System

Abstract

To provide a system according to an embodiment which enables a similar conversation to a conversation made by a deceased person on the basis of information of the deceased person.SOLUTION: An system according to an embodiment includes a collection unit, a generation unit, and a conversation unit. The collection unit collects information of a deceased person. The generation unit generates a model which simulates the nature of the deceased person on the basis of the information collected by the collection unit. The conversation unit has a conversation with a user by using the model generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not yet fully realized personality imitation or conversation based on information about the deceased, and there is room for improvement.

[0005] The system of the embodiment aims to have a conversation similar to that of the deceased based on information about the deceased. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a conversation unit. The collection unit collects information about the deceased. The generation unit generates a model that imitates the personality of the deceased based on the information collected by the collection unit. The conversation unit uses the model generated by the generation unit to have a conversation with a user. Effect of the Invention

[0007] The system according to the embodiment can have a conversation similar to that of the deceased based on information about the deceased. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system according to the embodiment of the present invention uses a generation AI to imitate the experiences, memories, personality, and recollections of the deceased, and allows a user to have a conversation similar to that of the deceased. This system collects information about the deceased, generates a model that imitates the personality of the deceased based on the collected information, and uses the generated model to have a conversation with the user. For example, the diary, letters, photos, videos, audio recordings, etc. of the deceased are collected and input to the generation AI. The generation AI analyzes this information and learns the experiences, memories, personality, and recollections of the deceased. The generation AI learns the thinking patterns and emotions of the deceased from the diary and letters of the deceased, and learns the facial expressions and gestures of the deceased from photos and videos. This allows the generation AI to reproduce the personality of the deceased. The user can enjoy a conversation similar to that of the deceased by interacting with the generation AI. For example, when the user asks a question to the deceased, the generation AI answers based on the experience and memories of the deceased. It is also possible to have a conversation based on the personality and memories of the deceased. This allows the user to share memories with the deceased and feel a connection with the deceased. This system enables users to converse with the deceased, allowing them to reminisce about their memories with the deceased. In addition, by imitating the personality of the deceased, users can feel a connection with the deceased. For example, by recreating memories and experiences that the deceased spoke of while alive, users can reaffirm their bond with the deceased. This allows the system to collect information about the deceased, generate a model that imitates the personality of the deceased, and converse with the user.

[0029] The system according to the embodiment includes a collection unit, a generation unit, and a conversation unit. The collection unit collects information about the deceased. The information about the deceased includes, but is not limited to, a diary, a letter, a photograph, a video, and an audio recording. The collection unit, for example, digitizes and reads the diary or letter of the deceased using scanning technology. The collection unit can also directly read information submitted in digital format. Furthermore, the collection unit can also read printed information using OCR technology. For example, the collection unit scans a handwritten diary with a high-resolution scanner and converts it into text information using OCR technology. Information in digital format can be directly read from information submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The generation unit uses a generation AI to analyze the information collected by the collection unit and learn the experiences, memories, personality, and recollections of the deceased. The generation AI uses, for example, a text generation AI (e.g., LLM) to learn the thinking patterns and emotions of the deceased. The generation unit can also learn the facial expressions and gestures of the deceased using a multimodal generation AI. The generation AI generates a model that imitates the personality of the deceased based on information about the deceased. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities such as images and voices as well as text. The generation AI uses keyword extraction technology to pick up particularly important information about the deceased and generate a model based on that information. The conversation unit uses the model generated by the generation unit to have a conversation with the user. For example, when the user asks a question to the deceased, the generation AI answers based on the experience and memory of the deceased. The conversation unit can also have a conversation based on the personality and memories of the deceased. This allows the user to share memories with the deceased and feel a connection with the deceased. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can have a conversation using an AI model that inputs the model generated by the generation unit and outputs a dialogue with the user.This allows the system according to the embodiment to collect information about the deceased, generate a model that mimics the personality of the deceased, and engage in conversation with the user.

[0030] The collection unit collects information about the deceased. The information about the deceased includes, but is not limited to, diaries, letters, photographs, videos, and audio recordings. For example, the collection unit digitizes and reads the diaries and letters of the deceased using scanning technology. Specifically, a handwritten diary or letter is scanned using a high-resolution scanner and saved as a digital image. This digital image is used in subsequent processing, so quality control such as resolution and color adjustment is important. The collection unit can also directly read information submitted in digital format. For example, digital documents, emails, and social media posts created by the deceased during their lifetime are examples of this. This digital information is often submitted in a specific file format (e.g., PDF, DOCX, JPEG, etc.), and the collection unit can directly read these files. In addition, the collection unit can also read printed information using OCR technology. For example, the collection unit scans a handwritten diary with a high-resolution scanner and converts it into text information using OCR technology. OCR technology recognizes printed characters with high accuracy and converts them into digital text. In this process, pre-processing such as noise removal and contrast adjustment of images may be performed to improve the accuracy of character recognition. Digital information can be directly read if it is submitted in a specific file format. For example, photos and videos taken by the deceased during their lifetime are often saved in common file formats such as JPEG and MP4. The collection department directly reads these files and stores them in the database. This allows the collection department to efficiently collect and digitize information on the deceased in various formats.

[0031] The generation unit uses the generation AI to analyze the information collected by the collection unit and learn the experiences, memories, personality, and recollections of the deceased. The generation AI, for example, uses a text generation AI (e.g., LLM) to learn the thinking patterns and emotions of the deceased. Specifically, it analyzes the collected text data of diaries and letters to learn the characteristics of the deceased's writing and the patterns of words used. This allows the generation AI to imitate the thoughts and emotions of the deceased. The generation unit can also learn the facial expressions and gestures of the deceased using a multimodal generation AI. For example, it analyzes collected photos and videos to extract the characteristics of the facial expressions and movements of the deceased. The multimodal generation AI can handle multiple modals such as images and voices as well as text, so it can generate a more realistic model of the deceased. The generation AI generates a model that imitates the personality of the deceased based on the information of the deceased. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. This allows the deceased's writing style and wording to be reproduced. Multimodal generative AI learns by integrating multiple modalities such as images and audio, in addition to text, so it can also reproduce the facial expressions and vocal characteristics of the deceased. The generative AI uses keyword extraction technology to pick out particularly important information about the deceased and generate a model based on that. For example, it extracts specific keywords and phrases from the diary or letters of the deceased, and generates a model that reproduces the thoughts and emotions of the deceased based on that. This allows the generator to analyze the information of the deceased in detail and generate an advanced model that mimics the personality of the deceased.

[0032] The conversation unit uses the model generated by the generation unit to converse with the user. For example, when the user asks the deceased a question, the generation AI responds based on the deceased's experiences and memories. Specifically, when the user asks, "What is your best memory?", the generation AI responds in detail about the deceased's best memory based on the collected information. The conversation unit can also have a conversation based on the personality and memories of the deceased. For example, if the deceased liked humor, the generation AI can generate a humorous response. This allows the user to share memories with the deceased and feel a connection with the deceased. Some or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can have a conversation using an AI model that takes the model generated by the generation unit as input and outputs a dialogue with the user. Specifically, the conversation unit uses natural language processing technology to analyze the user's question and generate an appropriate response. This allows the conversation unit to smoothly converse with the user. The conversation unit can also collect user feedback and continuously improve the model of the generation AI. For example, if a user requests "tell me more" in response to a generated answer, the AI ​​will provide additional information. This allows the conversation section to gain a deeper understanding of the deceased's information through dialogue with the user, enabling a more natural conversation.

[0033] The collection unit can collect information on the deceased's diary, letters, photos, videos, and audio recordings. For example, the collection unit reads the deceased's diary and letters with a scanner and saves them as digital data. The collection unit can also digitize and save the deceased's photos and videos. For example, the collection unit scans the deceased's photos with a high-resolution scanner and saves them as digital images. The collection unit can also digitize and save the deceased's videos. Furthermore, the collection unit can digitize and save the deceased's voice recordings. For example, the collection unit saves the deceased's voice recordings as digital audio files. In this way, by collecting information on the deceased's diary, letters, photos, videos, and audio recordings, more detailed information on the deceased can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the deceased into AI and cause AI to collect information.

[0034] The generating unit can analyze the information collected by the collecting unit and learn the experiences, memories, personality, and recollections of the deceased. The generating unit can analyze, for example, the diary or letters of the deceased and learn the thinking patterns and emotions of the deceased. The generating unit can also analyze photos or videos of the deceased and learn the facial expressions and gestures of the deceased. For example, the generating unit can analyze the diary of the deceased using text analysis technology and learn the thinking patterns and emotions of the deceased. The generating unit can also analyze the letters of the deceased using natural language processing technology and learn the emotions of the deceased. Furthermore, the generating unit can analyze photos of the deceased using image analysis technology and learn the facial expressions and gestures of the deceased. For example, the generating unit can analyze photos of the deceased using image recognition technology and learn the facial expressions of the deceased. The generating unit can also analyze videos of the deceased using video analysis technology and learn the gestures of the deceased. This allows the personality of the deceased to be imitated more accurately by analyzing the collected information and learning the experiences, memories, personality, and recollections of the deceased. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected information to AI and cause the AI ​​to generate a model that imitates the personality of the deceased person.

[0035] When collecting information on the deceased, the collection unit can evaluate the reliability of the information and prioritize collection of reliable information. For example, the collection unit evaluates the reliability of the diary and letters of the deceased and prioritizes collection of reliable information. The collection unit can also evaluate the reliability of photos and videos of the deceased and prioritize collection of reliable information. For example, the collection unit scans photos of the deceased with a high-resolution scanner and evaluates the quality of the images. The collection unit can also digitize videos of the deceased and evaluate the quality of the images. Furthermore, the collection unit can evaluate the reliability of audio recordings of the deceased and prioritize collection of reliable information. For example, the collection unit saves audio recordings of the deceased as digital audio files and evaluates the quality of the audio. This allows more accurate information on the deceased to be obtained by preferentially collecting reliable information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input information on the deceased to AI and have the AI ​​execute evaluation of the reliability of the information.

[0036] When collecting information on the deceased, the collection unit can filter the information taking into consideration the living environment and social background of the deceased. The collection unit, for example, filters the relevant information based on the living environment of the deceased. The collection unit can also filter the relevant information based on the social background of the deceased. For example, the collection unit filters the relevant information based on the residential environment of the deceased. The collection unit can also filter the relevant information based on the local community of the deceased. Furthermore, the collection unit can filter the relevant information based on the economic situation of the deceased. For example, the collection unit collects information on the residential environment of the deceased and filters the relevant information. The collection unit can also collect information on the local community of the deceased and filter the relevant information. Furthermore, the collection unit can collect information on the economic situation of the deceased and filter the relevant information. As a result, by filtering information taking into consideration the living environment and social background of the deceased, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the deceased to AI and cause the AI ​​to execute filtering of the information.

[0037] When collecting information on the deceased, the collection unit can collect related information taking into consideration the friendships and family structure of the deceased. The collection unit collects related information, for example, based on the friendships of the deceased. The collection unit can also collect related information based on the family structure of the deceased. For example, the collection unit collects information about the deceased's friends and colleagues and collects related information. The collection unit can also collect information about the deceased's family and collect related information. Furthermore, the collection unit can collect related information based on memories of the deceased's friends and family. For example, the collection unit collects photos and videos with the deceased's friends and family and collects related information. The collection unit can also collect letters and audio recordings with the deceased's friends and family and collect related information. In this way, by collecting related information taking into consideration the friendships and family structure of the deceased, more detailed information on the deceased can be obtained. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs information about the friendships and family structure of the deceased into AI, and causes AI to execute collection of related information.

[0038] When collecting information on the deceased, the collection unit can preferentially collect specific information based on the hobbies and interests of the deceased. The collection unit, for example, preferentially collects related information based on the hobbies of the deceased. The collection unit can also preferentially collect related information based on the interests of the deceased. For example, the collection unit collects information on the sports of the deceased and preferentially collects related information. The collection unit can also collect information on the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect information on the reading of the deceased and preferentially collect related information. For example, the collection unit collects photos and videos related to the sports of the deceased and preferentially collect related information. The collection unit can also collect letters and audio recordings related to the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect diaries and letters related to the reading of the deceased and preferentially collect related information. In this way, by preferentially collecting specific information based on the hobbies and interests of the deceased, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department can input information about the deceased person's hobbies and interests into the AI ​​and have the AI ​​prioritize collecting certain information.

[0039] When analyzing the information of the deceased, the generation unit can learn the emotions and thought patterns of the deceased in detail. The generation unit learns the emotions and thought patterns of the deceased from, for example, the diary or letters of the deceased. The generation unit can also learn the facial expressions and gestures of the deceased from photos and videos of the deceased. For example, the generation unit analyzes the diary of the deceased using text analysis technology to learn the emotions and thought patterns of the deceased. The generation unit can also analyze the letters of the deceased using natural language processing technology to learn the emotions of the deceased. Furthermore, the generation unit can analyze the photos of the deceased using image analysis technology to learn the facial expressions and gestures of the deceased. For example, the generation unit analyzes the photos of the deceased using image recognition technology to learn the facial expressions of the deceased. The generation unit can also analyze the videos of the deceased using video analysis technology to learn the gestures of the deceased. As a result, by learning the emotions and thought patterns of the deceased in detail, it is possible to more accurately imitate the personality of the deceased. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about the deceased into the AI ​​and have the AI ​​learn the emotions and thought patterns of the deceased.

[0040] When analyzing the information of the deceased, the generation unit can customize the model based on the specific events and experiences of the deceased. The generation unit customizes the model based on, for example, the specific events of the deceased. The generation unit can also customize the model based on the specific experiences of the deceased. For example, the generation unit collects information on the specific events of the deceased and customizes the model. The generation unit can also collect information on the specific experiences of the deceased and customize the model. Furthermore, the generation unit can customize the model based on the specific memories of the deceased. For example, the generation unit collects diaries and letters related to the specific events of the deceased and customizes the model. The generation unit can also collect photos and videos related to the specific experiences of the deceased and customize the model. Furthermore, the generation unit can collect audio recordings related to the specific memories of the deceased and customize the model. In this way, by customizing the model based on the specific events and experiences of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information on the specific events and experiences of the deceased into AI and cause AI to perform model customization.

[0041] When analyzing the information of the deceased, the generation unit can generate a model based on the social role and occupation of the deceased. The generation unit generates a model based on, for example, the social role of the deceased. The generation unit can also generate a model based on the occupation of the deceased. For example, the generation unit collects information on the social role of the deceased and generates a model. The generation unit can also collect information on the occupation of the deceased and generate a model. Furthermore, the generation unit can generate a model based on the social background of the deceased. For example, the generation unit collects diaries and letters related to the social role of the deceased and generates a model. The generation unit can also collect photos and videos related to the occupation of the deceased and generate a model. Furthermore, the generation unit can collect audio recordings related to the social background of the deceased and generate a model. In this way, by generating a model based on the social role and occupation of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information on the social role and occupation of the deceased to AI and cause AI to execute the generation of the model.

[0042] When analyzing information about the deceased, the generation unit can generate a model taking into account the cultural background and values ​​of the deceased. The generation unit generates a model, for example, based on the cultural background of the deceased. The generation unit can also generate a model based on the values ​​of the deceased. For example, the generation unit collects information about the cultural background of the deceased and generates a model. The generation unit can also collect information about the values ​​of the deceased and generate a model. Furthermore, the generation unit can generate a model based on the religious background of the deceased. For example, the generation unit collects diaries and letters related to the cultural background of the deceased and generates a model. The generation unit can also collect photos and videos related to the values ​​of the deceased and generate a model. Furthermore, the generation unit can collect audio recordings related to the religious background of the deceased and generate a model. In this way, by generating a model taking into account the cultural background and values ​​of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information about the cultural background and values ​​of the deceased to AI and cause AI to execute the generation of the model.

[0043] When interacting with the user, the conversation unit can induce the conversation to draw out the specific memories and experiences of the deceased. The conversation unit, for example, induces the conversation based on the specific memories of the deceased. The conversation unit can also induce the conversation based on the specific experiences of the deceased. For example, the conversation unit collects information on the specific memories of the deceased and induces the conversation. The conversation unit can also collect information on the specific experiences of the deceased and induce the conversation. Furthermore, the conversation unit can also induce the conversation based on a specific event of the deceased. For example, the conversation unit collects diaries and letters related to the specific memories of the deceased and induces the conversation. The conversation unit can also collect photos and videos related to the specific experiences of the deceased and induce the conversation. Furthermore, the conversation unit can collect audio recordings related to the specific events of the deceased and induce the conversation. This allows for a deeper conversation by inducing the conversation to draw out the specific memories and experiences of the deceased. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input information about specific memories and experiences of the deceased into the AI ​​and have the AI ​​guide the conversation.

[0044] When the conversation unit converses with the user, the conversation unit can adjust the conversation content by referring to the user's past dialogue history. The conversation unit adjusts the conversation content based on the user's past dialogue history, for example. The conversation unit can also extract topics of interest from the user's past dialogue history. For example, the conversation unit optimizes the conversation content based on the user's past dialogue history. The conversation unit can also analyze the user's past dialogue history and provide optimal conversation content. Furthermore, the conversation unit can adjust the progress of the conversation by referring to the user's past dialogue history. For example, the conversation unit adjusts the progress speed of the conversation based on the user's past dialogue history. The conversation unit can also adjust the tone and content of the conversation based on the user's past dialogue history. As a result, a more appropriate conversation can be conducted by optimizing the conversation content by referring to the user's past dialogue history. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the user's past dialogue history into AI and cause the AI ​​to adjust the conversation content.

[0045] When interacting with a user, the conversation unit can customize the conversation content based on the user's current living situation and interests. The conversation unit customizes the conversation content based on the user's current living situation, for example. The conversation unit can also customize the conversation content based on the user's interests. For example, the conversation unit collects information on the user's current living situation and customizes the conversation content. The conversation unit can also collect information on the user's interests and customize the conversation content. Furthermore, the conversation unit can customize the conversation content based on the user's current activity. For example, the conversation unit collects diaries and letters related to the user's current living situation and customizes the conversation content. The conversation unit can also collect photos and videos related to the user's interests and customize the conversation content. Furthermore, the conversation unit can collect audio recordings related to the user's current activity and customize the conversation content. As a result, by customizing the conversation content based on the user's current living situation and interests, a more relevant conversation can be held. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input information about the user's current living situation and interests into the AI, allowing the AI ​​to customize the conversation content.

[0046] When interacting with a user, the conversation unit can adjust the conversation content taking into account the geographical background and cultural background of the user. The conversation unit adjusts the conversation content based on, for example, the geographical background of the user. The conversation unit can also adjust the conversation content based on the cultural background of the user. For example, the conversation unit collects information on the geographical background of the user and adjusts the conversation content. The conversation unit can also collect information on the cultural background of the user and adjust the conversation content. Furthermore, the conversation unit can adjust the conversation content based on the religious background of the user. For example, the conversation unit collects diaries and letters related to the geographical background of the user and adjusts the conversation content. The conversation unit can also collect photos and videos related to the cultural background of the user and adjust the conversation content. Furthermore, the conversation unit can collect audio recordings related to the religious background of the user and adjust the conversation content. As a result, by adjusting the conversation content taking into account the geographical background and cultural background of the user, a more appropriate conversation can be conducted. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input information about the user's geographical and cultural background into the AI ​​and have the AI ​​adjust the content of the conversation.

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

[0048] When collecting information on the deceased, the collection unit can filter the information taking into consideration the health condition and medical records of the deceased. For example, the collection unit filters relevant information based on the health condition of the deceased. The collection unit can also filter relevant information based on the medical records of the deceased. For example, the collection unit collects information on the health condition of the deceased and filters the relevant information. The collection unit can also collect information on the medical records of the deceased and filter the relevant information. Furthermore, the collection unit can preferentially collect specific information based on the health condition and medical records of the deceased. For example, the collection unit collects diaries and letters related to the health condition of the deceased and filters the relevant information. The collection unit can also collect photos and videos related to the medical records of the deceased and filter the relevant information. In this way, by filtering information taking into consideration the health condition and medical records of the deceased, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the health condition and medical records of the deceased to AI and cause the AI ​​to perform filtering of information.

[0049] When analyzing the information of the deceased, the generation unit can customize the model based on the hobbies and interests of the deceased. For example, the generation unit customizes the model based on the hobbies of the deceased. The generation unit can also customize the model based on the interests of the deceased. For example, the generation unit collects information on the hobbies of the deceased and customizes the model. The generation unit can also collect information on the interests of the deceased and customize the model. Furthermore, the generation unit can preferentially collect specific information based on the hobbies and interests of the deceased. For example, the generation unit collects diaries and letters related to the hobbies of the deceased and customizes the model. The generation unit can also collect photos and videos related to the interests of the deceased and customize the model. In this way, by customizing the model based on the hobbies and interests of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information on the hobbies and interests of the deceased to AI and cause the AI ​​to execute customization of the model.

[0050] When collecting information on the deceased, the collection unit can filter the information based on the travel record and places visited by the deceased. For example, the collection unit filters relevant information based on the travel record of the deceased. The collection unit can also filter relevant information based on the places visited by the deceased. For example, the collection unit collects information on the travel record of the deceased and filters the relevant information. The collection unit can also collect information on the places visited by the deceased and filter the relevant information. Furthermore, the collection unit can preferentially collect specific information based on the travel record and places visited by the deceased. For example, the collection unit collects diaries and letters related to the travel record of the deceased and filters the relevant information. The collection unit can also collect photos and videos related to the places visited by the deceased and filter the relevant information. In this way, by filtering information based on the travel record and places visited by the deceased, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the travel record and places visited by the deceased to AI and have the AI ​​execute filtering of information.

[0051] When collecting information on the deceased, the collection unit can filter the information based on the educational background and academic history of the deceased. For example, the collection unit filters relevant information based on the educational background of the deceased. The collection unit can also filter relevant information based on the educational history of the deceased. For example, the collection unit collects information on the educational background of the deceased and filters the relevant information. The collection unit can also collect information on the educational history of the deceased and filter the relevant information. Furthermore, the collection unit can preferentially collect specific information based on the educational background and academic history of the deceased. For example, the collection unit collects diaries and letters related to the educational background of the deceased and filters the relevant information. The collection unit can also collect photos and videos related to the educational history of the deceased and filter the relevant information. In this way, by filtering information based on the educational background and academic history of the deceased, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the educational background and academic history of the deceased to AI and cause the AI ​​to filter the information.

[0052] When collecting information on the deceased, the collection unit can preferentially collect specific information based on the hobbies and interests of the deceased. For example, the collection unit preferentially collects related information based on the hobbies of the deceased. The collection unit can also preferentially collect related information based on the interests of the deceased. For example, the collection unit collects information on the sports of the deceased and preferentially collects related information. The collection unit can also collect information on the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect information on the reading of the deceased and preferentially collect related information. For example, the collection unit collects photos and videos related to the sports of the deceased and preferentially collect related information. The collection unit can also collect letters and audio recordings related to the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect diaries and letters related to the reading of the deceased and preferentially collect related information. In this way, by preferentially collecting specific information based on the hobbies and interests of the deceased, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department can input information about the deceased person's hobbies and interests into the AI ​​and have the AI ​​prioritize collecting certain information.

[0053] The process flow of the first embodiment will be briefly described below.

[0054] Step 1: The collection department collects the information of the deceased. The information of the deceased may include diaries, letters, photos, videos, audio recordings, etc. The collection department digitizes and reads the diaries and letters of the deceased using scanning technology. The collection department may also directly read information submitted in digital format. In addition, the collection department may also read printed information using OCR technology. For example, a handwritten diary may be scanned with a high-resolution scanner and converted into text information using OCR technology. Digital information may be directly read if submitted in a specific file format. Step 2: The generation unit uses the generation AI to analyze the information collected by the collection unit and learn the experiences, memories, personality, and recollections of the deceased. The generation AI uses a text generation AI (e.g., LLM) to learn the thinking patterns and emotions of the deceased. It can also use a multimodal generation AI to learn the facial expressions and gestures of the deceased. The generation AI generates a model that imitates the personality of the deceased based on the information of the deceased. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information about the deceased and generates a model based on that information. Step 3: The conversation unit uses the model generated by the generation unit to converse with the user. When the user asks the deceased a question, the generation AI responds based on the deceased's experiences and memories. The conversation unit can also hold a conversation based on the personality and memories of the deceased. This allows the user to share memories with the deceased and feel a connection with them. The processing in the conversation unit is often performed using AI, but it can also be performed without AI. For example, a conversation can be held using an AI model that takes the model generated by the generation unit as input and outputs a dialogue with the user.

[0055] (Example 2) The system according to the embodiment of the present invention uses a generation AI to imitate the experiences, memories, personality, and recollections of the deceased, and allows a user to have a conversation similar to that of the deceased. This system collects information about the deceased, generates a model that imitates the personality of the deceased based on the collected information, and uses the generated model to have a conversation with the user. For example, the diary, letters, photos, videos, audio recordings, etc. of the deceased are collected and input to the generation AI. The generation AI analyzes this information and learns the experiences, memories, personality, and recollections of the deceased. The generation AI learns the thinking patterns and emotions of the deceased from the diary and letters of the deceased, and learns the facial expressions and gestures of the deceased from photos and videos. This allows the generation AI to reproduce the personality of the deceased. The user can enjoy a conversation similar to that of the deceased by interacting with the generation AI. For example, when the user asks a question to the deceased, the generation AI answers based on the experience and memories of the deceased. It is also possible to have a conversation based on the personality and memories of the deceased. This allows the user to share memories with the deceased and feel a connection with the deceased. This system enables users to converse with the deceased, allowing them to reminisce about their memories with the deceased. In addition, by imitating the personality of the deceased, users can feel a connection with the deceased. For example, by recreating memories and experiences that the deceased spoke of while alive, users can reaffirm their bond with the deceased. This allows the system to collect information about the deceased, generate a model that imitates the personality of the deceased, and converse with the user.

[0056] The system according to the embodiment includes a collection unit, a generation unit, and a conversation unit. The collection unit collects information about the deceased. The information about the deceased includes, but is not limited to, a diary, a letter, a photograph, a video, and an audio recording. The collection unit, for example, digitizes and reads the diary or letter of the deceased using scanning technology. The collection unit can also directly read information submitted in digital format. Furthermore, the collection unit can also read printed information using OCR technology. For example, the collection unit scans a handwritten diary with a high-resolution scanner and converts it into text information using OCR technology. Information in digital format can be directly read from information submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The generation unit uses a generation AI to analyze the information collected by the collection unit and learn the experiences, memories, personality, and recollections of the deceased. The generation AI uses, for example, a text generation AI (e.g., LLM) to learn the thinking patterns and emotions of the deceased. The generation unit can also learn the facial expressions and gestures of the deceased using a multimodal generation AI. The generation AI generates a model that imitates the personality of the deceased based on information about the deceased. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities such as images and voices as well as text. The generation AI uses keyword extraction technology to pick up particularly important information about the deceased and generate a model based on that information. The conversation unit uses the model generated by the generation unit to have a conversation with the user. For example, when the user asks a question to the deceased, the generation AI answers based on the experience and memory of the deceased. The conversation unit can also have a conversation based on the personality and memories of the deceased. This allows the user to share memories with the deceased and feel a connection with the deceased. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can have a conversation using an AI model that inputs the model generated by the generation unit and outputs a dialogue with the user.This allows the system according to the embodiment to collect information about the deceased, generate a model that mimics the personality of the deceased, and engage in conversation with the user.

[0057] The collection unit collects information about the deceased. The information about the deceased includes, but is not limited to, diaries, letters, photographs, videos, and audio recordings. For example, the collection unit digitizes and reads the diaries and letters of the deceased using scanning technology. Specifically, a handwritten diary or letter is scanned using a high-resolution scanner and saved as a digital image. This digital image is used in subsequent processing, so quality control such as resolution and color adjustment is important. The collection unit can also directly read information submitted in digital format. For example, digital documents, emails, and social media posts created by the deceased during their lifetime are examples of this. This digital information is often submitted in a specific file format (e.g., PDF, DOCX, JPEG, etc.), and the collection unit can directly read these files. In addition, the collection unit can also read printed information using OCR technology. For example, the collection unit scans a handwritten diary with a high-resolution scanner and converts it into text information using OCR technology. OCR technology recognizes printed characters with high accuracy and converts them into digital text. In this process, pre-processing such as noise removal and contrast adjustment of images may be performed to improve the accuracy of character recognition. Digital information can be directly read if it is submitted in a specific file format. For example, photos and videos taken by the deceased during their lifetime are often saved in common file formats such as JPEG and MP4. The collection department directly reads these files and stores them in the database. This allows the collection department to efficiently collect and digitize information on the deceased in various formats.

[0058] The generation unit uses the generation AI to analyze the information collected by the collection unit and learn the experiences, memories, personality, and recollections of the deceased. The generation AI, for example, uses a text generation AI (e.g., LLM) to learn the thinking patterns and emotions of the deceased. Specifically, it analyzes the collected text data of diaries and letters to learn the characteristics of the deceased's writing and the patterns of words used. This allows the generation AI to imitate the thoughts and emotions of the deceased. The generation unit can also learn the facial expressions and gestures of the deceased using a multimodal generation AI. For example, it analyzes collected photos and videos to extract the characteristics of the facial expressions and movements of the deceased. The multimodal generation AI can handle multiple modals such as images and voices as well as text, so it can generate a more realistic model of the deceased. The generation AI generates a model that imitates the personality of the deceased based on the information of the deceased. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. This allows the deceased's writing style and wording to be reproduced. Multimodal generative AI learns by integrating multiple modalities such as images and audio, in addition to text, so it can also reproduce the facial expressions and vocal characteristics of the deceased. The generative AI uses keyword extraction technology to pick out particularly important information about the deceased and generate a model based on that. For example, it extracts specific keywords and phrases from the diary or letters of the deceased, and generates a model that reproduces the thoughts and emotions of the deceased based on that. This allows the generator to analyze the information of the deceased in detail and generate an advanced model that mimics the personality of the deceased.

[0059] The conversation unit uses the model generated by the generation unit to converse with the user. For example, when the user asks the deceased a question, the generation AI responds based on the deceased's experiences and memories. Specifically, when the user asks, "What is your best memory?", the generation AI responds in detail about the deceased's best memory based on the collected information. The conversation unit can also have a conversation based on the personality and memories of the deceased. For example, if the deceased liked humor, the generation AI can generate a humorous response. This allows the user to share memories with the deceased and feel a connection with the deceased. Some or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can have a conversation using an AI model that takes the model generated by the generation unit as input and outputs a dialogue with the user. Specifically, the conversation unit uses natural language processing technology to analyze the user's question and generate an appropriate response. This allows the conversation unit to smoothly converse with the user. The conversation unit can also collect user feedback and continuously improve the model of the generation AI. For example, if a user requests "tell me more" in response to a generated answer, the AI ​​will provide additional information. This allows the conversation section to gain a deeper understanding of the deceased's information through dialogue with the user, enabling a more natural conversation.

[0060] The collection unit can collect information on the deceased's diary, letters, photos, videos, and audio recordings. For example, the collection unit reads the deceased's diary and letters with a scanner and saves them as digital data. The collection unit can also digitize and save the deceased's photos and videos. For example, the collection unit scans the deceased's photos with a high-resolution scanner and saves them as digital images. The collection unit can also digitize and save the deceased's videos. Furthermore, the collection unit can digitize and save the deceased's voice recordings. For example, the collection unit saves the deceased's voice recordings as digital audio files. In this way, by collecting information on the deceased's diary, letters, photos, videos, and audio recordings, more detailed information on the deceased can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the deceased into AI and cause AI to collect information.

[0061] The generating unit can analyze the information collected by the collecting unit and learn the experiences, memories, personality, and recollections of the deceased. The generating unit can analyze, for example, the diary or letters of the deceased and learn the thinking patterns and emotions of the deceased. The generating unit can also analyze photos or videos of the deceased and learn the facial expressions and gestures of the deceased. For example, the generating unit can analyze the diary of the deceased using text analysis technology and learn the thinking patterns and emotions of the deceased. The generating unit can also analyze the letters of the deceased using natural language processing technology and learn the emotions of the deceased. Furthermore, the generating unit can analyze photos of the deceased using image analysis technology and learn the facial expressions and gestures of the deceased. For example, the generating unit can analyze photos of the deceased using image recognition technology and learn the facial expressions of the deceased. The generating unit can also analyze videos of the deceased using video analysis technology and learn the gestures of the deceased. This allows the personality of the deceased to be imitated more accurately by analyzing the collected information and learning the experiences, memories, personality, and recollections of the deceased. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected information to AI and cause the AI ​​to generate a model that imitates the personality of the deceased person.

[0062] The collection unit can estimate the user's emotion and adjust the type and amount of information to be collected based on the estimated user's emotion. For example, when the user is sad, the collection unit reduces the amount of information to be collected in order to reduce the emotional burden. In addition, when the user is nostalgic, the collection unit can also collect detailed information and dig deeper into memories. For example, when the user is nostalgic, the collection unit collects detailed diaries and letters of the deceased. In addition, when the user is excited, the collection unit can also collect a wide range of information and provide diverse memories. For example, when the user is excited, the collection unit collects a wide range of photos and videos of the deceased. In this way, the type and amount of information to be collected according to the user's emotion can be adjusted to reduce the emotional burden of the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI or may be performed without using an AI. For example, the collection unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the type and amount of information to be collected.

[0063] When collecting information on the deceased, the collection unit can evaluate the reliability of the information and prioritize collection of reliable information. For example, the collection unit evaluates the reliability of the diary and letters of the deceased and prioritizes collection of reliable information. The collection unit can also evaluate the reliability of photos and videos of the deceased and prioritize collection of reliable information. For example, the collection unit scans photos of the deceased with a high-resolution scanner and evaluates the quality of the images. The collection unit can also digitize videos of the deceased and evaluate the quality of the images. Furthermore, the collection unit can evaluate the reliability of audio recordings of the deceased and prioritize collection of reliable information. For example, the collection unit saves audio recordings of the deceased as digital audio files and evaluates the quality of the audio. This allows more accurate information on the deceased to be obtained by preferentially collecting reliable information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input information on the deceased to AI and have the AI ​​execute evaluation of the reliability of the information.

[0064] When collecting information on the deceased, the collection unit can filter the information taking into consideration the living environment and social background of the deceased. The collection unit, for example, filters the relevant information based on the living environment of the deceased. The collection unit can also filter the relevant information based on the social background of the deceased. For example, the collection unit filters the relevant information based on the residential environment of the deceased. The collection unit can also filter the relevant information based on the local community of the deceased. Furthermore, the collection unit can filter the relevant information based on the economic situation of the deceased. For example, the collection unit collects information on the residential environment of the deceased and filters the relevant information. The collection unit can also collect information on the local community of the deceased and filter the relevant information. Furthermore, the collection unit can collect information on the economic situation of the deceased and filter the relevant information. As a result, by filtering information taking into consideration the living environment and social background of the deceased, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the deceased to AI and cause the AI ​​to execute filtering of the information.

[0065] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is sad, the collection unit selects information to be collected with priority in order to reduce the emotional burden. In addition, when the user is nostalgic, the collection unit can also select information to be collected with priority in order to dig deeper into memories. For example, when the user is nostalgic, the collection unit preferentially collects detailed diaries and letters of the deceased. In addition, when the user is excited, the collection unit can also select information to be collected with priority in order to provide diverse memories. For example, when the user is excited, the collection unit preferentially collects photos and videos of the deceased. In this way, the priority of information to be collected according to the user's emotion can be determined, thereby reducing the emotional burden of the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data into the AI ​​and have the AI ​​decide the priorities of the information to be collected.

[0066] When collecting information on the deceased, the collection unit can collect related information taking into consideration the friendships and family structure of the deceased. The collection unit collects related information, for example, based on the friendships of the deceased. The collection unit can also collect related information based on the family structure of the deceased. For example, the collection unit collects information about the deceased's friends and colleagues and collects related information. The collection unit can also collect information about the deceased's family and collect related information. Furthermore, the collection unit can collect related information based on memories of the deceased's friends and family. For example, the collection unit collects photos and videos with the deceased's friends and family and collects related information. The collection unit can also collect letters and audio recordings with the deceased's friends and family and collect related information. In this way, by collecting related information taking into consideration the friendships and family structure of the deceased, more detailed information on the deceased can be obtained. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs information about the friendships and family structure of the deceased into AI, and causes AI to execute collection of related information.

[0067] When collecting information on the deceased, the collection unit can preferentially collect specific information based on the hobbies and interests of the deceased. The collection unit, for example, preferentially collects related information based on the hobbies of the deceased. The collection unit can also preferentially collect related information based on the interests of the deceased. For example, the collection unit collects information on the sports of the deceased and preferentially collects related information. The collection unit can also collect information on the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect information on the reading of the deceased and preferentially collect related information. For example, the collection unit collects photos and videos related to the sports of the deceased and preferentially collect related information. The collection unit can also collect letters and audio recordings related to the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect diaries and letters related to the reading of the deceased and preferentially collect related information. In this way, by preferentially collecting specific information based on the hobbies and interests of the deceased, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department can input information about the deceased person's hobbies and interests into the AI ​​and have the AI ​​prioritize collecting certain information.

[0068] The generation unit can estimate the user's emotion and adjust the expression method of the model to be generated based on the estimated user's emotion. For example, when the user is sad, the generation unit adopts a gentle expression method to reduce the emotional burden. In addition, when the user is nostalgic, the generation unit can also adopt a detailed expression method to reproduce detailed memories. For example, when the user is nostalgic, the generation unit adopts an expression method based on a detailed diary or letter of the deceased. In addition, when the user is excited, the generation unit can also adopt a visually stimulating expression method. For example, when the user is excited, the generation unit adopts an expression method based on a photo or video of the deceased. In this way, the expression method of the model to be generated according to the user's emotion can be adjusted to reduce the emotional burden of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the expression method of the generated model.

[0069] When analyzing the information of the deceased, the generation unit can learn the emotions and thought patterns of the deceased in detail. The generation unit learns the emotions and thought patterns of the deceased from, for example, the diary or letters of the deceased. The generation unit can also learn the facial expressions and gestures of the deceased from photos and videos of the deceased. For example, the generation unit analyzes the diary of the deceased using text analysis technology to learn the emotions and thought patterns of the deceased. The generation unit can also analyze the letters of the deceased using natural language processing technology to learn the emotions of the deceased. Furthermore, the generation unit can analyze the photos of the deceased using image analysis technology to learn the facial expressions and gestures of the deceased. For example, the generation unit analyzes the photos of the deceased using image recognition technology to learn the facial expressions of the deceased. The generation unit can also analyze the videos of the deceased using video analysis technology to learn the gestures of the deceased. As a result, by learning the emotions and thought patterns of the deceased in detail, it is possible to more accurately imitate the personality of the deceased. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about the deceased into the AI ​​and have the AI ​​learn the emotions and thought patterns of the deceased.

[0070] When analyzing the information of the deceased, the generation unit can customize the model based on the specific events and experiences of the deceased. The generation unit customizes the model based on, for example, the specific events of the deceased. The generation unit can also customize the model based on the specific experiences of the deceased. For example, the generation unit collects information on the specific events of the deceased and customizes the model. The generation unit can also collect information on the specific experiences of the deceased and customize the model. Furthermore, the generation unit can customize the model based on the specific memories of the deceased. For example, the generation unit collects diaries and letters related to the specific events of the deceased and customizes the model. The generation unit can also collect photos and videos related to the specific experiences of the deceased and customize the model. Furthermore, the generation unit can collect audio recordings related to the specific memories of the deceased and customize the model. In this way, by customizing the model based on the specific events and experiences of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information on the specific events and experiences of the deceased into AI and cause AI to perform model customization.

[0071] The generation unit can estimate the user's emotion and adjust the level of detail of the model to be generated based on the estimated user's emotion. For example, when the user is sad, the generation unit generates a concise model to reduce the emotional burden. In addition, when the user is nostalgic, the generation unit can also generate a detailed model to reproduce detailed memories. For example, when the user is nostalgic, the generation unit generates a model based on a detailed diary or letter of the deceased. In addition, when the user is excited, the generation unit can also generate a visually stimulating model. For example, when the user is excited, the generation unit generates a model based on a photo or video of the deceased. In this way, the level of detail of the model to be generated according to the user's emotion can be adjusted to reduce the emotional burden of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the level of detail of the model to be generated.

[0072] When analyzing the information of the deceased, the generation unit can generate a model based on the social role and occupation of the deceased. The generation unit generates a model based on, for example, the social role of the deceased. The generation unit can also generate a model based on the occupation of the deceased. For example, the generation unit collects information on the social role of the deceased and generates a model. The generation unit can also collect information on the occupation of the deceased and generate a model. Furthermore, the generation unit can generate a model based on the social background of the deceased. For example, the generation unit collects diaries and letters related to the social role of the deceased and generates a model. The generation unit can also collect photos and videos related to the occupation of the deceased and generate a model. Furthermore, the generation unit can collect audio recordings related to the social background of the deceased and generate a model. In this way, by generating a model based on the social role and occupation of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information on the social role and occupation of the deceased to AI and cause AI to execute the generation of the model.

[0073] When analyzing information about the deceased, the generation unit can generate a model taking into account the cultural background and values ​​of the deceased. The generation unit generates a model, for example, based on the cultural background of the deceased. The generation unit can also generate a model based on the values ​​of the deceased. For example, the generation unit collects information about the cultural background of the deceased and generates a model. The generation unit can also collect information about the values ​​of the deceased and generate a model. Furthermore, the generation unit can generate a model based on the religious background of the deceased. For example, the generation unit collects diaries and letters related to the cultural background of the deceased and generates a model. The generation unit can also collect photos and videos related to the values ​​of the deceased and generate a model. Furthermore, the generation unit can collect audio recordings related to the religious background of the deceased and generate a model. In this way, by generating a model taking into account the cultural background and values ​​of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information about the cultural background and values ​​of the deceased to AI and cause AI to execute the generation of the model.

[0074] The conversation unit can estimate the user's emotion and adjust the tone and content of the conversation based on the estimated user's emotion. For example, when the user is sad, the conversation unit can conduct a conversation in which detailed memories are shared when the user is nostalgic. For example, when the user is nostalgic, the conversation unit can conduct a conversation based on detailed diaries and letters of the deceased. For example, when the user is excited, the conversation unit can conduct a conversation based on photos and videos of the deceased. This allows a more appropriate conversation to be conducted by adjusting the tone and content of the conversation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the tone and content of the conversation.

[0075] When interacting with the user, the conversation unit can induce the conversation to draw out the specific memories and experiences of the deceased. The conversation unit, for example, induces the conversation based on the specific memories of the deceased. The conversation unit can also induce the conversation based on the specific experiences of the deceased. For example, the conversation unit collects information on the specific memories of the deceased and induces the conversation. The conversation unit can also collect information on the specific experiences of the deceased and induce the conversation. Furthermore, the conversation unit can also induce the conversation based on a specific event of the deceased. For example, the conversation unit collects diaries and letters related to the specific memories of the deceased and induces the conversation. The conversation unit can also collect photos and videos related to the specific experiences of the deceased and induce the conversation. Furthermore, the conversation unit can collect audio recordings related to the specific events of the deceased and induce the conversation. This allows for a deeper conversation by inducing the conversation to draw out the specific memories and experiences of the deceased. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input information about specific memories and experiences of the deceased into the AI ​​and have the AI ​​guide the conversation.

[0076] When the conversation unit converses with the user, the conversation unit can adjust the conversation content by referring to the user's past dialogue history. The conversation unit adjusts the conversation content based on the user's past dialogue history, for example. The conversation unit can also extract topics of interest from the user's past dialogue history. For example, the conversation unit optimizes the conversation content based on the user's past dialogue history. The conversation unit can also analyze the user's past dialogue history and provide optimal conversation content. Furthermore, the conversation unit can adjust the progress of the conversation by referring to the user's past dialogue history. For example, the conversation unit adjusts the progress speed of the conversation based on the user's past dialogue history. The conversation unit can also adjust the tone and content of the conversation based on the user's past dialogue history. As a result, a more appropriate conversation can be conducted by optimizing the conversation content by referring to the user's past dialogue history. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the user's past dialogue history into AI and cause the AI ​​to adjust the conversation content.

[0077] The conversation unit can estimate the user's emotion and adjust the speed of the conversation based on the estimated user's emotion. For example, when the user is sad, the conversation unit conducts the conversation at a slow speed. Also, when the user is nostalgic, the conversation unit can conduct the conversation at a moderate speed. For example, when the user is nostalgic, the conversation unit conducts the conversation based on a detailed diary or letter of the deceased. Also, when the user is excited, the conversation unit can conduct the conversation at a fast speed. For example, when the user is excited, the conversation unit conducts the conversation based on a photo or video of the deceased. In this way, by adjusting the speed of the conversation according to the user's emotion, a more appropriate conversation can be conducted. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the speed at which the conversation progresses.

[0078] When interacting with a user, the conversation unit can customize the conversation content based on the user's current living situation and interests. The conversation unit customizes the conversation content based on the user's current living situation, for example. The conversation unit can also customize the conversation content based on the user's interests. For example, the conversation unit collects information on the user's current living situation and customizes the conversation content. The conversation unit can also collect information on the user's interests and customize the conversation content. Furthermore, the conversation unit can customize the conversation content based on the user's current activity. For example, the conversation unit collects diaries and letters related to the user's current living situation and customizes the conversation content. The conversation unit can also collect photos and videos related to the user's interests and customize the conversation content. Furthermore, the conversation unit can collect audio recordings related to the user's current activity and customize the conversation content. As a result, by customizing the conversation content based on the user's current living situation and interests, a more relevant conversation can be held. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input information about the user's current living situation and interests into the AI, allowing the AI ​​to customize the conversation content.

[0079] When interacting with a user, the conversation unit can adjust the conversation content taking into account the geographical background and cultural background of the user. The conversation unit adjusts the conversation content based on, for example, the geographical background of the user. The conversation unit can also adjust the conversation content based on the cultural background of the user. For example, the conversation unit collects information on the geographical background of the user and adjusts the conversation content. The conversation unit can also collect information on the cultural background of the user and adjust the conversation content. Furthermore, the conversation unit can adjust the conversation content based on the religious background of the user. For example, the conversation unit collects diaries and letters related to the geographical background of the user and adjusts the conversation content. The conversation unit can also collect photos and videos related to the cultural background of the user and adjust the conversation content. Furthermore, the conversation unit can collect audio recordings related to the religious background of the user and adjust the conversation content. As a result, by adjusting the conversation content taking into account the geographical background and cultural background of the user, a more appropriate conversation can be conducted. A part or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input information about the user's geographical and cultural background into the AI ​​and have the AI ​​adjust the content of the conversation.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When collecting information on the deceased, the collection unit can filter the information taking into consideration the health condition and medical records of the deceased. For example, the collection unit filters relevant information based on the health condition of the deceased. The collection unit can also filter relevant information based on the medical records of the deceased. For example, the collection unit collects information on the health condition of the deceased and filters the relevant information. The collection unit can also collect information on the medical records of the deceased and filter the relevant information. Furthermore, the collection unit can preferentially collect specific information based on the health condition and medical records of the deceased. For example, the collection unit collects diaries and letters related to the health condition of the deceased and filters the relevant information. The collection unit can also collect photos and videos related to the medical records of the deceased and filter the relevant information. In this way, by filtering information taking into consideration the health condition and medical records of the deceased, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the health condition and medical records of the deceased to AI and cause the AI ​​to perform filtering of information.

[0082] When analyzing the information of the deceased, the generation unit can customize the model based on the hobbies and interests of the deceased. For example, the generation unit customizes the model based on the hobbies of the deceased. The generation unit can also customize the model based on the interests of the deceased. For example, the generation unit collects information on the hobbies of the deceased and customizes the model. The generation unit can also collect information on the interests of the deceased and customize the model. Furthermore, the generation unit can preferentially collect specific information based on the hobbies and interests of the deceased. For example, the generation unit collects diaries and letters related to the hobbies of the deceased and customizes the model. The generation unit can also collect photos and videos related to the interests of the deceased and customize the model. In this way, by customizing the model based on the hobbies and interests of the deceased, it is possible to more accurately imitate the personality of the deceased. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information on the hobbies and interests of the deceased to AI and cause the AI ​​to execute customization of the model.

[0083] The conversation unit can estimate the user's emotion and select a conversation topic based on the estimated user's emotion. For example, when the user is sad, the conversation unit selects a calm topic to reduce the emotional burden. In addition, when the user is nostalgic, the conversation unit can also select a topic that shares detailed memories. For example, when the user is nostalgic, the conversation unit selects a topic based on a detailed diary or letter of the deceased. In addition, when the user is excited, the conversation unit can also select a visually stimulating topic. For example, when the user is excited, the conversation unit selects a topic based on a photo or video of the deceased. In this way, the emotional burden of the user can be reduced by selecting a conversation topic according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotional data into the AI ​​and have the AI ​​select the topic of the conversation.

[0084] When collecting information on the deceased, the collection unit can filter the information based on the travel record and places visited by the deceased. For example, the collection unit filters relevant information based on the travel record of the deceased. The collection unit can also filter relevant information based on the places visited by the deceased. For example, the collection unit collects information on the travel record of the deceased and filters the relevant information. The collection unit can also collect information on the places visited by the deceased and filter the relevant information. Furthermore, the collection unit can preferentially collect specific information based on the travel record and places visited by the deceased. For example, the collection unit collects diaries and letters related to the travel record of the deceased and filters the relevant information. The collection unit can also collect photos and videos related to the places visited by the deceased and filter the relevant information. In this way, by filtering information based on the travel record and places visited by the deceased, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the travel record and places visited by the deceased to AI and have the AI ​​execute filtering of information.

[0085] The generation unit can estimate the user's emotion and adjust the dialogue style of the model to be generated based on the estimated user's emotion. For example, when the user is sad, the generation unit adopts a gentle dialogue style to reduce the emotional burden. In addition, when the user is nostalgic, the generation unit can also adopt a detailed dialogue style to reproduce detailed memories. For example, when the user is nostalgic, the generation unit adopts a dialogue style based on a detailed diary or letter of the deceased. In addition, when the user is excited, the generation unit can also adopt a visually stimulating dialogue style. For example, when the user is excited, the generation unit adopts a dialogue style based on a photo or video of the deceased. In this way, the emotional burden of the user can be reduced by adjusting the dialogue style of the model to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the dialogue style of the model it generates.

[0086] When collecting information on the deceased, the collection unit can filter the information based on the educational background and academic history of the deceased. For example, the collection unit filters relevant information based on the educational background of the deceased. The collection unit can also filter relevant information based on the educational history of the deceased. For example, the collection unit collects information on the educational background of the deceased and filters the relevant information. The collection unit can also collect information on the educational history of the deceased and filter the relevant information. Furthermore, the collection unit can preferentially collect specific information based on the educational background and academic history of the deceased. For example, the collection unit collects diaries and letters related to the educational background of the deceased and filters the relevant information. The collection unit can also collect photos and videos related to the educational history of the deceased and filter the relevant information. In this way, by filtering information based on the educational background and academic history of the deceased, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on the educational background and academic history of the deceased to AI and cause the AI ​​to filter the information.

[0087] The conversation unit can estimate the user's emotions and adjust the conversation progress method based on the estimated user's emotions. For example, when the user is sad, the conversation unit adopts a slow progress method to reduce the emotional burden. In addition, when the user is nostalgic, the conversation unit can also adopt a detailed progress method to share detailed memories. For example, when the user is nostalgic, the conversation unit adopts a progress method based on detailed diaries and letters of the deceased. In addition, when the user is excited, the conversation unit can also adopt a visually stimulating progress method. For example, when the user is excited, the conversation unit adopts a progress method based on photos and videos of the deceased. In this way, the emotional burden of the user can be reduced by adjusting the conversation progress method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotional data into the AI ​​and have the AI ​​adjust how the conversation progresses.

[0088] When collecting information on the deceased, the collection unit can preferentially collect specific information based on the hobbies and interests of the deceased. For example, the collection unit preferentially collects related information based on the hobbies of the deceased. The collection unit can also preferentially collect related information based on the interests of the deceased. For example, the collection unit collects information on the sports of the deceased and preferentially collects related information. The collection unit can also collect information on the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect information on the reading of the deceased and preferentially collect related information. For example, the collection unit collects photos and videos related to the sports of the deceased and preferentially collect related information. The collection unit can also collect letters and audio recordings related to the music of the deceased and preferentially collect related information. Furthermore, the collection unit can collect diaries and letters related to the reading of the deceased and preferentially collect related information. In this way, by preferentially collecting specific information based on the hobbies and interests of the deceased, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department can input information about the deceased person's hobbies and interests into the AI ​​and have the AI ​​prioritize collecting certain information.

[0089] The generation unit can estimate the user's emotion and adjust the level of detail of the model to be generated based on the estimated user's emotion. For example, when the user is sad, the generation unit generates a concise model to reduce the emotional burden. In addition, when the user is nostalgic, the generation unit can also generate a detailed model to reproduce detailed memories. For example, when the user is nostalgic, the generation unit generates a model based on a detailed diary or letter of the deceased. In addition, when the user is excited, the generation unit can also generate a visually stimulating model. For example, when the user is excited, the generation unit generates a model based on a photo or video of the deceased. In this way, the level of detail of the model to be generated according to the user's emotion can be adjusted to reduce the emotional burden of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the level of detail of the model to be generated.

[0090] The conversation unit can estimate the user's emotions and adjust the tone and content of the conversation based on the estimated user's emotions. For example, if the user is sad, the conversation unit can conduct the conversation in a gentle tone. Also, if the user is nostalgic, the conversation unit can conduct a conversation in which detailed memories are shared. For example, if the user is nostalgic, the conversation unit can conduct a conversation based on detailed diaries and letters of the deceased. Also, if the user is excited, the conversation unit can conduct a conversation in a lively tone. For example, if the user is excited, the conversation unit can conduct a conversation based on photos and videos of the deceased. This allows a more appropriate conversation to be conducted by adjusting the tone and content of the conversation according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the tone and content of the conversation.

[0091] The process flow of the second embodiment will be briefly described below.

[0092] Step 1: The collection department collects the information of the deceased. The information of the deceased may include diaries, letters, photos, videos, audio recordings, etc. The collection department digitizes and reads the diaries and letters of the deceased using scanning technology. The collection department may also directly read information submitted in digital format. In addition, the collection department may also read printed information using OCR technology. For example, a handwritten diary may be scanned with a high-resolution scanner and converted into text information using OCR technology. Digital information may be directly read if submitted in a specific file format. Step 2: The generation unit uses the generation AI to analyze the information collected by the collection unit and learn the experiences, memories, personality, and recollections of the deceased. The generation AI uses a text generation AI (e.g., LLM) to learn the thinking patterns and emotions of the deceased. It can also use a multimodal generation AI to learn the facial expressions and gestures of the deceased. The generation AI generates a model that imitates the personality of the deceased based on the information of the deceased. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information about the deceased and generates a model based on that information. Step 3: The conversation unit uses the model generated by the generation unit to converse with the user. When the user asks the deceased a question, the generation AI responds based on the deceased's experiences and memories. The conversation unit can also hold a conversation based on the personality and memories of the deceased. This allows the user to share memories with the deceased and feel a connection with them. The processing in the conversation unit is often performed using AI, but it can also be performed without AI. For example, a conversation can be held using an AI model that takes the model generated by the generation unit as input and outputs a dialogue with the user.

[0093] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0095] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0096] For example, the collection unit can collect information about the deceased person using the camera 42 and microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and generates a model that imitates the personality of the deceased person. The conversation unit is realized by the control unit 46A of the smart device 14, and uses the generated model to converse with the user. Each of the collection unit, the generation unit, and the conversation unit may be realized by at least one of the data processing device 12 and the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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, the RAM 30, and the 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.

[0100] 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.

[0101] 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 the voice according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0103] 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 performed in a secure state.

[0104] Fig. 4 shows an example of 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.

[0105] 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.

[0106] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0110] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0111] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] For example, the collection unit can collect information about the deceased person using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and generates a model that imitates the personality of the deceased person. The conversation unit is realized by the control unit 46A of the smart glasses 214, and uses the generated model to converse with the user. Each of the collection unit, the generation unit, and the conversation unit may be realized by at least one of the data processing device 12 and the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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, the RAM 30, and the 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 voice indicating a user input for 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] For example, the collection unit can collect information about the deceased person using the camera 42 and microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and generates a model that imitates the personality of the deceased. The conversation unit is realized by the control unit 46A of the headset type terminal 314, and uses the generated model to converse with the user. Each of the collection unit, the generation unit, and the conversation unit may be realized by at least one of the data processing device 12 and the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a 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.

[0131] 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, the RAM 30, and the 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0135] 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 performed in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0137] 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.

[0138] 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.

[0139] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 a voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0145] For example, the collection unit can collect information about the deceased person using the camera 42 and microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and generates a model that imitates the personality of the deceased person. The conversation unit is realized by the control unit 46A of the robot 414, and uses the generated model to converse with the user. Each of the collection unit, the generation unit, and the conversation unit may be realized by at least one of the data processing device 12 and the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0147] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0149] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0151] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0153] 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.

[0154] 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 Universal Serial Bus (USB) 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.

[0155] In addition, 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 upon request from the data processing device 12.

[0156] 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.

[0157] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0158] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0159] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0160] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0162] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0164] (Appendix 1) a collection department that collects information about the deceased; A generation unit that generates a model that imitates the personality of the deceased person based on the information collected by the collection unit; A conversation unit that uses the model generated by the generation unit to have a conversation with a user. A system characterized by: (Appendix 2) The collecting unit includes: Collect information from the deceased's diary (e.g. letters, photographs, videos, audio recordings) 2. The system of claim 1, (Appendix 3) The generation unit is Analyzing the information collected by the collection unit to learn about the experiences (e.g., memories, personality, and recollections) of the deceased. 2. The system of claim 1, (Appendix 4) The collecting unit includes: To estimate a user's emotion and adjust the type or amount of information to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 5) The collecting unit includes: When collecting information about the deceased, evaluate the reliability of the information and prioritize collection of reliable information 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting information about the deceased, filter the information by taking into account the deceased's living circumstances or social background. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting information about the deceased, collect relevant information by considering the deceased's social connections (e.g., family structure). 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting information about the deceased, prioritize collecting certain information based on the deceased's interests (e.g., 2. The system of claim 1, (Appendix 10) The generation unit is Estimate the user's emotions and adjust the representation of the generated model based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The generation unit is When analyzing information about the deceased, learn in detail the emotions (e.g., thought patterns) of the deceased 2. The system of claim 1, (Appendix 12) The generation unit is When analyzing the deceased's information, customize the model based on the deceased's specific events (e.g., experiences) 2. The system of claim 1, (Appendix 13) The generation unit is Estimate the user's emotions and adjust the level of detail of the generated model based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The generation unit is When analyzing information about the deceased, generate a model based on the social role (e.g., occupation) of the deceased 2. The system of claim 1, (Appendix 15) The generation unit is When analyzing information about the deceased, generate a model that takes into account the cultural background (e.g., values) of the deceased. 2. The system of claim 1, (Appendix 16) The conversation unit includes: Inferring a user's emotions and adjusting the tone or content of a conversation based on the inferred user's emotions 2. The system of claim 1, (Appendix 17) The conversation unit includes: When interacting with the user, guide the conversation to elicit specific memories (e.g., experiences) of the deceased person. 2. The system of claim 1, (Appendix 18) The conversation unit includes: When interacting with a user, the content of the conversation is adjusted by referring to the user's past interaction history. 2. The system of claim 1, (Appendix 19) The conversation unit includes: Estimate the user's emotions and adjust the pace of the conversation based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The conversation unit includes: When interacting with a user, tailor the conversation based on the user's current life situation (e.g., interests) 2. The system of claim 1, (Appendix 21) The conversation unit includes: When interacting with a user, adjust the content of the conversation to take into account the user's geographical background (e.g., cultural background) 2. The system of claim 1, [Explanation of symbols]

[0165] 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 collection department that collects information about the deceased; A generation unit that generates a model that imitates the personality of the deceased person based on the information collected by the collection unit; A conversation unit that uses the model generated by the generation unit to have a conversation with a user. A system characterized by:

2. The collecting unit includes: Collect diary information of the deceased 2. The system of claim 1 .

3. The generation unit is Analyzing the information collected by the collection unit and learning about the experiences of the deceased 2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotion and adjust the type or amount of information to be collected based on the estimated user's emotion.

2. The system of claim 1 .

5. The collecting unit includes: When collecting information about the deceased, evaluate the reliability of the information and prioritize collection of reliable information 2. The system of claim 1 .

6. The collecting unit includes: When collecting information about the deceased, filter the information by taking into account the deceased's living circumstances or social background.

2. The system of claim 1 .

7. The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions.

2. The system of claim 1 .

8. The collecting unit includes: When collecting information about the deceased, collect relevant information by taking into account the deceased's social connections.

2. The system of claim 1 .

9. The collecting unit includes: When collecting information about the deceased, certain information is prioritized based on the deceased's interests.

2. The system of claim 1 .

10. The generation unit is Estimate the user's emotions and adjust the representation of the generated model based on the estimated user emotions.

2. The system of claim 1 .

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