System

Abstract

An object of a system according to an embodiment is to appropriately grasp the health condition and mood of an elderly person and provide feedback to a family member or the like.SOLUTION: A system includes a picture and voice projection unit, a conversation analysis unit, and a feedback unit. The picture and voice projection unit projects a picture and a voice imitating a human. The conversation analysis unit performs a conversation with the elderly person using the picture and the voice projected by the picture and voice projection unit, and analyzes the conversation content. The feedback unit feeds back the health condition, mood, and the like of the elderly person to a family member or the like on the basis of the conversation content analyzed by the conversation analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to properly grasp elderly people's feelings of loneliness and health status and provide feedback to their families.

[0005] The system according to the embodiment aims to properly grasp the health condition and mood of an elderly person and provide feedback to their family and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a picture and voice projection unit, a conversation analysis unit, and a feedback unit. The picture and voice projection unit projects human-like pictures and voices. The conversation analysis unit uses the pictures and voices projected by the picture and voice projection unit to converse with the elderly person and analyze the content of the conversation. The feedback unit provides feedback to family members, etc., about the elderly person's health condition, mood, etc., based on the content of the conversation analyzed by the conversation analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately grasp the health condition and mood of an elderly person and provide feedback to family members and the like. [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 system according to an embodiment of the present invention is a system for solving social problems such as elderly care and lonely deaths that occur with the increasing elderly population. This system projects human-like images and voices and converses with elderly people, compiling the results and providing feedback on health status and other matters to family members. This system can reduce the sense of loneliness felt by elderly people and ease the burden of elderly care.

[0029] The system according to the embodiment includes a picture and voice projection unit, a conversation analysis unit, and a feedback unit. The picture and voice projection unit projects human-like pictures and voices. For example, the generation AI generates realistic human figures and voices based on user input. When an elderly person speaks to the system, the generation AI can recognize their voice and provide an appropriate response. The generation AI can also generate pictures and voices based on prompts. The conversation analysis unit uses the pictures and voices projected by the picture and voice projection unit to converse with the elderly person and analyze the content of the conversation. For example, the generation AI converts the conversation with the elderly person into text and analyzes the content to understand the elderly person's health condition, mood, etc. The generation AI can also perform analysis based on prompts that do not include instructions using a pre-finished model. The generation AI can also evaluate the elderly person's health condition and mood based on the content of the conversation. The feedback unit provides feedback to family members, etc., regarding the elderly person's health condition, mood, etc., based on the content of the conversation analyzed by the conversation analysis unit. For example, the generation AI converts the analysis results into text and sends them to family members via email or a messaging app. The generation AI can also provide real-time feedback based on the analysis results. The generation AI can also customize the feedback content and provide only the necessary information. As a result, the system according to the embodiment can provide feedback on the health condition and mood of the elderly to their families, thereby reducing feelings of loneliness and the burden of elderly care.

[0030] The picture and voice projection unit can generate more personalized pictures and voices using past photos and videos of the elderly person. For example, the picture and voice projection unit collects past photos and videos of the elderly person, and the generation AI generates personalized pictures and voices based on them. For example, it recreates pictures based on photos from when the elderly person was younger or voices extracted from past videos. The picture and voice projection unit also generates personalized pictures and voices using photos and videos provided by the elderly person's family. For example, it recreates pictures based on photos selected from a family album or voices extracted from family video messages. The picture and voice projection unit also allows the elderly person to upload photos and videos of their own choosing, and the generation AI generates personalized pictures and voices based on them. For example, it generates pictures and voices based on the elderly person's favorite photos and videos. This improves the quality of conversations by providing elderly people with personalized pictures and voices that are familiar to them.

[0031] The picture and voice projection unit can learn the tone and speaking style of the elderly person's voice and respond accordingly. For example, the picture and voice projection unit records the tone and speaking style of the elderly person's voice, and the generation AI learns from this. For example, it analyzes the characteristics of the elderly person's voice and generates responses that match it. In addition, the picture and voice projection unit learns the elderly person's speaking style and tone in real time through conversations with the elderly. For example, it captures changes in the elderly person's voice during conversation and responds accordingly. In addition, the picture and voice projection unit collects the tone and speaking style of the elderly person's voice in advance, and the generation AI learns from this. For example, it analyzes recordings of the elderly person's voice and generates responses based on that. This makes conversations more natural for the elderly.

[0032] The picture and voice projection unit projects the elderly person's favorite music or scenery onto the background, providing a relaxing environment for conversation. For example, the picture and voice projection unit plays the elderly person's favorite music in the background while the generation AI conducts the conversation. For example, the conversation can proceed while classical music or nostalgic songs are played. The picture and voice projection unit also projects the elderly person's favorite scenery onto the background while the generation AI conducts the conversation. For example, a sea or mountain scenery can be set as the background, allowing the conversation to take place in a relaxing environment. The picture and voice projection unit also sets the music or scenery selected by the elderly as the background while the generation AI conducts the conversation. For example, a background based on the music or scenery selected by the elderly themselves can be set. This allows the elderly to have a conversation in a relaxing environment, providing a sense of psychological security.

[0033] The picture and voice projection unit can generate pictures and voices that correspond to different cultures and languages. For example, to generate pictures and voices that correspond to different cultures and languages, the generation AI learns multilingual data. For example, it generates pictures and voices that correspond to multiple languages, such as English and Spanish. In addition, to generate pictures and voices that correspond to the cultural background of the elderly person, the generation AI learns culture-specific data. For example, it generates pictures and voices based on traditional Japanese clothing and scenery. In addition, to generate pictures and voices that correspond to different cultures and languages, the generation AI learns multicultural coexistence data. For example, it generates pictures and voices based on music and scenery from different cultures. This promotes multicultural coexistence and can accommodate elderly people with different cultural backgrounds.

[0034] The conversation analysis unit can perform more accurate analysis by referring to the elderly person's past conversation history. For example, the conversation analysis unit stores the elderly person's past conversation history in a database, and the generation AI refers to it to analyze the conversation content. For example, it analyzes changes in health status based on the past conversation content. The conversation analysis unit also refers to the elderly person's past conversation history in real time, and the generation AI analyzes the conversation content. For example, it analyzes changes in mood based on the past conversation content. The conversation analysis unit also collects the elderly person's past conversation history in advance, and the generation AI analyzes the conversation content based on that. For example, it analyzes changes in health status and mood based on the past conversation content. In this way, by referring to the past conversation history, more accurate analysis of health status and mood is possible.

[0035] The conversation analysis unit analyzes not only the text of the conversation content but also changes in voice and facial expression, allowing for a more multifaceted understanding of the health condition. For example, the conversation analysis unit analyzes not only the text of the conversation content with an elderly person but also changes in voice and facial expression. For example, the health condition is understood based on changes in voice tone and facial expression. The conversation analysis unit also analyzes the conversation content with the elderly person in real time and takes into account changes in voice and facial expression. For example, it analyzes changes in mood based on changes in voice tone and facial expression. The conversation analysis unit also collects the conversation content with the elderly person in advance and analyzes changes in voice and facial expression. For example, it analyzes changes in health condition and mood based on changes in voice tone and facial expression. In this way, by analyzing changes in voice and facial expression, it becomes possible to understand the health condition from more multifaceted perspectives.

[0036] The conversation analysis unit can compare the conversation data with other elderly people to identify common health risks. The conversation analysis unit, for example, analyzes the content of a conversation with an elderly person and compares it with the conversation data with other elderly people. For example, the conversation data of multiple elderly people is analyzed to identify common health risks. The conversation analysis unit also analyzes the content of a conversation with an elderly person in real time and compares it with the conversation data with other elderly people. For example, the conversation data of multiple elderly people is analyzed in real time to identify common health risks. The conversation analysis unit also collects the content of a conversation with an elderly person in advance and compares it with the conversation data with other elderly people. For example, the conversation data of multiple elderly people is analyzed in advance to identify common health risks. As a result, common health risks can be identified by comparing it with the conversation data with other elderly people.

[0037] The conversation analysis unit can learn the hobbies and interests of the elderly and improve the quality of the conversation. For example, the conversation analysis unit analyzes the content of a conversation with the elderly, and the generation AI learns the hobbies and interests of the elderly. For example, the quality of the conversation can be improved by having a conversation based on the hobbies and interests of the elderly. In addition, the conversation analysis unit analyzes the content of a conversation with the elderly in real time, and the generation AI learns the hobbies and interests of the elderly. For example, the quality of the conversation can be improved by having a conversation based on the hobbies and interests of the elderly. In addition, the conversation analysis unit collects the content of a conversation with the elderly in advance, and the generation AI learns the hobbies and interests of the elderly. For example, the quality of the conversation can be improved by having a conversation based on the hobbies and interests of the elderly. In this way, the quality of the conversation can be improved by learning the hobbies and interests of the elderly.

[0038] The feedback unit can customize the feedback content according to the wishes of the family member and provide only the necessary information. The feedback unit customizes the feedback content according to the wishes of the family member, for example, by providing only information about the health condition. The feedback unit also customizes the feedback content according to the wishes of the family member, for example, by providing only information about mood. The feedback unit also customizes the feedback content according to the wishes of the family member, for example, by providing a combination of information about the health condition and mood. This makes it possible to provide information according to the wishes of the family member.

[0039] The feedback unit can convert the feedback content into graphs and charts to make it easier to understand visually. For example, the feedback unit converts the results of the conversation summarized by the generation AI into graphs and charts and provides them to the family. For example, changes in health status are displayed in a line graph. The feedback unit also generates charts by the generation AI to make the feedback content easier to understand visually. For example, changes in mood are displayed in a pie chart. The feedback unit also generates graphs and charts to visually display the results of the conversation summarized by the generation AI. For example, changes in health status and mood are displayed in a bar graph. This makes it possible to provide feedback that is easier to understand visually.

[0040] The feedback unit can also share the feedback content with medical institutions and nursing care facilities, thereby realizing comprehensive care. For example, the feedback unit shares the results of the conversation summarized by the generation AI with medical institutions and nursing care facilities. For example, it reports changes in health condition to a doctor. The feedback unit can also share the feedback content with nursing care facilities, thereby realizing comprehensive care. For example, it reports changes in mood to nursing staff. The feedback unit can also share the results of the conversation summarized by the generation AI with medical institutions and nursing care facilities. For example, it reports changes in health condition and mood. In this way, comprehensive care can be realized by sharing information with medical institutions and nursing care facilities.

[0041] The feedback unit provides the feedback content as a voice message, making it possible to accommodate visually impaired family members as well. The feedback unit, for example, provides the results of a conversation summarized by the generation AI as a voice message. For example, it may report changes in health status by voice. The feedback unit also provides the feedback content as a voice message, making it possible to accommodate visually impaired family members as well. For example, it may report changes in mood by voice. The feedback unit also provides the results of a conversation summarized by the generation AI as a voice message. For example, it may report changes in health status or mood by voice. This makes it possible to provide feedback that is suitable for visually impaired family members as well.

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

[0043] The system can also include a motion analysis unit that monitors the amount of exercise an elderly person performs. The motion analysis unit uses cameras and sensors to record, for example, the walking and exercise movements that an elderly person performs daily and analyzes the data. For example, it analyzes walking speed and distance, and the number and accuracy of exercises, to detect insufficient or excessive exercise. The motion analysis unit can also evaluate the elderly person's physical strength and health status based on the motion data and propose an appropriate exercise plan. For example, if insufficient exercise is detected, it can suggest light stretching or walking, and if excessive exercise is detected, it can issue an alert urging the elderly person to rest. This can support the elderly person's exercise habits and contribute to maintaining their health.

[0044] The system can further include a dietary analysis unit that records and analyzes the dietary content of the elderly person. The dietary analysis unit, for example, takes photos of the meals eaten by the elderly person and analyzes the contents. For example, it calculates the nutritional balance and calories of the meals and provides dietary advice according to the elderly person's health condition. The dietary analysis unit can also grasp the elderly person's eating habits based on the dietary records and provide feedback to a nutritionist or doctor as needed. For example, if the elderly person continues to eat an unbalanced diet, the elderly person can receive advice from a nutritionist. The dietary analysis unit can also share the dietary records with family members and receive advice on dietary content. This can support the elderly person's eating habits and contribute to maintaining their health.

[0045] The system can further include a sleep analysis unit that monitors the sleep status of the elderly person. For example, the sleep analysis unit uses sensors to record the elderly person's movements and breathing while they sleep and analyzes the data. For example, it analyzes the quality, duration, and breathing rhythm of sleep to detect signs of sleep disorders. The sleep analysis unit can also evaluate the elderly person's sleep patterns based on the sleep data and suggest an appropriate sleeping environment. For example, if the elderly person is sleep deprived, it can suggest relaxing music and lighting and provide advice to improve sleep quality. The sleep analysis unit can also share the sleep data with family members and receive advice on their sleep status. This can support the elderly person's sleep habits and contribute to maintaining their health.

[0046] The system can further include a social activity analysis unit that records and analyzes the social activities of elderly people. The social activity analysis unit, for example, collects records of local events and club activities in which elderly people participate and analyzes the data. For example, it analyzes the frequency of participation and the content of activities to detect signs of social isolation. The social activity analysis unit can also evaluate the elderly person's social connections based on the social activity data and suggest appropriate activities. For example, if isolation is detected, it can issue an alert encouraging the elderly person to join a new club or event. The social activity analysis unit can also share the social activity data with family members and receive advice about social connections. This can support the elderly person's social activities and contribute to reducing feelings of isolation.

[0047] The system can further include a hobby analysis unit that supports the hobbies and interests of elderly people. The hobby analysis unit, for example, collects data on the hobbies and interests enjoyed by elderly people and analyzes the data. For example, it analyzes the frequency and content of hobbies and evaluates the fulfillment of hobby activities. The hobby analysis unit can also make suggestions to stimulate elderly people's interests based on the hobby data. For example, it can suggest activities to develop new hobbies or interests and expand the range of hobby activities. The hobby analysis unit can also share the hobby data with family members and receive advice on hobby activities. This can support the hobbies and interests of elderly people and improve their quality of life.

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

[0049] Step 1: The image and voice projection unit projects human-like images and voices. For example, the generation AI generates realistic human appearances and voices based on input from the user. The generation AI can also recognize the voice of an elderly person when they speak to it and respond appropriately. Furthermore, the generation AI can generate images and voices based on prompts. Step 2: The conversation analysis unit uses the images and voices projected by the image and voice projection unit to converse with the elderly person and analyze the content of the conversation. For example, the generation AI converts the conversation with the elderly person into text and analyzes the content to understand their health condition, mood, etc. The generation AI can also use a pre-finished model to perform analysis based on prompts that do not include instructions. Furthermore, the generation AI can also evaluate their health condition and mood based on the content of the conversation. Step 3: The feedback unit provides feedback to family members about the elderly person's health condition, mood, etc. based on the conversation content analyzed by the conversation analysis unit. For example, the generation AI converts the analysis results into text and sends it to the family member via email or messenger app. The generation AI can also provide real-time feedback based on the analysis results. Furthermore, the generation AI can customize the feedback content and provide only the necessary information.

[0050] (Example 2) A system according to an embodiment of the present invention is a system for solving social problems such as elderly care and lonely deaths that occur with the increasing elderly population. This system projects human-like images and voices and converses with elderly people, compiling the results and providing feedback on health status and other matters to family members. This system can reduce the sense of loneliness felt by elderly people and ease the burden of elderly care.

[0051] The system according to the embodiment includes a picture and voice projection unit, a conversation analysis unit, and a feedback unit. The picture and voice projection unit projects human-like pictures and voices. For example, the generation AI generates realistic human figures and voices based on user input. When an elderly person speaks to the system, the generation AI can recognize their voice and provide an appropriate response. The generation AI can also generate pictures and voices based on prompts. The conversation analysis unit uses the pictures and voices projected by the picture and voice projection unit to converse with the elderly person and analyze the content of the conversation. For example, the generation AI converts the conversation with the elderly person into text and analyzes the content to understand the elderly person's health condition, mood, etc. The generation AI can also perform analysis based on prompts that do not include instructions using a pre-finished model. The generation AI can also evaluate the elderly person's health condition and mood based on the content of the conversation. The feedback unit provides feedback to family members, etc., regarding the elderly person's health condition, mood, etc., based on the content of the conversation analyzed by the conversation analysis unit. For example, the generation AI converts the analysis results into text and sends them to family members via email or a messaging app. The generation AI can also provide real-time feedback based on the analysis results. The generation AI can also customize the feedback content and provide only the necessary information. As a result, the system according to the embodiment can provide feedback on the health condition and mood of the elderly to their families, thereby reducing feelings of loneliness and the burden of elderly care.

[0052] The picture and voice projection unit can generate more personalized pictures and voices using past photos and videos of the elderly person. For example, the picture and voice projection unit collects past photos and videos of the elderly person, and the generation AI generates personalized pictures and voices based on them. For example, it recreates pictures based on photos from when the elderly person was younger or voices extracted from past videos. The picture and voice projection unit also generates personalized pictures and voices using photos and videos provided by the elderly person's family. For example, it recreates pictures based on photos selected from a family album or voices extracted from family video messages. The picture and voice projection unit also allows the elderly person to upload photos and videos of their own choosing, and the generation AI generates personalized pictures and voices based on them. For example, it generates pictures and voices based on the elderly person's favorite photos and videos. This improves the quality of conversations by providing elderly people with personalized pictures and voices that are familiar to them.

[0053] The picture and voice projection unit can learn the tone and speaking style of the elderly person's voice and respond accordingly. For example, the picture and voice projection unit records the tone and speaking style of the elderly person's voice, and the generation AI learns from this. For example, it analyzes the characteristics of the elderly person's voice and generates responses that match it. In addition, the picture and voice projection unit learns the elderly person's speaking style and tone in real time through conversations with the elderly. For example, it captures changes in the elderly person's voice during conversation and responds accordingly. In addition, the picture and voice projection unit collects the tone and speaking style of the elderly person's voice in advance, and the generation AI learns from this. For example, it analyzes recordings of the elderly person's voice and generates responses based on that. This makes conversations more natural for the elderly.

[0054] The picture and voice projection unit can use the emotion estimation function to generate pictures and voices that correspond to the emotional state of the elderly person. For example, the picture and voice projection unit analyzes the elderly person's facial expressions and tone of voice and uses the emotion estimation function to grasp their emotional state. For example, it generates pictures and voices based on photos of smiling faces and cheerful voices. The picture and voice projection unit also analyzes the elderly person's emotional state in real time and generates pictures and voices that correspond to that. For example, it generates pictures and voices based on sad faces and depressed voices. The picture and voice projection unit also collects the elderly person's emotional state in advance, and the generation AI generates pictures and voices that correspond to the emotions based on that information. For example, pictures and voices that correspond to the emotional state are set in advance and generated based on that. This increases emotional empathy by providing pictures and voices that correspond to the elderly person's emotions.

[0055] The picture and voice projection unit projects the elderly person's favorite music or scenery onto the background, providing a relaxing environment for conversation. For example, the picture and voice projection unit plays the elderly person's favorite music in the background while the generation AI conducts the conversation. For example, the conversation can proceed while classical music or nostalgic songs are played. The picture and voice projection unit also projects the elderly person's favorite scenery onto the background while the generation AI conducts the conversation. For example, a sea or mountain scenery can be set as the background, allowing the conversation to take place in a relaxing environment. The picture and voice projection unit also sets the music or scenery selected by the elderly as the background while the generation AI conducts the conversation. For example, a background based on the music or scenery selected by the elderly themselves can be set. This allows the elderly to have a conversation in a relaxing environment, providing a sense of psychological security.

[0056] The picture and voice projection unit can generate pictures and voices that correspond to different cultures and languages. For example, to generate pictures and voices that correspond to different cultures and languages, the generation AI learns multilingual data. For example, it generates pictures and voices that correspond to multiple languages, such as English and Spanish. In addition, to generate pictures and voices that correspond to the cultural background of the elderly person, the generation AI learns culture-specific data. For example, it generates pictures and voices based on traditional Japanese clothing and scenery. In addition, to generate pictures and voices that correspond to different cultures and languages, the generation AI learns multicultural coexistence data. For example, it generates pictures and voices based on music and scenery from different cultures. This promotes multicultural coexistence and can accommodate elderly people with different cultural backgrounds.

[0057] The picture and voice projection unit can use the emotion estimation function to analyze the emotions of the elderly person when they speak in real time, and generate pictures and voices that elicit positive emotions. For example, the picture and voice projection unit can analyze the emotions of the elderly person when they speak in real time, and generate pictures and voices that elicit positive emotions. For example, it can generate a picture of a smiling face and a cheerful voice. The picture and voice projection unit can also analyze the emotional state of the elderly person in real time, and generate pictures and voices that elicit positive emotions. For example, it can generate encouraging pictures and voices. The picture and voice projection unit can also analyze the emotional state of the elderly person in real time, and generate pictures and voices that elicit positive emotions. For example, it can generate encouraging words and pictures of a smiling face. This can elicit positive emotions from the elderly, providing a sense of psychological security.

[0058] The conversation analysis unit can perform more accurate analysis by referring to the elderly person's past conversation history. For example, the conversation analysis unit stores the elderly person's past conversation history in a database, and the generation AI refers to it to analyze the conversation content. For example, it analyzes changes in health status based on the past conversation content. The conversation analysis unit also refers to the elderly person's past conversation history in real time, and the generation AI analyzes the conversation content. For example, it analyzes changes in mood based on the past conversation content. The conversation analysis unit also collects the elderly person's past conversation history in advance, and the generation AI analyzes the conversation content based on that. For example, it analyzes changes in health status and mood based on the past conversation content. In this way, by referring to the past conversation history, more accurate analysis of health status and mood is possible.

[0059] The conversation analysis unit analyzes not only the text of the conversation content but also changes in voice and facial expression, allowing for a more multifaceted understanding of the health condition. For example, the conversation analysis unit analyzes not only the text of the conversation content with an elderly person but also changes in voice and facial expression. For example, the health condition is understood based on changes in voice tone and facial expression. The conversation analysis unit also analyzes the conversation content with the elderly person in real time and takes into account changes in voice and facial expression. For example, it analyzes changes in mood based on changes in voice tone and facial expression. The conversation analysis unit also collects the conversation content with the elderly person in advance and analyzes changes in voice and facial expression. For example, it analyzes changes in health condition and mood based on changes in voice tone and facial expression. In this way, by analyzing changes in voice and facial expression, it becomes possible to understand the health condition from more multifaceted perspectives.

[0060] The conversation analysis unit uses the emotion estimation function to analyze changes in emotions during a conversation, and can also grasp the emotional health state. The conversation analysis unit, for example, uses the emotion estimation function to analyze changes in emotions during a conversation with an elderly person. For example, the health state is grasped based on changes in emotions during a conversation. The conversation analysis unit also analyzes changes in emotions during a conversation with an elderly person in real time to grasp the emotional health state. For example, it analyzes changes in mood based on changes in emotions during a conversation. The conversation analysis unit also collects changes in emotions during a conversation with an elderly person in advance and analyzes them using the emotion estimation function. For example, it analyzes changes in health state and mood based on changes in emotions during a conversation. In this way, the emotional health state can also be grasped by analyzing changes in emotions.

[0061] The conversation analysis unit can compare the conversation data with other elderly people to identify common health risks. The conversation analysis unit, for example, analyzes the content of a conversation with an elderly person and compares it with the conversation data with other elderly people. For example, the conversation data of multiple elderly people is analyzed to identify common health risks. The conversation analysis unit also analyzes the content of a conversation with an elderly person in real time and compares it with the conversation data with other elderly people. For example, the conversation data of multiple elderly people is analyzed in real time to identify common health risks. The conversation analysis unit also collects the content of a conversation with an elderly person in advance and compares it with the conversation data with other elderly people. For example, the conversation data of multiple elderly people is analyzed in advance to identify common health risks. As a result, common health risks can be identified by comparing it with the conversation data with other elderly people.

[0062] The conversation analysis unit can learn the hobbies and interests of the elderly and improve the quality of the conversation. For example, the conversation analysis unit analyzes the content of a conversation with the elderly, and the generation AI learns the hobbies and interests of the elderly. For example, the quality of the conversation can be improved by having a conversation based on the hobbies and interests of the elderly. In addition, the conversation analysis unit analyzes the content of a conversation with the elderly in real time, and the generation AI learns the hobbies and interests of the elderly. For example, the quality of the conversation can be improved by having a conversation based on the hobbies and interests of the elderly. In addition, the conversation analysis unit collects the content of a conversation with the elderly in advance, and the generation AI learns the hobbies and interests of the elderly. For example, the quality of the conversation can be improved by having a conversation based on the hobbies and interests of the elderly. In this way, the quality of the conversation can be improved by learning the hobbies and interests of the elderly.

[0063] The conversation analysis unit can use the emotion estimation function to analyze changes in emotions during a conversation in real time and provide emotional support. The conversation analysis unit, for example, uses the emotion estimation function to analyze changes in emotions during a conversation with an elderly person in real time. For example, support can be provided according to the changes in emotions. The conversation analysis unit can also analyze changes in emotions during a conversation with an elderly person in real time and provide emotional support. For example, words of encouragement or comfort can be provided according to the changes in emotions. The conversation analysis unit can also analyze changes in emotions during a conversation with an elderly person in real time and provide emotional support. For example, an appropriate response can be provided according to the changes in emotions. In this way, emotional support can be provided by analyzing changes in emotions in real time.

[0064] The feedback unit can customize the feedback content according to the wishes of the family member and provide only the necessary information. The feedback unit customizes the feedback content according to the wishes of the family member, for example, by providing only information about the health condition. The feedback unit also customizes the feedback content according to the wishes of the family member, for example, by providing only information about mood. The feedback unit also customizes the feedback content according to the wishes of the family member, for example, by providing a combination of information about the health condition and mood. This makes it possible to provide information according to the wishes of the family member.

[0065] The feedback unit can convert the feedback content into graphs and charts to make it easier to understand visually. For example, the feedback unit converts the results of the conversation summarized by the generation AI into graphs and charts and provides them to the family. For example, changes in health status are displayed in a line graph. The feedback unit also generates charts by the generation AI to make the feedback content easier to understand visually. For example, changes in mood are displayed in a pie chart. The feedback unit also generates graphs and charts to visually display the results of the conversation summarized by the generation AI. For example, changes in health status and mood are displayed in a bar graph. This makes it possible to provide feedback that is easier to understand visually.

[0066] The feedback unit uses the emotion estimation function to include the emotional state of the elderly in the feedback content, making it easier for family members to provide emotional support. The feedback unit, for example, uses the emotion estimation function to include the emotional state of the elderly in the feedback content. For example, changes in emotion during a conversation are reflected in text. The feedback unit also analyzes the emotional state of the elderly in real time and includes it in the feedback content. For example, changes in emotion are displayed in a graph. The feedback unit also uses the emotion estimation function to include the emotional state of the elderly in the feedback content. For example, changes in emotion are displayed in a chart. This makes it possible to provide feedback that includes the emotional state of the elderly.

[0067] The feedback unit can also share the feedback content with medical institutions and nursing care facilities, thereby realizing comprehensive care. For example, the feedback unit shares the results of the conversation summarized by the generation AI with medical institutions and nursing care facilities. For example, it reports changes in health condition to a doctor. The feedback unit can also share the feedback content with nursing care facilities, thereby realizing comprehensive care. For example, it reports changes in mood to nursing staff. The feedback unit can also share the results of the conversation summarized by the generation AI with medical institutions and nursing care facilities. For example, it reports changes in health condition and mood. In this way, comprehensive care can be realized by sharing information with medical institutions and nursing care facilities.

[0068] The feedback unit provides the feedback content as a voice message, making it possible to accommodate visually impaired family members as well. The feedback unit, for example, provides the results of a conversation summarized by the generation AI as a voice message. For example, it may report changes in health status by voice. The feedback unit also provides the feedback content as a voice message, making it possible to accommodate visually impaired family members as well. For example, it may report changes in mood by voice. The feedback unit also provides the results of a conversation summarized by the generation AI as a voice message. For example, it may report changes in health status or mood by voice. This makes it possible to provide feedback that is suitable for visually impaired family members as well.

[0069] The feedback unit uses the emotion estimation function to reflect the emotional reactions of the family members in the feedback content, thereby enabling more appropriate information to be provided. The feedback unit, for example, uses the emotion estimation function to reflect the emotional reactions of the family members in the feedback content. For example, information is provided according to the emotions of the family members. The feedback unit also analyzes the emotional reactions of the family members in real time and reflects this in the feedback content. For example, appropriate information is provided according to the emotions of the family members. The feedback unit also uses the emotion estimation function to reflect the emotional reactions of the family members in the feedback content. For example, information is provided according to the emotions of the family members. This enables feedback that reflects the emotional reactions of the family members to be provided.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The system can also include a motion analysis unit that monitors the amount of exercise an elderly person performs. The motion analysis unit uses cameras and sensors to record, for example, the walking and exercise movements that an elderly person performs daily and analyzes the data. For example, it analyzes walking speed and distance, and the number and accuracy of exercises, to detect insufficient or excessive exercise. The motion analysis unit can also evaluate the elderly person's physical strength and health status based on the motion data and propose an appropriate exercise plan. For example, if insufficient exercise is detected, it can suggest light stretching or walking, and if excessive exercise is detected, it can issue an alert urging the elderly person to rest. This can support the elderly person's exercise habits and contribute to maintaining their health.

[0072] The system can further include a dietary analysis unit that records and analyzes the dietary content of the elderly person. The dietary analysis unit, for example, takes photos of the meals eaten by the elderly person and analyzes the contents. For example, it calculates the nutritional balance and calories of the meals and provides dietary advice according to the elderly person's health condition. The dietary analysis unit can also grasp the elderly person's eating habits based on the dietary records and provide feedback to a nutritionist or doctor as needed. For example, if the elderly person continues to eat an unbalanced diet, the elderly person can receive advice from a nutritionist. The dietary analysis unit can also share the dietary records with family members and receive advice on dietary content. This can support the elderly person's eating habits and contribute to maintaining their health.

[0073] The system can further include a sleep analysis unit that monitors the sleep status of the elderly person. For example, the sleep analysis unit uses sensors to record the elderly person's movements and breathing while they sleep and analyzes the data. For example, it analyzes the quality, duration, and breathing rhythm of sleep to detect signs of sleep disorders. The sleep analysis unit can also evaluate the elderly person's sleep patterns based on the sleep data and suggest an appropriate sleeping environment. For example, if the elderly person is sleep deprived, it can suggest relaxing music and lighting and provide advice to improve sleep quality. The sleep analysis unit can also share the sleep data with family members and receive advice on their sleep status. This can support the elderly person's sleep habits and contribute to maintaining their health.

[0074] The system can further include a social activity analysis unit that records and analyzes the social activities of elderly people. The social activity analysis unit, for example, collects records of local events and club activities in which elderly people participate and analyzes the data. For example, it analyzes the frequency of participation and the content of activities to detect signs of social isolation. The social activity analysis unit can also evaluate the elderly person's social connections based on the social activity data and suggest appropriate activities. For example, if isolation is detected, it can issue an alert encouraging the elderly person to join a new club or event. The social activity analysis unit can also share the social activity data with family members and receive advice about social connections. This can support the elderly person's social activities and contribute to reducing feelings of isolation.

[0075] The system can further include a hobby analysis unit that supports the hobbies and interests of elderly people. The hobby analysis unit, for example, collects data on the hobbies and interests enjoyed by elderly people and analyzes the data. For example, it analyzes the frequency and content of hobbies and evaluates the fulfillment of hobby activities. The hobby analysis unit can also make suggestions to stimulate elderly people's interests based on the hobby data. For example, it can suggest activities to develop new hobbies or interests and expand the range of hobby activities. The hobby analysis unit can also share the hobby data with family members and receive advice on hobby activities. This can support the hobbies and interests of elderly people and improve their quality of life.

[0076] The system can further include an emotional response unit that estimates the emotional state of the elderly person and provides music and videos corresponding to the emotion. The emotional response unit, for example, analyzes the facial expression and tone of voice of the elderly person to estimate the emotional state. For example, it provides relaxing music and fun videos corresponding to the emotional state. The emotional response unit can also analyze the emotional state in real time and provide content corresponding to the emotion. For example, if a sad expression is detected, it provides uplifting music and videos. The emotional response unit can also collect the emotional state in advance and provide content based on that. For example, it can set music and videos corresponding to the emotional state in advance and provide content based on that. This makes it possible to provide emotional support by providing music and videos corresponding to the elderly person's emotions.

[0077] The system may further include an emotional exercise suggestion unit that estimates the emotional state of the elderly person and suggests exercises and relaxation activities according to the emotions. The emotional exercise suggestion unit, for example, analyzes the facial expressions and tone of voice of the elderly person to estimate the emotional state. For example, it suggests light stretching or deep breathing according to the emotional state. The emotional exercise suggestion unit can also analyze the emotional state in real time and suggest exercises and relaxation activities according to the emotions. For example, if stress is detected, it suggests yoga or meditation to help relax. The emotional exercise suggestion unit can also collect the emotional state in advance and suggest exercises and relaxation activities based on that. For example, it can set exercises and relaxation activities according to the emotional state in advance and make suggestions based on that. This makes it possible to suggest exercises and relaxation activities according to the emotions of the elderly person.

[0078] The system can further include an emotional meal suggestion unit that estimates the emotional state of the elderly person and suggests meals according to the emotions. The emotional meal suggestion unit, for example, analyzes the facial expressions and tone of voice of the elderly person to estimate the emotional state. For example, it suggests relaxing meals or energizing meals according to the emotional state. The emotional meal suggestion unit can also analyze the emotional state in real time and suggest meals according to the emotions. For example, if stress is detected, it suggests relaxing herbal tea or energizing nutritious meals. The emotional meal suggestion unit can also collect the emotional state in advance and suggest meals based on that. For example, it sets meals according to the emotional state in advance and makes suggestions based on that. This makes it possible to suggest meals according to the emotions of the elderly person.

[0079] The system may further include an emotion and hobby suggestion unit that estimates the emotional state of the elderly person and suggests hobbies and activities according to the emotion. The emotion and hobby suggestion unit, for example, analyzes the facial expression and tone of voice of the elderly person to estimate the emotional state. For example, it suggests relaxing hobbies and uplifting activities according to the emotional state. The emotion and hobby suggestion unit can also analyze the emotional state in real time and suggest hobbies and activities according to the emotion. For example, if stress is detected, it suggests painting to relax or taking a walk to uplift. The emotion and hobby suggestion unit can also collect the emotional state in advance and suggest hobbies and activities based on the collected emotional state. For example, it sets hobbies and activities according to the emotional state in advance and makes suggestions based on them. This makes it possible to suggest hobbies and activities according to the emotions of the elderly person.

[0080] The system can further include an emotional communication suggestion unit that estimates the emotional state of the elderly person and suggests communication in accordance with the emotion. The emotional communication suggestion unit, for example, analyzes the facial expressions and tone of voice of the elderly person to estimate the emotional state. For example, it suggests relaxing conversations or uplifting conversations in accordance with the emotional state. The emotional communication suggestion unit can also analyze the emotional state in real time and suggest communication in accordance with the emotion. For example, if stress is detected, it suggests topics that are relaxing or uplifting. The emotional communication suggestion unit can also collect the emotional state in advance and suggest communication based on that. For example, it sets communication in accordance with the emotional state in advance and makes suggestions based on that. This makes it possible to suggest communication in accordance with the emotions of the elderly person.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The image and voice projection unit projects human-like images and voices. For example, the generation AI generates realistic human appearances and voices based on input from the user. The generation AI can also recognize the voice of an elderly person when they speak to it and respond appropriately. Furthermore, the generation AI can generate images and voices based on prompts. Step 2: The conversation analysis unit uses the images and voices projected by the image and voice projection unit to converse with the elderly person and analyze the content of the conversation. For example, the generation AI converts the conversation with the elderly person into text and analyzes the content to understand their health condition, mood, etc. The generation AI can also use a pre-finished model to perform analysis based on prompts that do not include instructions. Furthermore, the generation AI can also evaluate their health condition and mood based on the content of the conversation. Step 3: The feedback unit provides feedback to family members about the elderly person's health condition, mood, etc. based on the conversation content analyzed by the conversation analysis unit. For example, the generation AI converts the analysis results into text and sends it to the family member via email or messenger app. The generation AI can also provide real-time feedback based on the analysis results. Furthermore, the generation AI can customize the feedback content and provide only the necessary information.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 picture and voice projection unit that projects human-like pictures and voices; a conversation analysis unit that uses the image and voice projected by the image and voice projection unit to converse with the elderly person and analyzes the content of the conversation; a feedback unit that provides feedback on the health condition and mood of the elderly person to a family member or the like based on the conversation content analyzed by the conversation analysis unit. A system characterized by:

2. The picture and voice projection unit is Using past photos and videos of the elderly person, a more personalized picture and voice are generated.

2. The system of claim 1.

3. The picture and voice projection unit is Learn the elderly person's tone of voice and speaking style and respond accordingly 2. The system of claim 1.

4. The picture and voice projection unit is Generate pictures and voices according to the emotional state of the elderly person 2. The system of claim 1.

5. The picture and voice projection unit is The elderly person's favorite music and scenery are projected into the background, providing a relaxing environment for conversation.

2. The system of claim 1.

6. The picture and voice projection unit is Generate images and voices that correspond to different cultures and languages 2. The system of claim 1.

7. The picture and voice projection unit is The system analyzes the emotions of the elderly person when they speak in real time and generates pictures and voices that evoke positive emotions.

2. The system of claim 1.

8. The conversation analysis unit By referencing the elderly person's past conversation history, a more accurate analysis can be performed.

2. The system of claim 1.

Images