System

Abstract

An object of a system according to an embodiment is to facilitate communication between grandparents and grandchildren with dementia.SOLUTION: A system includes a data collection unit, a generation unit, and a voice conversion unit. The data collection unit converts the past experiences, hobbies, and favorite foods of the grandparents into data. The generation unit generates a conversation with the grandchild based on the information converted into data by the data collection unit. The voice conversion unit converts the conversation generated by the generation unit into voice.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 makes it difficult for grandparents with dementia to communicate with their grandchildren, which can reduce opportunities for family bonding.

[0005] The system according to the embodiment aims to facilitate communication between grandparents with dementia and their grandchildren. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a generation unit, and a voice conversion unit. The data collection unit digitizes the grandparents' past experiences, hobbies, and favorite foods. The generation unit generates a conversation with the grandchild based on the information digitized by the data collection unit. The voice conversion unit converts the conversation generated by the generation unit into voice. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication between grandparents with dementia and their grandchildren. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication system according to an embodiment of the present invention is a system for enabling smooth communication between grandparents with dementia and their children. This system converts the grandparents' past experiences, hobbies, favorite foods, etc. into data, and a generation AI generates conversations with their grandchildren based on this data and converts them into voice. This allows the communication system to enable smooth communication between grandparents with dementia and their children.

[0029] A communication system according to an embodiment includes a data collection unit, a generation unit, and a voice conversion unit. The data collection unit digitizes grandparents' past experiences, hobbies, and favorite foods. For example, the data collection unit may collect information about events experienced by grandparents when they were young, their lifestyle habits at the time, and the tools they used in an interview format, and store the collected information as text data. The data collection unit may also collect information about the grandparents' favorite cooking recipes and hobbies (fishing, gardening, crafts, etc.), and store the collected information as text data. The generation unit generates a conversation with a grandchild based on the information digitized by the data collection unit. For example, when a grandchild asks, "Grandpa, what games did you play in your old days?", the generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate a response such as, "We used to play tag outside a lot. We didn't have a television, so we would all listen to the radio while we played." In addition, when a grandchild asks, "Grandpa, what's your favorite food?", the generation unit uses the generation AI to generate a response such as, "Grandpa has always loved sushi. I especially like tuna nigiri." The speech conversion unit converts the conversation generated by the generation unit into speech. For example, the speech conversion unit converts the text generated by the generation AI into speech using speech synthesis technology and conveys it to the grandchild. This enables the communication system to facilitate smooth communication between grandparents with dementia and their children. For example, by listening to their grandparents' old stories, grandchildren can share memories of their grandparents and deepen family bonds. Furthermore, by talking about their grandparents' hobbies and favorite foods, grandchildren can understand their grandparents' personalities and feel closer to them. Furthermore, the generation AI enables natural conversations, providing grandchildren with an experience that makes them feel as if they were talking to their grandparents when they were healthy.

[0030] The data collection unit can also store visual data using videos or photographs and link it to text data. For example, when collecting grandparents' past experiences, the data collection unit can store visual data using videos or photographs and link it to text data. For example, the data collection unit can collect photos and videos of grandparents when they were young and store related episodes as text data. The data collection unit can also store grandparents' past experiences as visual data using videos and photographs and link it to text data. For example, it can collect photos of tools used by grandparents and the house they lived in and store related episodes as text data. The data collection unit can also link visual data and text data to store grandparents' past experiences more specifically. For example, it can link photos of places where grandparents traveled to episodes related to those trips. This allows grandparents' past experiences to be stored more specifically.

[0031] The data collection unit can also collect episodes from the perspectives of other family members and integrate data from multiple perspectives. For example, when collecting past experiences of grandparents, the data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes from grandparents' children and grandchildren are also collected and integrated. The data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes from grandparents' siblings and friends are also collected and integrated. The data collection unit also integrates data from multiple perspectives to more specifically store past experiences of grandparents. For example, episodes from grandparents' friends and colleagues are also collected and integrated. In this way, by integrating data from multiple perspectives, it is possible to more specifically store past experiences of grandparents.

[0032] The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, when collecting information about the grandparents' past experiences, the data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, the data collection unit may collect information about the history and culture of the region where the grandparents lived and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, the data collection unit may collect information about local events and festivals that the grandparents participated in and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, the data collection unit may collect information about traditional events and customs of the region where the grandparents lived and integrate it. In this way, by collecting information about the history and culture of the region, it is possible to compile the data into a broader context.

[0033] The generation unit can refer to past conversation history and generate a consistent conversation. For example, when the generation AI generates a conversation, the generation unit refers to past conversation history and generates a consistent conversation. For example, it carries over the content that was discussed in the previous conversation. The generation unit also uses the generation AI to refer to past conversation history and generate a consistent conversation. For example, it generates answers to questions that came up in the previous conversation. The generation unit also refers to past conversation history and generates a consistent conversation when the generation AI generates a conversation. For example, it generates follow-up information about an event that was discussed in the previous conversation. This makes it possible to generate a consistent conversation.

[0034] The generation unit can also generate conversations in different languages, thereby realizing communication from an international perspective. For example, when the generation AI generates a conversation, the generation unit can also generate conversations in different languages, thereby realizing communication from an international perspective. For example, conversations are generated in multiple languages ​​such as English and French. The generation unit also uses the generation AI to generate conversations in different languages. For example, conversations are generated in a language that a grandchild is learning. The generation unit can also generate conversations in different languages, thereby realizing communication from an international perspective. For example, conversations are generated in languages ​​that the grandparents can speak. This makes it possible to generate conversations in different languages, thereby realizing communication from an international perspective.

[0035] The generation unit can also generate conversations between family members of different generations, thereby promoting communication throughout the family. For example, when the generation AI generates a conversation, the generation unit can also generate conversations between family members of different generations, thereby promoting communication throughout the family. For example, it can generate conversations not only between grandparents and grandchildren, but also between parents and siblings. The generation unit also uses the generation AI to generate conversations between family members of different generations. For example, it can generate conversations in which all family members participate. When the generation AI generates a conversation, the generation unit can also generate conversations between family members of different generations, thereby promoting communication throughout the family. For example, it can generate conversations about family events and occasions. This makes it possible to generate conversations between family members of different generations, thereby promoting communication throughout the family.

[0036] The voice conversion unit samples the grandparents' actual voices to generate more realistic voices. For example, when converting a conversation generated by the generation AI into voice, the voice conversion unit samples the grandparents' actual voices to generate more realistic voices. For example, the voices of the grandparents are recorded and voices are generated based on that voice. The voice conversion unit also uses the generation AI to sample the grandparents' actual voices to generate more realistic voices. For example, it learns the characteristics of the grandparents' voices and generates voices based on that voice. The voice conversion unit also samples the grandparents' actual voices to generate more realistic voices when converting a conversation generated by the generation AI into voice. For example, it reproduces the tone and intonation of the grandparents' voices. This allows the voice conversion unit to sample the grandparents' actual voices to generate more realistic voices.

[0037] The speech conversion unit can also generate speech in different languages, enabling communication from an international perspective. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit can also generate speech in different languages, enabling communication from an international perspective. For example, speech is generated in multiple languages, such as English and French. The speech conversion unit also uses the generation AI to generate speech in different languages. For example, speech is generated in a language that a grandchild is learning. The speech conversion unit can also generate speech in different languages, enabling communication from an international perspective, when converting a conversation generated by the generation AI into speech. For example, speech is generated in a language that a grandparent can speak. This allows speech to be generated in different languages, enabling communication from an international perspective.

[0038] The speech conversion unit can generate speech for conversations between family members of different generations, thereby promoting communication throughout the family. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit can generate speech for conversations between family members of different generations, thereby promoting communication throughout the family. For example, speech is generated for conversations between grandparents and grandchildren, as well as between parents and siblings. The speech conversion unit also uses the generation AI to generate speech for conversations between family members of different generations. For example, speech is generated for conversations in which all family members participate. The speech conversion unit also uses the generation AI to generate speech for conversations between family members of different generations, thereby promoting communication throughout the family. For example, speech is generated for conversations about family events and ceremonies. This allows speech to be generated for conversations between family members of different generations, thereby promoting communication throughout the family.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The data collection unit digitizes grandparents' past experiences, hobbies, and favorite foods. For example, the data collection unit may interview grandparents to gather information about events they experienced when they were young, their lifestyle habits at the time, and the tools they used, and store the information as text data. The data collection unit also collects information about grandparents' favorite recipes and hobbies (fishing, gardening, crafts, etc.) and stores the information as text data. The generation unit generates conversations with grandchildren based on the information digitized by the data collection unit. For example, if a grandchild asks, "Grandpa, what games did you play in the past?", the generation unit uses a generation AI (e.g., text generation AI or multimodal generation AI) to generate an answer such as, "We used to play tag outside a lot. We didn't have a television, so we would all play while listening to the radio." If a grandchild asks, "Grandpa, what's your favorite food?", the generation unit uses a generation AI to generate an answer such as, "Grandpa, I've always loved sushi. I especially like tuna nigiri." The speech conversion unit converts the conversation generated by the generation unit into speech. For example, the speech conversion unit converts the text generated by the generation AI into speech using speech synthesis technology and conveys it to grandchildren. This allows the communication system to facilitate smooth communication between grandparents who have begun to suffer from dementia and their children. For example, by listening to their grandparents' old stories, grandchildren can share memories of their grandparents and deepen family bonds. Also, by talking about their grandparents' hobbies and favorite foods, grandchildren can understand their grandparents' personalities and feel closer to them. Furthermore, the generation AI can realize natural conversations, providing grandchildren with an experience that makes them feel as if they were talking to their grandparents when they were healthy.

[0041] For example, when collecting the past experiences of grandparents, the data collection unit uses videos and photographs to store the experiences as visual data and links them to text data. For example, it collects photos and videos of grandparents when they were young and stores related episodes as text data. The data collection unit also uses videos and videos to store the past experiences of grandparents as visual data and links them to text data. For example, it collects photos of tools used by grandparents and the house they lived in and stores related episodes as text data. The data collection unit also links the visual data and text data to store the past experiences of grandparents more specifically. For example, it links photos of places where grandparents traveled to with episodes related to those trips. This allows the past experiences of grandparents to be stored more specifically.

[0042] For example, when collecting the past experiences of grandparents, the data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes are collected from the grandparents' children and grandchildren and integrated. The data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes are collected from the grandparents' siblings and friends and integrated. The data collection unit also integrates data from multiple perspectives to more specifically store the past experiences of grandparents. For example, episodes are collected from the grandparents' friends and colleagues and integrated. In this way, by integrating data from multiple perspectives, the past experiences of grandparents can be more specifically stored.

[0043] For example, when collecting information about the grandparents' past experiences, the data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, it may collect information about the history and culture of the region where the grandparents lived and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, it may collect information about local events and festivals that the grandparents attended and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, it may collect information about traditional events and customs of the region where the grandparents lived and integrate it. In this way, by collecting information about the history and culture of the region, it is possible to compile the data into a broader context.

[0044] For example, when the generation AI generates a conversation, the generation unit references past conversation history to generate a consistent conversation. For example, it carries over the content that was discussed in the previous conversation. The generation unit also uses the generation AI to reference past conversation history to generate a consistent conversation. For example, it generates answers to questions that came up in the previous conversation. The generation unit also references past conversation history to generate a consistent conversation when the generation AI generates a conversation. For example, it generates follow-up information about an event that was discussed in the previous conversation. This makes it possible to generate a consistent conversation.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The data collection department compiles data on the grandparents' past experiences, hobbies, and favorite foods. For example, the data collection department may use interviews to gather information about events the grandparents experienced when they were young, their lifestyle habits at the time, and the tools they used, and save this information as text data. The data collection department may also collect information about the grandparents' favorite cooking recipes and hobbies (fishing, gardening, crafts, etc.) and save this information as text data. Step 2: The generation unit generates a conversation with the grandchild based on the information digitized by the data collection unit. For example, if a grandchild asks, "Grandpa, what games did you play in the old days?", the generation unit uses a generation AI (e.g., text generation AI or multimodal generation AI) to generate an answer such as, "We used to play tag outside a lot. We didn't have a TV, so we would all play while listening to the radio." Similarly, if a grandchild asks, "Grandpa, what's your favorite food?", the generation unit uses a generation AI to generate an answer such as, "Grandpa has always loved sushi. I especially like tuna nigiri." Step 3: The speech conversion unit converts the conversation generated by the generation unit into speech. For example, the speech conversion unit converts the text generated by the generation AI into speech using speech synthesis technology and conveys it to the grandchild.

[0047] (Example 2) A communication system according to an embodiment of the present invention is a system for enabling smooth communication between grandparents with dementia and their children. This system converts the grandparents' past experiences, hobbies, favorite foods, etc. into data, and a generation AI generates conversations with their grandchildren based on this data and converts them into voice. This allows the communication system to enable smooth communication between grandparents with dementia and their children.

[0048] A communication system according to an embodiment includes a data collection unit, a generation unit, and a voice conversion unit. The data collection unit digitizes grandparents' past experiences, hobbies, and favorite foods. For example, the data collection unit may collect information about events experienced by grandparents when they were young, their lifestyle habits at the time, and the tools they used in an interview format, and store the collected information as text data. The data collection unit may also collect information about the grandparents' favorite cooking recipes and hobbies (fishing, gardening, crafts, etc.), and store the collected information as text data. The generation unit generates a conversation with a grandchild based on the information digitized by the data collection unit. For example, when a grandchild asks, "Grandpa, what games did you play in your old days?", the generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate a response such as, "We used to play tag outside a lot. We didn't have a television, so we would all listen to the radio while we played." In addition, when a grandchild asks, "Grandpa, what's your favorite food?", the generation unit uses the generation AI to generate a response such as, "Grandpa has always loved sushi. I especially like tuna nigiri." The speech conversion unit converts the conversation generated by the generation unit into speech. For example, the speech conversion unit converts the text generated by the generation AI into speech using speech synthesis technology and conveys it to the grandchild. This enables the communication system to facilitate smooth communication between grandparents with dementia and their children. For example, by listening to their grandparents' old stories, grandchildren can share memories of their grandparents and deepen family bonds. Furthermore, by talking about their grandparents' hobbies and favorite foods, grandchildren can understand their grandparents' personalities and feel closer to them. Furthermore, the generation AI enables natural conversations, providing grandchildren with an experience that makes them feel as if they were talking to their grandparents when they were healthy.

[0049] The data collection unit can use generative AI to perform emotion analysis and select important episodes based on the intensity and type of emotion. For example, when collecting grandparents' past experiences, the data collection unit can use generative AI to perform emotion analysis and select important episodes based on the intensity and type of emotion. For example, events to which the grandparents had particularly strong emotional reactions can be prioritized for data collection. The data collection unit also performs emotion analysis on the collected episodes to identify episodes with high emotional intensity. For example, it can select emotionally significant events such as weddings and the birth of children. The data collection unit also uses emotion analysis to extract particularly emotionally significant episodes from the grandparents' past experiences and digitize them in detail. For example, it can select war experiences and events that were major turning points. This allows emotionally significant episodes to be prioritized for data collection.

[0050] The data collection unit can also store visual data using videos or photographs and link it to text data. For example, when collecting grandparents' past experiences, the data collection unit can store visual data using videos or photographs and link it to text data. For example, the data collection unit can collect photos and videos of grandparents when they were young and store related episodes as text data. The data collection unit can also store grandparents' past experiences as visual data using videos and photographs and link it to text data. For example, it can collect photos of tools used by grandparents and the house they lived in and store related episodes as text data. The data collection unit can also link visual data and text data to store grandparents' past experiences more specifically. For example, it can link photos of places where grandparents traveled to episodes related to those trips. This allows grandparents' past experiences to be stored more specifically.

[0051] The data collection unit can use the emotion estimation function to identify emotionally significant events and conduct detailed interviews about those events. For example, the data collection unit uses the emotion estimation function to identify emotionally significant events in the grandparents' past experiences and conduct detailed interviews about those events. For example, specific questions are asked about events with high emotion scores to collect detailed information. The data collection unit also uses the emotion estimation function to identify emotionally significant events and conduct detailed interviews about those events. For example, interviews are conducted about events to which the grandparents had particularly emotional reactions and detailed episodes are collected. The data collection unit also uses the emotion estimation function to identify emotionally significant events and conduct detailed interviews about those events. For example, interviews are conducted about events with high emotion scores to collect detailed information. In this way, emotionally significant events can be collected in detail.

[0052] The data collection unit can also collect episodes from the perspectives of other family members and integrate data from multiple perspectives. For example, when collecting past experiences of grandparents, the data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes from grandparents' children and grandchildren are also collected and integrated. The data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes from grandparents' siblings and friends are also collected and integrated. The data collection unit also integrates data from multiple perspectives to more specifically store past experiences of grandparents. For example, episodes from grandparents' friends and colleagues are also collected and integrated. In this way, by integrating data from multiple perspectives, it is possible to more specifically store past experiences of grandparents.

[0053] The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, when collecting information about the grandparents' past experiences, the data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, the data collection unit may collect information about the history and culture of the region where the grandparents lived and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, the data collection unit may collect information about local events and festivals that the grandparents participated in and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, the data collection unit may collect information about traditional events and customs of the region where the grandparents lived and integrate it. In this way, by collecting information about the history and culture of the region, it is possible to compile the data into a broader context.

[0054] The data collection unit can use the emotion estimation function to analyze the grandchild's emotional reaction to the collected data and preferentially store episodes that are likely to be emotionally empathetic. The data collection unit, for example, uses the emotion estimation function to analyze the grandchild's emotional reaction to the collected data and preferentially store episodes that are likely to be emotionally empathetic. For example, it preferentially stores episodes to which the grandchild reacted particularly emotionally. The data collection unit also analyzes the grandchild's emotional reaction and preferentially stores episodes that are likely to be emotionally empathetic. For example, it preferentially stores episodes that the grandchild was particularly moved by. The data collection unit also uses the emotion estimation function to analyze the grandchild's emotional reaction to the collected data and preferentially store episodes that are likely to be emotionally empathetic. For example, it preferentially stores episodes that the grandchild was particularly interested in. This makes it possible to preferentially store episodes that are likely to be emotionally empathetic.

[0055] The generation unit can use the emotion estimation function to generate appropriate conversation content according to the grandchild's emotions. For example, when the generation AI generates a conversation, the generation unit uses the emotion estimation function to generate appropriate conversation content according to the grandchild's emotions. For example, if the grandchild is sad, it generates words of encouragement. The generation unit also uses the emotion estimation function to generate conversation content according to the grandchild's emotions. For example, if the grandchild is happy, it generates words of empathy. The generation unit also uses the emotion estimation function to generate appropriate conversation content according to the grandchild's emotions. For example, if the grandchild is in trouble, it generates advice. In this way, appropriate conversation content according to the grandchild's emotions can be generated.

[0056] The generation unit can refer to past conversation history and generate a consistent conversation. For example, when the generation AI generates a conversation, the generation unit refers to past conversation history and generates a consistent conversation. For example, it carries over the content that was discussed in the previous conversation. The generation unit also uses the generation AI to refer to past conversation history and generate a consistent conversation. For example, it generates answers to questions that came up in the previous conversation. The generation unit also refers to past conversation history and generates a consistent conversation when the generation AI generates a conversation. For example, it generates follow-up information about an event that was discussed in the previous conversation. This makes it possible to generate a consistent conversation.

[0057] The generation unit can use the emotion estimation function to prioritize emotionally important topics. For example, when the generation AI generates a conversation, the generation unit uses the emotion estimation function to prioritize emotionally important topics. For example, topics that a grandchild is particularly interested in are preferentially incorporated into the conversation. The generation unit also uses the generation AI to prioritize emotionally important topics using the emotion estimation function. For example, a conversation is generated about an event that particularly moved a grandchild. The generation unit also uses the emotion estimation function to prioritize emotionally important topics when the generation AI generates a conversation. For example, a conversation is generated about hobbies that a grandchild is particularly interested in. This makes it possible to prioritize emotionally important topics.

[0058] The generation unit can also generate conversations in different languages, thereby realizing communication from an international perspective. For example, when the generation AI generates a conversation, the generation unit can also generate conversations in different languages, thereby realizing communication from an international perspective. For example, conversations are generated in multiple languages ​​such as English and French. The generation unit also uses the generation AI to generate conversations in different languages. For example, conversations are generated in a language that a grandchild is learning. The generation unit can also generate conversations in different languages, thereby realizing communication from an international perspective. For example, conversations are generated in languages ​​that the grandparents can speak. This makes it possible to generate conversations in different languages, thereby realizing communication from an international perspective.

[0059] The generation unit can also generate conversations between family members of different generations, thereby promoting communication throughout the family. For example, when the generation AI generates a conversation, the generation unit can also generate conversations between family members of different generations, thereby promoting communication throughout the family. For example, it can generate conversations not only between grandparents and grandchildren, but also between parents and siblings. The generation unit also uses the generation AI to generate conversations between family members of different generations. For example, it can generate conversations in which all family members participate. When the generation AI generates a conversation, the generation unit can also generate conversations between family members of different generations, thereby promoting communication throughout the family. For example, it can generate conversations about family events and occasions. This makes it possible to generate conversations between family members of different generations, thereby promoting communication throughout the family.

[0060] The generation unit uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate optimal conversation content. For example, when the generation AI generates a conversation, the generation unit uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate optimal conversation content. For example, the conversation content is adjusted each time the grandchild's emotions change. The generation unit also uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate optimal conversation content. For example, if the grandchild is happy, it generates a fun topic. The generation unit also uses the emotion estimation function to monitor the grandchild's emotional reactions in real time when the generation AI generates a conversation and continuously generate optimal conversation content. For example, if the grandchild is in trouble, it generates advice. In this way, the generation unit can monitor the grandchild's emotional reactions in real time and continuously generate optimal conversation content.

[0061] The speech conversion unit can use the emotion estimation function to add emotionally appropriate tones and intonations. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit uses the emotion estimation function to add emotionally appropriate tones and intonations. For example, if a grandchild is sad, the speech conversion unit speaks in a gentle tone. The speech conversion unit also uses the generation AI to add emotionally appropriate tones and intonations using the emotion estimation function. For example, if a grandchild is happy, the speech conversion unit speaks in a cheerful tone. The speech conversion unit also uses the emotion estimation function to add emotionally appropriate tones and intonations when converting a conversation generated by the generation AI into speech. For example, if a grandchild is in trouble, the speech conversion unit speaks in a calm tone. This makes it possible to add emotionally appropriate tones and intonations.

[0062] The voice conversion unit samples the grandparents' actual voices to generate more realistic voices. For example, when converting a conversation generated by the generation AI into voice, the voice conversion unit samples the grandparents' actual voices to generate more realistic voices. For example, the voices of the grandparents are recorded and voices are generated based on that voice. The voice conversion unit also uses the generation AI to sample the grandparents' actual voices to generate more realistic voices. For example, it learns the characteristics of the grandparents' voices and generates voices based on that voice. The voice conversion unit also samples the grandparents' actual voices to generate more realistic voices when converting a conversation generated by the generation AI into voice. For example, it reproduces the tone and intonation of the grandparents' voices. This allows the voice conversion unit to sample the grandparents' actual voices to generate more realistic voices.

[0063] The speech conversion unit can use the emotion estimation function to emphasize emotionally important parts. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit uses the emotion estimation function to emphasize emotionally important parts. For example, the speech conversion unit emphasizes parts that a grandchild is particularly interested in. The speech conversion unit also uses the generation AI to emphasize emotionally important parts using the emotion estimation function. For example, the speech conversion unit emphasizes parts that a grandchild is particularly interested in. The speech conversion unit also uses the emotion estimation function to emphasize emotionally important parts when converting a conversation generated by the generation AI into speech. For example, the speech conversion unit emphasizes parts that a grandchild is particularly interested in. This allows emotionally important parts to be emphasized.

[0064] The speech conversion unit can also generate speech in different languages, enabling communication from an international perspective. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit can also generate speech in different languages, enabling communication from an international perspective. For example, speech is generated in multiple languages, such as English and French. The speech conversion unit also uses the generation AI to generate speech in different languages. For example, speech is generated in a language that a grandchild is learning. The speech conversion unit can also generate speech in different languages, enabling communication from an international perspective, when converting a conversation generated by the generation AI into speech. For example, speech is generated in a language that a grandparent can speak. This allows speech to be generated in different languages, enabling communication from an international perspective.

[0065] The speech conversion unit can generate speech for conversations between family members of different generations, thereby promoting communication throughout the family. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit can generate speech for conversations between family members of different generations, thereby promoting communication throughout the family. For example, speech is generated for conversations between grandparents and grandchildren, as well as between parents and siblings. The speech conversion unit also uses the generation AI to generate speech for conversations between family members of different generations. For example, speech is generated for conversations in which all family members participate. The speech conversion unit also uses the generation AI to generate speech for conversations between family members of different generations, thereby promoting communication throughout the family. For example, speech is generated for conversations about family events and ceremonies. This allows speech to be generated for conversations between family members of different generations, thereby promoting communication throughout the family.

[0066] The speech conversion unit uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate the optimal speech. For example, when converting a conversation generated by the generation AI into speech, the speech conversion unit uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate the optimal speech. For example, the tone and intonation of the speech are adjusted each time the grandchild's emotions change. The speech conversion unit also uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate the optimal speech. For example, if the grandchild is happy, speech is generated in a bright tone. The speech conversion unit also uses the emotion estimation function to monitor the grandchild's emotional reactions in real time and continuously generate the optimal speech when converting a conversation generated by the generation AI into speech and continuously generate the optimal speech. For example, if the grandchild is in trouble, speech is generated in a calm tone. This makes it possible to monitor the grandchild's emotional reactions in real time and continuously generate the optimal speech.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The data collection unit digitizes grandparents' past experiences, hobbies, and favorite foods. For example, the data collection unit may interview grandparents to gather information about events they experienced when they were young, their lifestyle habits at the time, and the tools they used, and store the information as text data. The data collection unit also collects information about grandparents' favorite recipes and hobbies (fishing, gardening, crafts, etc.) and stores the information as text data. The generation unit generates conversations with grandchildren based on the information digitized by the data collection unit. For example, if a grandchild asks, "Grandpa, what games did you play in the past?", the generation unit uses a generation AI (e.g., text generation AI or multimodal generation AI) to generate an answer such as, "We used to play tag outside a lot. We didn't have a television, so we would all play while listening to the radio." If a grandchild asks, "Grandpa, what's your favorite food?", the generation unit uses a generation AI to generate an answer such as, "Grandpa, I've always loved sushi. I especially like tuna nigiri." The speech conversion unit converts the conversation generated by the generation unit into speech. For example, the speech conversion unit converts the text generated by the generation AI into speech using speech synthesis technology and conveys it to grandchildren. This allows the communication system to facilitate smooth communication between grandparents who have begun to suffer from dementia and their children. For example, by listening to their grandparents' old stories, grandchildren can share memories of their grandparents and deepen family bonds. Also, by talking about their grandparents' hobbies and favorite foods, grandchildren can understand their grandparents' personalities and feel closer to them. Furthermore, the generation AI can realize natural conversations, providing grandchildren with an experience that makes them feel as if they were talking to their grandparents when they were healthy.

[0069] For example, when collecting grandparents' past experiences, the data collection unit uses generative AI to perform emotion analysis and select important episodes based on the intensity and type of emotion. For example, events to which the grandparents had particularly strong emotional reactions are prioritized for data collection. The data collection unit also performs emotion analysis on the collected episodes to identify episodes with high emotional intensity. For example, it selects emotionally significant events such as weddings and the birth of children. The data collection unit also uses emotion analysis to extract particularly emotionally strong episodes from the grandparents' past experiences and digitizes them in detail. For example, it selects war experiences and events that were major turning points. This allows emotionally significant episodes to be prioritized for data collection.

[0070] For example, when collecting the past experiences of grandparents, the data collection unit uses videos and photographs to store the experiences as visual data and links them to text data. For example, it collects photos and videos of grandparents when they were young and stores related episodes as text data. The data collection unit also uses videos and videos to store the past experiences of grandparents as visual data and links them to text data. For example, it collects photos of tools used by grandparents and the house they lived in and stores related episodes as text data. The data collection unit also links the visual data and text data to store the past experiences of grandparents more specifically. For example, it links photos of places where grandparents traveled to with episodes related to those trips. This allows the past experiences of grandparents to be stored more specifically.

[0071] The data collection unit, for example, uses the emotion estimation function to identify emotionally significant events in the grandparents' past experiences and conducts detailed interviews about those events. For example, specific questions are asked about events with high emotion scores to collect detailed information. The data collection unit also uses the emotion estimation function to identify emotionally significant events and conducts detailed interviews about those events. For example, interviews are conducted about events to which the grandparents had particularly emotional reactions and detailed episodes are collected. The data collection unit also uses the emotion estimation function to identify emotionally significant events and conducts detailed interviews about those events. For example, interviews are conducted about events with high emotion scores to collect detailed information. This makes it possible to collect emotionally significant events in detail.

[0072] For example, when collecting the past experiences of grandparents, the data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes are collected from the grandparents' children and grandchildren and integrated. The data collection unit also collects episodes from the perspectives of other family members and integrates data from multiple perspectives. For example, episodes are collected from the grandparents' siblings and friends and integrated. The data collection unit also integrates data from multiple perspectives to more specifically store the past experiences of grandparents. For example, episodes are collected from the grandparents' friends and colleagues and integrated. In this way, by integrating data from multiple perspectives, the past experiences of grandparents can be more specifically stored.

[0073] For example, when collecting information about the grandparents' past experiences, the data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, it may collect information about the history and culture of the region where the grandparents lived and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, it may collect information about local events and festivals that the grandparents attended and integrate it. The data collection unit may also collect information about the history and culture of the region, and compile the data into a broader context. For example, it may collect information about traditional events and customs of the region where the grandparents lived and integrate it. In this way, by collecting information about the history and culture of the region, it is possible to compile the data into a broader context.

[0074] The data collection unit, for example, uses an emotion estimation function to analyze the grandchild's emotional reaction to the collected data and prioritizes saving episodes that are likely to evoke emotional empathy. For example, it prioritizes saving episodes to which the grandchild responded particularly emotionally. The data collection unit also analyzes the grandchild's emotional reaction and prioritizes saving episodes that are likely to evoke emotional empathy. For example, it prioritizes saving episodes that the grandchild was particularly moved by. The data collection unit also uses the emotion estimation function to analyze the grandchild's emotional reaction to the collected data and prioritizes saving episodes that are likely to evoke emotional empathy. For example, it prioritizes saving episodes that the grandchild was particularly interested in. This makes it possible to prioritize saving episodes that are likely to evoke emotional empathy.

[0075] For example, when the generation AI generates a conversation, the generation unit uses the emotion estimation function to generate appropriate conversation content according to the grandchild's emotions. For example, if the grandchild is sad, it generates words of encouragement. The generation unit also uses the emotion estimation function to generate conversation content according to the grandchild's emotions. For example, if the grandchild is happy, it generates words of sympathy. The generation unit also uses the emotion estimation function to generate appropriate conversation content according to the grandchild's emotions. For example, if the grandchild is in trouble, it generates advice. In this way, appropriate conversation content according to the grandchild's emotions can be generated.

[0076] For example, when the generation AI generates a conversation, the generation unit references past conversation history to generate a consistent conversation. For example, it carries over the content that was discussed in the previous conversation. The generation unit also uses the generation AI to reference past conversation history to generate a consistent conversation. For example, it generates answers to questions that came up in the previous conversation. The generation unit also references past conversation history to generate a consistent conversation when the generation AI generates a conversation. For example, it generates follow-up information about an event that was discussed in the previous conversation. This makes it possible to generate a consistent conversation.

[0077] For example, when the generation AI generates a conversation, the generation unit uses the emotion estimation function to prioritize emotionally important topics. For example, topics that a grandchild is particularly interested in are prioritized for inclusion in the conversation. The generation unit also uses the generation AI to prioritize emotionally important topics using the emotion estimation function. For example, a conversation is generated about an event that particularly moved a grandchild. The generation unit also uses the emotion estimation function to prioritize emotionally important topics when the generation AI generates a conversation. For example, a conversation is generated about hobbies that a grandchild is particularly interested in. This makes it possible to prioritize emotionally important topics.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The data collection department compiles data on the grandparents' past experiences, hobbies, and favorite foods. For example, the data collection department may use interviews to gather information about events the grandparents experienced when they were young, their lifestyle habits at the time, and the tools they used, and save this information as text data. The data collection department may also collect information about the grandparents' favorite cooking recipes and hobbies (fishing, gardening, crafts, etc.) and save this information as text data. Step 2: The generation unit generates a conversation with the grandchild based on the information digitized by the data collection unit. For example, if a grandchild asks, "Grandpa, what games did you play in the old days?", the generation unit uses a generation AI (e.g., text generation AI or multimodal generation AI) to generate an answer such as, "We used to play tag outside a lot. We didn't have a TV, so we would all play while listening to the radio." Similarly, if a grandchild asks, "Grandpa, what's your favorite food?", the generation unit uses a generation AI to generate an answer such as, "Grandpa has always loved sushi. I especially like tuna nigiri." Step 3: The speech conversion unit converts the conversation generated by the generation unit into speech. For example, the speech conversion unit converts the text generated by the generation AI into speech using speech synthesis technology and conveys it to the grandchild.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[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 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.

[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 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).

[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] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that compiles data on grandparents' past experiences, hobbies, and favorite foods; a generation unit that generates a conversation with a grandchild based on the information digitized by the data collection unit; a voice conversion unit that converts the conversation generated by the generation unit into voice; A system characterized by:

2. The data collection unit When collecting grandparents' past experiences, the generative AI is used to perform sentiment analysis and select important episodes based on the intensity and type of emotion.

2. The system of claim 1.

3. The data collection unit When collecting grandparents' past experiences, save them as visual data using videos or photographs and link them to text data.

2. The system of claim 1.

4. The data collection unit Identify emotionally significant events and conduct in-depth interviews about those events 2. The system of claim 1.

5. The data collection unit Collecting stories from the perspectives of other family members and integrating data from multiple perspectives 2. The system of claim 1.

6. The data collection unit We will also collect information on the history and culture of the region and create data in a broader context.

2. The system of claim 1.

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