. The generation AI can also learn the words used by the deceased using a neural network. For example, the generation AI learns the phrases that the deceased often used. Furthermore, the generation AI can also learn the unique intonation of the deceased. For example, it learns the characteristics of the deceased's speaking style. Step 3: The dialogue generation unit generates a dialogue with the user based on the data learned by the learning unit. For example, when the user asks, "How was your day?", the generation AI generates an answer that the deceased would have said when they were alive. Also, when the user asks, "Do you have any advice?", the generation AI can generate advice that the deceased would have said when they were alive. For example, the generation AI generates an answer based on advice that the deceased often gave. Furthermore, when the user asks, "Tell me about your memories," the generation AI can recreate memories that the deceased shared when they were alive. For example, the generation AI will have the deceased talk about memories they had with their family.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 voice data collection unit that collects voice data of the deceased; a learning unit that learns the voice data collected by the voice data collection unit; a dialogue generation unit that generates a dialogue with a user based on the data learned by the learning unit; A system characterized by:

2. The voice data collection unit In addition to the voice data of the deceased, text data such as handwritten notes and diaries are also collected. The learning unit Learning the thought patterns and emotions of the deceased based on the voice data and the text data 2. The system of claim 1.

3. The dialogue generation unit Reproduce the music and movie lines that the deceased loved and provide them to users 2. The system of claim 1.

4. The dialogue generation unit Analyze the user's past dialogue history and perform dialogue simulations based on the user's preferences and interests 2. The system of claim 1.

5. The dialogue generation unit Utilizing the knowledge and experience of the deceased, a dialogue is generated that allows users to make new discoveries and gain new insights.

2. The system of claim 1.

6. The learning unit Using emotion estimation function, the system learns changes in emotions from the voice data of the deceased, The dialogue generation unit Based on the change in emotion, a speaking style corresponding to the emotion is reproduced.

2. The system of claim 1.

7. The dialogue generation unit Using emotion estimation functionality, emotions are estimated from the voice data of the deceased, and appropriate voices are provided when the user wants to feel a specific emotion.

2. The system of claim 1.

8. The dialogue generation unit Using emotion estimation functionality, the system analyzes the user's emotional state in real time and generates dialogue content accordingly.

2. The system of claim 1.

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