System

Abstract

The system according to the embodiment is intended for elderly people to look back and systematically record their lives.SOLUTION: A system includes a question generation part, an answer collection part, an answer arrangement part, an end note creation part, and a storage part. The question generation unit generates an appropriate question for the elderly person. The answer collection unit collects an answer of the elderly person based on the question generated by the question generation unit. The answer organization unit organizes the answers collected by the answer collection unit. The end note creating section creates an end note based on the answers arranged by the answer arranging section. The storage unit stores the end note generated by the end note generation unit in an encrypted cloud storage.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of lacking adequate support for seniors to systematically reflect on and record their lives.

[0005] The system according to the embodiment aims to enable elderly people to look back on their lives and record them systematically. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, an answer collection unit, an answer organization unit, an end note creation unit, and a storage unit. The question generation unit generates appropriate questions for the elderly. The answer collection unit collects answers from the elderly based on the questions generated by the question generation unit. The answer organization unit organizes the answers collected by the answer collection unit. The end note creation unit creates end notes based on the answers organized by the answer organization unit. The storage unit stores the end notes created by the end note creation unit in encrypted cloud storage. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to look back on their lives and record them systematically. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​Endnote service according to an embodiment of the present invention allows elderly people to look back on their lives and record important memories, values, and messages they wish to convey. This service uses AI to ask appropriate questions and systematically create endnotes based on the elderly's answers. This allows the AI ​​Endnote service to help elderly people look back on their lives and put their important thoughts into words.

[0029] An AI endnote service according to an embodiment includes a question generation unit, an answer collection unit, an answer organization unit, an endnote creation unit, and a storage unit. The question generation unit generates appropriate questions for elderly people. For example, the generation AI generates questions such as, "What is the most memorable event in your life?" or "Is there a message you would like to convey to your family?" The generation AI receives input from a prompt containing instructions from the user about what the user wants the generation AI to do, and the generation AI generates questions based on the prompt. The answer collection unit collects answers from elderly people based on the questions generated by the question generation unit. For example, it converts the answers from elderly people into text using voice recognition technology. The answer collection unit can also scan handwritten answers and convert them into digital data. The answer organization unit organizes the collected answers. For example, the generation AI classifies the answers from elderly people by category and organizes them under categories such as "memories," "values," and "messages." The endnote creation unit creates endnotes based on the answers organized by the answer organization unit. For example, the generation AI automatically generates endnotes based on the organized answers. The storage unit stores the end notes created by the end note creation unit in encrypted cloud storage. For example, the end notes are encrypted using AES encryption technology and stored in secure cloud storage. This allows the AI ​​end note service according to the embodiment to enable elderly people to look back on their lives and record important memories, values, and messages they want to convey.

[0030] The question generation unit can analyze the elderly person's past social media posts and blog posts and generate individually customized questions. For example, the question generation unit collects the elderly person's past social media posts and blog posts and analyzes their content using natural language processing technology. For example, it extracts posts related to specific events or emotions and generates questions based on them. The question generation unit can also use generation AI to generate questions related to the content of past posts. For example, the generation AI generates follow-up questions such as, "Please tell me more about this post you wrote previously." This makes it possible to analyze the elderly person's past social media posts and blog posts and generate individually customized questions.

[0031] When generating questions, the question generation unit generates multimodal questions using audio and images, stimulating the visual and auditory senses. For example, when generating questions, the question generation unit generates multimodal questions that combine audio and images. For example, while displaying past photos, questions about the photos are asked by audio. The question generation unit can also generate questions that combine audio and text. For example, the generation AI asks questions by audio and displays the content in text. Furthermore, the question generation unit can also generate questions using video. For example, a video of a past event is played and questions about the content are asked. In this way, multimodal questions using audio and images can be generated, stimulating the visual and auditory senses.

[0032] The question generation unit can generate culturally appropriate questions for elderly people with different cultural backgrounds. For example, the question generation unit generates culturally appropriate questions for elderly people with different cultural backgrounds. For example, the question generation unit generates questions related to traditional events and customs in a particular culture. The question generation unit can also generate questions related to culture using a generation AI. For example, the generation AI generates a question such as, "What is the most important holiday in your culture?" Furthermore, the question generation unit can generate questions in different languages. For example, the generation AI generates questions in multiple languages, such as English and French. This makes it possible to generate culturally appropriate questions for elderly people with different cultural backgrounds.

[0033] The answer organization unit can analyze the answers of the elderly, extract keywords from the answers, and automatically tag them. For example, the answer organization unit can analyze the answers of the elderly using natural language processing technology and extract important keywords. For example, tags can be generated based on frequently occurring words and phrases in the answers. The answer organization unit can also use a generation AI to generate tags based on the content of the answers. For example, the generation AI generates tags based on categories such as "memories," "values," and "messages." Furthermore, the answer organization unit can analyze the content of the answers and automatically tag related keywords. For example, the generation AI extracts important information from the answers and generates tags based on that information. This makes it possible to analyze the answers of the elderly, extract keywords, and automatically tag them.

[0034] The answer organizing unit can organize the content of the answers in chronological order and display important life events in chronological order. The answer organizing unit, for example, organizes the answers of elderly people in chronological order, building a system that displays important life events in chronological order. For example, it organizes events based on date and time information in the answers. The answer organizing unit can also organize the content of the answers in chronological order using a generation AI. For example, the generation AI extracts important events in the answers and displays them in chronological order. Furthermore, the answer organizing unit can display the content of the answers in graph or timeline format. For example, the generation AI displays important events in the answers in a graph or timeline to make them easier to understand visually. This makes it possible to organize the content of the answers in chronological order and display important life events in chronological order.

[0035] The answer organizing unit can convert the answers into visual notes or mind maps to make them easier to understand visually. The answer organizing unit, for example, converts the answers of elderly people into visual notes and builds a system to visually display them. For example, the main points of the answers are shown using diagrams or icons. The answer organizing unit can also convert the answers into mind maps using a generation AI. For example, the generation AI displays the content of the answers in mind map format. Furthermore, the answer organizing unit can convert the content of the answers into graphs or charts. For example, the generation AI converts important information in the answers into graphs or charts to make them easier to understand visually. This allows the answers to be converted into visual notes or mind maps to make them easier to understand visually.

[0036] The answer organization unit can automatically translate answers into different languages ​​and obtain feedback from an international perspective. The answer organization unit, for example, builds a system that automatically translates answers from elderly people into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The answer organization unit can also automatically translate answers using a generation AI. For example, the generation AI translates the content of the answer into a different language and collects feedback based on the translation results. Furthermore, the answer organization unit can build a system that evaluates the translated answers from an international perspective. For example, it collects feedback from users with different cultures and backgrounds and evaluates the answers based on that. This makes it possible to automatically translate answers into different languages ​​and obtain feedback from an international perspective.

[0037] The end note creation unit can add images and videos to the end note to enhance the visual elements. For example, the end note creation unit adds past photos and videos of the elderly person to the end note, building a system to enhance the visual elements. For example, it embeds photos and videos related to the answer in the end note. The end note creation unit can also use the generation AI to automatically add images and videos to the end note. For example, the generation AI searches for related images and videos based on the content of the answer and adds them to the end note. Furthermore, the end note creation unit can provide an editing function for images and videos. For example, the generation AI can trim and filter images and videos and add them to the end note in the optimal format. This makes it possible to add images and videos to the end note to enhance the visual elements.

[0038] The end note creation unit generates end notes in audio format, making it possible to accommodate visually impaired people. The end note creation unit, for example, builds a system that generates the contents of end notes in audio format, making it possible to accommodate visually impaired people. For example, it converts text into audio and plays end notes as audio. The end note creation unit can also use a generation AI to generate the contents of end notes in audio format. For example, the generation AI converts the contents of end notes into audio, making them accessible to visually impaired people. Furthermore, the end note creation unit can store end notes in audio format in cloud storage. For example, the generation AI encrypts end notes in audio format and stores them in secure cloud storage. This makes it possible to generate end notes in audio format, making it possible to accommodate visually impaired people.

[0039] The end note creation unit can publish the end note as an interactive web page, allowing users to add comments and feedback. The end note creation unit, for example, publishes the end note as an interactive web page and builds a system that allows users to add comments and feedback. For example, a comment section is provided for each section of the end note. The end note creation unit can also automatically generate an interactive web page using a generation AI. For example, the generation AI generates an interactive web page based on the contents of the end note, allowing users to add comments and feedback. Furthermore, the end note creation unit can provide a function that reflects comments and feedback in real time. For example, the generation AI analyzes user comments and feedback in real time and reflects them in the end note. This makes it possible to publish the end note as an interactive web page, allowing users to add comments and feedback.

[0040] The storage unit generates end notes in audio format, making it possible to accommodate visually impaired people. The storage unit, for example, builds a system that generates the contents of end notes in audio format, making it possible to accommodate visually impaired people. For example, it converts text into audio and plays end notes audibly. The storage unit can also use a generation AI to generate the contents of end notes in audio format. For example, the generation AI converts the contents of end notes into audio, making them accessible to visually impaired people. Furthermore, the storage unit can store end notes in audio format in cloud storage. For example, the generation AI encrypts end notes in audio format and stores them in secure cloud storage. This makes it possible to generate end notes in audio format, making it possible to accommodate visually impaired people.

[0041] The storage unit can publish end notes as interactive web pages, allowing users to add comments and feedback. For example, the storage unit can publish end notes as interactive web pages and build a system that allows users to add comments and feedback. For example, a comment section can be provided for each section of the end note. The storage unit can also automatically generate interactive web pages using a generation AI. For example, the generation AI can generate an interactive web page based on the contents of the end note, allowing users to add comments and feedback. Furthermore, the storage unit can provide a function that reflects comments and feedback in real time. For example, the generation AI can analyze user comments and feedback in real time and reflect them in the end note. This makes it possible to publish end notes as interactive web pages, allowing users to add comments and feedback.

[0042] The storage unit can automatically record the editing history of end notes and enable changes to be tracked. For example, the storage unit builds a system that can automatically record the editing history of end notes and track changes. For example, the editing history is saved along with a timestamp. The storage unit can also automatically record the editing history using a generation AI. For example, the generation AI records the editing history of end notes in real time and tracks changes. Furthermore, the storage unit can provide a function to visually display the editing history. For example, the generation AI displays the editing history in graph or timeline format, making it easier to understand visually. This makes it possible to automatically record the editing history of end notes and enable changes to be tracked.

[0043] The storage unit can link end notes with social media to make them easily shareable. For example, the storage unit can link end notes with social media to build a system that makes it easy to share them. For example, it can provide a function that allows the contents of end notes to be posted to social media with one click. The storage unit can also use a generation AI to automatically post the contents of end notes to social media. For example, the generation AI can analyze the contents of end notes and post them to social media at the appropriate time. Furthermore, the storage unit can also provide a function to collect feedback on social media. For example, the generation AI can analyze comments and reactions on social media and reflect them in end notes. This allows end notes to be linked with social media to make them easily shareable.

[0044] The storage unit can convert the contents of the end note into a printable format and store it as a physical album. The storage unit, for example, builds a system that converts the contents of the end note into a printable format and stores it as a physical album. For example, it converts it into PDF format and automatically generates a layout for printing. The storage unit can also use a generation AI to convert the contents of the end note into a printable format. For example, the generation AI analyzes the contents of the end note and converts it into a printable format with an optimal layout. Furthermore, the storage unit can also provide a function to assist in creating a physical album. For example, the generation AI automatically generates a photo book or scrapbook based on the contents of the end note. This allows the contents of the end note to be converted into a printable format and stored as a physical album.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The question generation unit can also generate questions based on the user's hobbies and interests. For example, if the user is interested in music, it can generate a question such as, "What is the music that has most influenced you?" If the user likes to travel, it can generate a question such as, "What is the most memorable place you have visited?" If the user is interested in cooking, it can generate a question such as, "What is your specialty dish?" This makes it possible to generate more personalized questions based on the user's hobbies and interests.

[0047] The question generation unit can also generate questions based on the user's health condition and lifestyle habits. For example, if the user is interested in health, it can generate a question such as, "What do you do to stay healthy?". If the user has a specific illness, it can also generate a question such as, "How do you deal with that illness?". Furthermore, if the user is accustomed to exercising, it can also generate a question such as, "What kind of exercise do you do on a daily basis?". This makes it possible to generate more relevant questions based on the user's health condition and lifestyle habits.

[0048] The question generation unit can analyze the user's past answers and generate follow-up questions based on the answers. For example, if the user answers "memories with family," a follow-up question such as "What is the most memorable episode from those memories?" can be generated. If the user answers "successful experiences at work," a question such as "What impact did that success experience have on you?" can also be generated. Furthermore, if the user answers "an episode related to a hobby," a question such as "What motivated you to start that hobby?" can also be generated. This makes it possible to generate more in-depth questions based on the user's past answers.

[0049] The question generation unit can generate questions based on the user's cultural background and religious beliefs. For example, if the user believes in a particular religion, it can generate a question such as, "How has that religion influenced your life?". Also, if the user belongs to a particular culture, it can generate a question such as, "What values ​​do you hold most dear in that culture?". Furthermore, if the user is interested in different cultures, it can generate a question such as, "What have you learned about that culture?". This makes it possible to generate more relevant questions based on the user's cultural background and religious beliefs.

[0050] The answer organizing unit can analyze the user's answers, extract keywords from the answers, and automatically tag them. For example, tags can be generated based on frequently occurring words and phrases in the answers. The answer organizing unit can also generate tags based on the content of the answers. For example, tags can be generated based on categories such as "memories," "values," and "messages." Furthermore, the answer organizing unit can analyze the content of the answers and automatically tag them with related keywords. This makes it possible to analyze the user's answers, extract keywords, and automatically tag them.

[0051] The answer organizing unit can organize the user's answers in chronological order and display important events in life in chronological order. For example, the events can be organized based on date and time information in the answers. The answer organizing unit can also organize the content of the answers in chronological order. For example, it can extract important events in the answers and display them in chronological order. Furthermore, the answer organizing unit can display the content of the answers in graph or timeline format. This allows the user's answers to be organized in chronological order and important events in life to be displayed in chronological order.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The question generation unit generates appropriate questions for the elderly. For example, the generation AI generates questions such as, "What is the most memorable event in your life?" or "Is there a message you would like to convey to your family?" The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates questions based on that prompt. Step 2: The answer collection unit collects answers from the elderly based on the questions generated by the question generation unit. For example, the answer collection unit converts the answers from the elderly into text using voice recognition technology. The answer collection unit can also scan handwritten answers and convert them into digital data. Step 3: The response organization unit organizes the collected responses. For example, the generation AI may categorize the elderly's responses and organize them into categories such as "memories," "values," and "messages." Step 4: The end note creation unit creates end notes based on the answers organized by the answer organization unit. For example, the generation AI automatically generates end notes based on the organized answers. Step 5: The storage unit stores the end note created by the end note creation unit in an encrypted cloud storage. For example, the end note is encrypted using AES encryption technology and stored in a secure cloud storage.

[0054] (Example 2) The AI ​​Endnote service according to an embodiment of the present invention allows elderly people to look back on their lives and record important memories, values, and messages they wish to convey. This service uses AI to ask appropriate questions and systematically create endnotes based on the elderly's answers. This allows the AI ​​Endnote service to help elderly people look back on their lives and put their important thoughts into words.

[0055] An AI endnote service according to an embodiment includes a question generation unit, an answer collection unit, an answer organization unit, an endnote creation unit, and a storage unit. The question generation unit generates appropriate questions for elderly people. For example, the generation AI generates questions such as, "What is the most memorable event in your life?" or "Is there a message you would like to convey to your family?" The generation AI receives input from a prompt containing instructions from the user about what the user wants the generation AI to do, and the generation AI generates questions based on the prompt. The answer collection unit collects answers from elderly people based on the questions generated by the question generation unit. For example, it converts the answers from elderly people into text using voice recognition technology. The answer collection unit can also scan handwritten answers and convert them into digital data. The answer organization unit organizes the collected answers. For example, the generation AI classifies the answers from elderly people by category and organizes them under categories such as "memories," "values," and "messages." The endnote creation unit creates endnotes based on the answers organized by the answer organization unit. For example, the generation AI automatically generates endnotes based on the organized answers. The storage unit stores the end notes created by the end note creation unit in encrypted cloud storage. For example, the end notes are encrypted using AES encryption technology and stored in secure cloud storage. This allows the AI ​​end note service according to the embodiment to enable elderly people to look back on their lives and record important memories, values, and messages they want to convey.

[0056] The question generation unit can analyze the elderly person's past social media posts and blog posts and generate individually customized questions. For example, the question generation unit collects the elderly person's past social media posts and blog posts and analyzes their content using natural language processing technology. For example, it extracts posts related to specific events or emotions and generates questions based on them. The question generation unit can also use generation AI to generate questions related to the content of past posts. For example, the generation AI generates follow-up questions such as, "Please tell me more about this post you wrote previously." This makes it possible to analyze the elderly person's past social media posts and blog posts and generate individually customized questions.

[0057] The question generation unit allows the generation AI to perform sentiment analysis of the elderly person's answers in real time and generate follow-up questions that correspond to the emotions. The question generation unit, for example, analyzes the elderly person's answers in real time and performs sentiment analysis. For example, if there is strong positive emotion in the answer, it generates a follow-up question that further draws out that emotion. In addition, if negative emotion is detected, the question generation unit can also generate a question that softens the emotion. For example, the generation AI generates a question that is sympathetic to the emotion, such as "How did you feel about that event?" This allows the generation AI to perform sentiment analysis of the elderly person's answers in real time and generate a follow-up question that corresponds to the emotion.

[0058] The question generation unit can use the emotion estimation function to estimate the emotional state of the elderly person and generate questions that elicit positive emotions. The question generation unit, for example, analyzes the elderly person's answers, facial expressions, and tone of voice, and estimates their emotional state using the emotion estimation function. For example, if a smile or a bright tone of voice is detected, a question that elicits positive emotions is generated. In addition, if a negative emotion is detected, the question generation unit can also generate a question that softens the emotion. For example, the generation AI generates a question that is sensitive to the emotion, such as, "How did you feel about that event?" This makes it possible to estimate the emotional state of the elderly person and generate questions that elicit positive emotions.

[0059] When generating questions, the question generation unit generates multimodal questions using audio and images, stimulating the visual and auditory senses. For example, when generating questions, the question generation unit generates multimodal questions that combine audio and images. For example, while displaying past photos, questions about the photos are asked by audio. The question generation unit can also generate questions that combine audio and text. For example, the generation AI asks questions by audio and displays the content in text. Furthermore, the question generation unit can also generate questions using video. For example, a video of a past event is played and questions about the content are asked. In this way, multimodal questions using audio and images can be generated, stimulating the visual and auditory senses.

[0060] The question generation unit can generate culturally appropriate questions for elderly people with different cultural backgrounds. For example, the question generation unit generates culturally appropriate questions for elderly people with different cultural backgrounds. For example, the question generation unit generates questions related to traditional events and customs in a particular culture. The question generation unit can also generate questions related to culture using a generation AI. For example, the generation AI generates a question such as, "What is the most important holiday in your culture?" Furthermore, the question generation unit can generate questions in different languages. For example, the generation AI generates questions in multiple languages, such as English and French. This makes it possible to generate culturally appropriate questions for elderly people with different cultural backgrounds.

[0061] The question generation unit uses the emotion estimation function to adjust the timing and order of questions, allowing it to ask questions that take the elderly person's emotions into consideration. For example, the question generation unit uses the emotion estimation function to monitor the elderly person's emotional state in real time and adjust the timing and order of questions. For example, it asks positive questions when emotions are high. The question generation unit can also ask deeper questions when emotions are calm. For example, the generation AI asks follow-up questions such as, "Tell me more about that incident." Furthermore, the question generation unit can adjust the order of questions according to changes in emotions. For example, if emotions are trending negatively, it asks positive questions first. This allows it to adjust the timing and order of questions and ask questions that take the elderly person's emotions into consideration.

[0062] The answer organization unit can analyze the answers of the elderly, extract keywords from the answers, and automatically tag them. For example, the answer organization unit can analyze the answers of the elderly using natural language processing technology and extract important keywords. For example, tags can be generated based on frequently occurring words and phrases in the answers. The answer organization unit can also use a generation AI to generate tags based on the content of the answers. For example, the generation AI generates tags based on categories such as "memories," "values," and "messages." Furthermore, the answer organization unit can analyze the content of the answers and automatically tag related keywords. For example, the generation AI extracts important information from the answers and generates tags based on that information. This makes it possible to analyze the answers of the elderly, extract keywords, and automatically tag them.

[0063] The answer organizing unit can organize the content of the answers in chronological order and display important life events in chronological order. The answer organizing unit, for example, organizes the answers of elderly people in chronological order, building a system that displays important life events in chronological order. For example, it organizes events based on date and time information in the answers. The answer organizing unit can also organize the content of the answers in chronological order using a generation AI. For example, the generation AI extracts important events in the answers and displays them in chronological order. Furthermore, the answer organizing unit can display the content of the answers in graph or timeline format. For example, the generation AI displays important events in the answers in a graph or timeline to make them easier to understand visually. This makes it possible to organize the content of the answers in chronological order and display important life events in chronological order.

[0064] The answer sorting unit can use the emotion estimation function to categorize answers based on the emotional intensity of the answers. The answer sorting unit, for example, performs emotion estimation on answers from elderly people and measures the emotional intensity. For example, answers with strong positive emotions are classified into a "joy" category. The answer sorting unit can also classify answers with strong negative emotions into a "sadness" category. For example, the generation AI analyzes the emotional intensity in the answers and categorizes them based on that. Furthermore, the answer sorting unit can also use the emotion estimation function to score the emotional intensity of the answers. For example, the generation AI quantifies the emotional intensity in the answers and categorizes them based on that. In this way, the emotion estimation function can be used to categorize answers based on the emotional intensity of the answers.

[0065] The answer organizing unit can convert the answers into visual notes or mind maps to make them easier to understand visually. The answer organizing unit, for example, converts the answers of elderly people into visual notes and builds a system to visually display them. For example, the main points of the answers are shown using diagrams or icons. The answer organizing unit can also convert the answers into mind maps using a generation AI. For example, the generation AI displays the content of the answers in mind map format. Furthermore, the answer organizing unit can convert the content of the answers into graphs or charts. For example, the generation AI converts important information in the answers into graphs or charts to make them easier to understand visually. This allows the answers to be converted into visual notes or mind maps to make them easier to understand visually.

[0066] The answer organization unit can automatically translate answers into different languages ​​and obtain feedback from an international perspective. The answer organization unit, for example, builds a system that automatically translates answers from elderly people into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The answer organization unit can also automatically translate answers using a generation AI. For example, the generation AI translates the content of the answer into a different language and collects feedback based on the translation results. Furthermore, the answer organization unit can build a system that evaluates the translated answers from an international perspective. For example, it collects feedback from users with different cultures and backgrounds and evaluates the answers based on that. This makes it possible to automatically translate answers into different languages ​​and obtain feedback from an international perspective.

[0067] The answer organizing unit can use the emotion estimation function to adjust the organizing method based on the emotional tone of the answers. For example, the answer organizing unit uses the emotion estimation function to analyze the emotional tone of the answers of the elderly person and adjust the organizing method based on the tone. For example, answers with a positive tone are preferentially displayed. The answer organizing unit can also classify answers with a negative tone into a different category. For example, the generation AI analyzes the emotional tone of the answers and adjusts the organizing method based on the emotional tone. Furthermore, the answer organizing unit can adjust the display order of the answers based on the emotional tone. For example, answers with a positive tone are displayed first and answers with a negative tone are displayed later. In this way, the emotion estimation function can be used to adjust the organizing method based on the emotional tone of the answers.

[0068] The end note creation unit can add images and videos to the end note to enhance the visual elements. For example, the end note creation unit adds past photos and videos of the elderly person to the end note, building a system to enhance the visual elements. For example, it embeds photos and videos related to the answer in the end note. The end note creation unit can also use the generation AI to automatically add images and videos to the end note. For example, the generation AI searches for related images and videos based on the content of the answer and adds them to the end note. Furthermore, the end note creation unit can provide an editing function for images and videos. For example, the generation AI can trim and filter images and videos and add them to the end note in the optimal format. This makes it possible to add images and videos to the end note to enhance the visual elements.

[0069] The end note creation unit can use the emotion estimation function to edit the contents of the end note so that it is easy to empathize emotionally. The end note creation unit, for example, uses the emotion estimation function to analyze the contents of the end note and build a system that edits it so that it is easy to empathize emotionally. For example, expressions that elicit positive emotions are added. The end note creation unit can also add expressions that soften negative emotions. For example, the generation AI analyzes the contents of the end note and edits it so that it is easy to empathize emotionally. Furthermore, the end note creation unit can also use the emotion estimation function to edit the contents of the end note in real time. For example, the generation AI analyzes the contents of the end note in real time and edits it so that it is easy to empathize emotionally. In this way, the emotion estimation function can be used to edit the contents of the end note so that it is easy to empathize emotionally.

[0070] The end note creation unit generates end notes in audio format, making it possible to accommodate visually impaired people. The end note creation unit, for example, builds a system that generates the contents of end notes in audio format, making it possible to accommodate visually impaired people. For example, it converts text into audio and plays end notes as audio. The end note creation unit can also use a generation AI to generate the contents of end notes in audio format. For example, the generation AI converts the contents of end notes into audio, making them accessible to visually impaired people. Furthermore, the end note creation unit can store end notes in audio format in cloud storage. For example, the generation AI encrypts end notes in audio format and stores them in secure cloud storage. This makes it possible to generate end notes in audio format, making it possible to accommodate visually impaired people.

[0071] The end note creation unit can publish the end note as an interactive web page, allowing users to add comments and feedback. The end note creation unit, for example, publishes the end note as an interactive web page and builds a system that allows users to add comments and feedback. For example, a comment section is provided for each section of the end note. The end note creation unit can also automatically generate an interactive web page using a generation AI. For example, the generation AI generates an interactive web page based on the contents of the end note, allowing users to add comments and feedback. Furthermore, the end note creation unit can provide a function that reflects comments and feedback in real time. For example, the generation AI analyzes user comments and feedback in real time and reflects them in the end note. This makes it possible to publish the end note as an interactive web page, allowing users to add comments and feedback.

[0072] The end note creation unit can use the emotion estimation function to adjust the content of the end note so that it is easier for family and friends to empathize emotionally. The end note creation unit, for example, uses the emotion estimation function to analyze the content of the end note and build a system that adjusts it so that it is easier for family and friends to empathize emotionally. For example, expressions that elicit positive emotions are added. The end note creation unit can also add expressions that soften negative emotions. For example, the generation AI analyzes the content of the end note and adjusts it so that it is easier for family and friends to empathize emotionally. Furthermore, the end note creation unit can also adjust the content of the end note in real time using the emotion estimation function. For example, the generation AI analyzes the content of the end note in real time and adjusts it so that it is easier for family and friends to empathize emotionally. In this way, the emotion estimation function can be used to adjust the content of the end note so that it is easier for family and friends to empathize emotionally.

[0073] The storage unit generates end notes in audio format, making it possible to accommodate visually impaired people. The storage unit, for example, builds a system that generates the contents of end notes in audio format, making it possible to accommodate visually impaired people. For example, it converts text into audio and plays end notes audibly. The storage unit can also use a generation AI to generate the contents of end notes in audio format. For example, the generation AI converts the contents of end notes into audio, making them accessible to visually impaired people. Furthermore, the storage unit can store end notes in audio format in cloud storage. For example, the generation AI encrypts end notes in audio format and stores them in secure cloud storage. This makes it possible to generate end notes in audio format, making it possible to accommodate visually impaired people.

[0074] The storage unit can publish end notes as interactive web pages, allowing users to add comments and feedback. For example, the storage unit can publish end notes as interactive web pages and build a system that allows users to add comments and feedback. For example, a comment section can be provided for each section of the end note. The storage unit can also automatically generate interactive web pages using a generation AI. For example, the generation AI can generate an interactive web page based on the contents of the end note, allowing users to add comments and feedback. Furthermore, the storage unit can provide a function that reflects comments and feedback in real time. For example, the generation AI can analyze user comments and feedback in real time and reflect them in the end note. This makes it possible to publish end notes as interactive web pages, allowing users to add comments and feedback.

[0075] The storage unit can use the emotion estimation function to adjust the contents of the end note so that it is easier for family and friends to empathize emotionally. For example, the storage unit uses the emotion estimation function to analyze the contents of the end note and build a system that adjusts it so that it is easier for family and friends to empathize emotionally. For example, expressions that elicit positive emotions are added. The storage unit can also add expressions that soften negative emotions. For example, the generation AI analyzes the contents of the end note and adjusts it so that it is easier for family and friends to empathize emotionally. Furthermore, the storage unit can also use the emotion estimation function to adjust the contents of the end note in real time. For example, the generation AI analyzes the contents of the end note in real time and adjusts it so that it is easier for family and friends to empathize emotionally. In this way, the emotion estimation function can be used to adjust the contents of the end note so that it is easier for family and friends to empathize emotionally.

[0076] The storage unit can monitor the emotional reactions of the sharing partner in real time when an EndNote is shared and collect feedback. The storage unit, for example, builds a system that monitors the emotional reactions of the sharing partner in real time when an EndNote is shared and collects that data. For example, it analyzes the facial expressions and voice of the sharing partner and calculates an emotional score. The storage unit can also analyze the emotional reactions of the sharing partner in real time using a generation AI. For example, the generation AI monitors the emotional reactions of the sharing partner in real time and collects feedback based on that data. Furthermore, the storage unit can automatically generate feedback based on the emotional reactions. For example, the generation AI analyzes the emotional reactions of the sharing partner and generates feedback based on that. In this way, the emotional reactions of the sharing partner can be monitored in real time when an EndNote is shared and feedback can be collected.

[0077] The storage unit can automatically record the editing history of end notes and enable changes to be tracked. For example, the storage unit builds a system that can automatically record the editing history of end notes and track changes. For example, the editing history is saved along with a timestamp. The storage unit can also automatically record the editing history using a generation AI. For example, the generation AI records the editing history of end notes in real time and tracks changes. Furthermore, the storage unit can provide a function to visually display the editing history. For example, the generation AI displays the editing history in graph or timeline format, making it easier to understand visually. This makes it possible to automatically record the editing history of end notes and enable changes to be tracked.

[0078] The storage unit can use the emotion estimation function to adjust the contents of the end note so that it is easier for the sharing recipient to empathize emotionally. For example, the storage unit uses the emotion estimation function to analyze the contents of the end note and build a system that adjusts the contents so that it is easier for the sharing recipient to empathize emotionally. For example, expressions that elicit positive emotions are added. The storage unit can also add expressions that soften negative emotions. For example, the generation AI analyzes the contents of the end note and adjusts it so that it is easier for the sharing recipient to empathize emotionally. Furthermore, the storage unit can also use the emotion estimation function to adjust the contents of the end note in real time. For example, the generation AI analyzes the contents of the end note in real time and adjusts it so that it is easier for the sharing recipient to empathize emotionally. In this way, the emotion estimation function can be used to adjust the contents of the end note so that it is easier for the sharing recipient to empathize emotionally.

[0079] The storage unit can link end notes with social media to make them easily shareable. For example, the storage unit can link end notes with social media to build a system that makes it easy to share them. For example, it can provide a function that allows the contents of end notes to be posted to social media with one click. The storage unit can also use a generation AI to automatically post the contents of end notes to social media. For example, the generation AI can analyze the contents of end notes and post them to social media at the appropriate time. Furthermore, the storage unit can also provide a function to collect feedback on social media. For example, the generation AI can analyze comments and reactions on social media and reflect them in end notes. This allows end notes to be linked with social media to make them easily shareable.

[0080] The storage unit can convert the contents of the end note into a printable format and store it as a physical album. The storage unit, for example, builds a system that converts the contents of the end note into a printable format and stores it as a physical album. For example, it converts it into PDF format and automatically generates a layout for printing. The storage unit can also use a generation AI to convert the contents of the end note into a printable format. For example, the generation AI analyzes the contents of the end note and converts it into a printable format with an optimal layout. Furthermore, the storage unit can also provide a function to assist in creating a physical album. For example, the generation AI automatically generates a photo book or scrapbook based on the contents of the end note. This allows the contents of the end note to be converted into a printable format and stored as a physical album.

[0081] The storage unit can use the emotion estimation function to adjust the contents of the end note so that it is easier for family and friends to empathize emotionally. For example, the storage unit uses the emotion estimation function to analyze the contents of the end note and build a system that adjusts it so that it is easier for family and friends to empathize emotionally. For example, expressions that elicit positive emotions are added. The storage unit can also add expressions that soften negative emotions. For example, the generation AI analyzes the contents of the end note and adjusts it so that it is easier for family and friends to empathize emotionally. Furthermore, the storage unit can also use the emotion estimation function to adjust the contents of the end note in real time. For example, the generation AI analyzes the contents of the end note in real time and adjusts it so that it is easier for family and friends to empathize emotionally. In this way, the emotion estimation function can be used to adjust the contents of the end note so that it is easier for family and friends to empathize emotionally.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The question generation unit can also generate questions based on the user's hobbies and interests. For example, if the user is interested in music, it can generate a question such as, "What is the music that has most influenced you?" If the user likes to travel, it can generate a question such as, "What is the most memorable place you have visited?" If the user is interested in cooking, it can generate a question such as, "What is your specialty dish?" This makes it possible to generate more personalized questions based on the user's hobbies and interests.

[0084] The question generation unit can also generate questions based on the user's health condition and lifestyle habits. For example, if the user is interested in health, it can generate a question such as, "What do you do to stay healthy?". If the user has a specific illness, it can also generate a question such as, "How do you deal with that illness?". Furthermore, if the user is accustomed to exercising, it can also generate a question such as, "What kind of exercise do you do on a daily basis?". This makes it possible to generate more relevant questions based on the user's health condition and lifestyle habits.

[0085] The question generation unit can estimate the emotional state of the user and generate questions according to the emotion. For example, if the user is sad, it generates a question that is close to the emotion, such as "How did that event make you feel?". If the user is happy, it can also generate a question that elicits positive emotions, such as "How happy did that event make you?". Furthermore, if the user is feeling anxious, it can also generate a question such as "How did you overcome that anxiety?". In this way, it is possible to generate questions according to the emotional state of the user.

[0086] The question generation unit can analyze the user's past answers and generate follow-up questions based on the answers. For example, if the user answers "memories with family," a follow-up question such as "What is the most memorable episode from those memories?" can be generated. If the user answers "successful experiences at work," a question such as "What impact did that success experience have on you?" can also be generated. Furthermore, if the user answers "an episode related to a hobby," a question such as "What motivated you to start that hobby?" can also be generated. This makes it possible to generate more in-depth questions based on the user's past answers.

[0087] The question generation unit can estimate the emotional state of the user and generate follow-up questions according to the emotion. For example, if the user is expressing positive emotion, a question that elicits positive emotion, such as "How happy did that event make you?", can be generated. If the user is expressing negative emotion, a question that is more in tune with the emotion, such as "How did that event make you feel?", can also be generated. Furthermore, if the user is in an emotionally neutral state, a question such as "What impact did that event have on you?" can be generated. This makes it possible to generate follow-up questions according to the emotional state of the user.

[0088] The question generation unit can generate questions based on the user's cultural background and religious beliefs. For example, if the user believes in a particular religion, it can generate a question such as, "How has that religion influenced your life?". Also, if the user belongs to a particular culture, it can generate a question such as, "What values ​​do you hold most dear in that culture?". Furthermore, if the user is interested in different cultures, it can generate a question such as, "What have you learned about that culture?". This makes it possible to generate more relevant questions based on the user's cultural background and religious beliefs.

[0089] The question generation unit can estimate the user's emotional state and adjust the timing and order of questions. For example, if the user is emotionally excited, positive questions can be asked first. On the other hand, if the user is emotionally calm, deeper questions can be asked. Furthermore, if the user's emotions are trending in a negative direction, positive questions can be asked first to calm the emotions. This makes it possible to adjust the timing and order of questions taking into account the user's emotional state.

[0090] The answer organizing unit can analyze the user's answers, extract keywords from the answers, and automatically tag them. For example, tags can be generated based on frequently occurring words and phrases in the answers. The answer organizing unit can also generate tags based on the content of the answers. For example, tags can be generated based on categories such as "memories," "values," and "messages." Furthermore, the answer organizing unit can analyze the content of the answers and automatically tag them with related keywords. This makes it possible to analyze the user's answers, extract keywords, and automatically tag them.

[0091] The answer organizing unit can organize the user's answers in chronological order and display important events in life in chronological order. For example, the events can be organized based on date and time information in the answers. The answer organizing unit can also organize the content of the answers in chronological order. For example, it can extract important events in the answers and display them in chronological order. Furthermore, the answer organizing unit can display the content of the answers in graph or timeline format. This allows the user's answers to be organized in chronological order and important events in life to be displayed in chronological order.

[0092] The answer organizing unit can estimate the emotional state of the user and categorize the answers based on the emotional intensity. For example, answers with a strong positive emotion can be categorized into a "joy" category. Also, answers with a strong negative emotion can be categorized into a "sadness" category. Furthermore, the answer organizing unit can score the emotional intensity and categorize the answers based on the score. This allows the user's emotional state to be estimated and categorize the answers based on the emotional intensity.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The question generation unit generates appropriate questions for the elderly. For example, the generation AI generates questions such as, "What is the most memorable event in your life?" or "Is there a message you would like to convey to your family?" The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates questions based on that prompt. Step 2: The answer collection unit collects answers from the elderly based on the questions generated by the question generation unit. For example, the answer collection unit converts the answers from the elderly into text using voice recognition technology. The answer collection unit can also scan handwritten answers and convert them into digital data. Step 3: The response organization unit organizes the collected responses. For example, the generation AI may categorize the elderly's responses and organize them into categories such as "memories," "values," and "messages." Step 4: The end note creation unit creates end notes based on the answers organized by the answer organization unit. For example, the generation AI automatically generates end notes based on the organized answers. Step 5: The storage unit stores the end note created by the end note creation unit in an encrypted cloud storage. For example, the end note is encrypted using AES encryption technology and stored in a secure cloud storage.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[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 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] 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.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] 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.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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 question generation unit that generates appropriate questions for elderly people; a response collection unit that collects responses from elderly people based on the questions generated by the question generation unit; an answer organizing unit that organizes the answers collected by the answer collecting unit; an end note creation unit that creates end notes based on the answers organized by the answer organization unit; A storage unit that stores the end note created by the end note creation unit in an encrypted cloud storage. A system characterized by:

2. The question generation unit Analyzing the elderly person's past social media posts and blog posts and generating individually customized questions 2. The system of claim 1.

3. The answer organizing unit The answers of the elderly person are analyzed, and keywords are extracted from the answers and automatically tagged.

2. The system of claim 1.

4. The end note creation unit Using an emotion estimation function, the contents of the end note are edited to be more emotionally relatable.

2. The system of claim 1.

5. The storage unit includes: When sharing the endnote, the emotional reaction of the sharing partner is monitored in real time and feedback is collected.

2. The system of claim 1.

6. The question generation unit The generation AI performs real-time sentiment analysis on the elderly person's answers and generates follow-up questions according to their emotions.

2. The system of claim 1.

7. The answer organizing unit Uses sentiment estimation to categorize responses based on their emotional intensity 2. The system of claim 1.

8. The end note creation unit Using an emotion estimation function, the contents of the end note are adjusted to make them more emotionally relatable to family and friends.

2. The system of claim 1.

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