system

Abstract

The system according to this embodiment aims to effectively support the mental health care of seniors. [Solution] The system according to this embodiment comprises an emotion recognition unit, a dialogue provision unit, an exercise suggestion unit, and a connection promotion unit. The emotion recognition unit recognizes the emotions of the senior. The dialogue provision unit provides personalized dialogue based on the emotions recognized by the emotion recognition unit. The exercise suggestion unit suggests relaxation exercises based on the dialogue provided by the dialogue provision unit. The connection promotion unit promotes social connections based on the exercises suggested by the exercise suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, means for effectively supporting senior mental health care are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively support senior mental health care.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an emotion recognition unit, a dialogue provision unit, an exercise suggestion unit, and a connection promotion unit. The emotion recognition unit recognizes the emotions of the senior. The dialogue provision unit provides personalized dialogue based on the emotions recognized by the emotion recognition unit. The exercise suggestion unit suggests relaxation exercises based on the dialogue provided by the dialogue provision unit. The connection promotion unit promotes social connections based on the exercises suggested by the exercise suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively support the mental health care of seniors. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) A virtual well-being companion (VWBC) according to an embodiment of the present invention is a platform that provides mental support to seniors. To enhance senior mental health care, the VWBC provides conversational AI, personalized exercises, relaxation content, and features that promote social connection. For example, the VWBC is an AI companion that seniors can easily talk to, providing support through daily emotional management and dialogue. The generative AI performs emotion recognition and personalized dialogue. Next, the VWBC suggests relaxation exercises, meditation, and mindfulness sessions. The generative AI uses a smart recommendation algorithm to suggest the most suitable mental health program for each day. Furthermore, the VWBC provides the ability to connect to online group chats and communities. The generative AI utilizes natural language processing to provide a simple interface that is easily accessible to seniors. Finally, the VWBC provides easy-to-follow operation guides and support for using digital devices. VWBC targets seniors aged 65 and over, particularly those living alone or struggling with limited social connections, addressing issues such as loneliness, social isolation, declining mental health, lack of communication with family and friends, and difficulty adapting to digital devices. This allows the Virtual Wellbeing Companion (VWBC) to provide emotional support and enhance mental health care for seniors.

[0029] The virtual well-being companion (VWBC) according to this embodiment comprises an emotion recognition unit, a dialogue provision unit, an exercise suggestion unit, and a connection promotion unit. The emotion recognition unit recognizes the emotions of seniors. The emotion recognition unit can, for example, analyze the tone of voice and speaking speed of seniors to detect subtle changes in emotion. The emotion recognition unit can also analyze the facial expressions of seniors in real time and instantly recognize changes in emotion. Furthermore, the emotion recognition unit can learn from the seniors' past emotional data and continuously improve its emotion recognition algorithm. The dialogue provision unit provides personalized dialogue based on the emotions recognized by the emotion recognition unit. For example, the dialogue provision unit can provide words of encouragement when seniors are sad. It can also provide words of empathy when seniors are happy. Furthermore, it can provide reassuring dialogue when seniors are anxious. The exercise suggestion unit suggests relaxation exercises based on the dialogue provided by the dialogue provision unit. For example, the exercise suggestion unit can suggest relaxation exercises when seniors are feeling stressed. Furthermore, the exercise suggestion unit can suggest active exercises when seniors are feeling energetic. Additionally, it can suggest mood-boosting exercises when seniors are feeling down. The connection promotion unit facilitates social connections based on the exercises suggested by the exercise suggestion unit. For example, when seniors are feeling lonely, the connection promotion unit can encourage participation in group chats. It can also suggest community events when seniors are feeling sociable. Furthermore, the connection promotion unit can provide individual support when seniors are feeling anxious. Thus, the virtual well-being companion (VWBC) according to this embodiment can provide personalized conversations and exercises based on seniors' emotions and promote social connections.

[0030] The emotion recognition unit recognizes the emotions of seniors. For example, it can analyze the tone of voice and speaking speed of seniors to detect subtle changes in emotion. Specifically, it uses voice analysis technology to extract parameters such as pitch, intensity, rhythm, and speed, and identifies changes in emotion based on these parameters. For example, if the tone of voice is low and the speaking speed is slow, it is determined that there is a high possibility that the senior is sad. The emotion recognition unit can also analyze the senior's facial expressions in real time and instantly recognize changes in emotion. Using facial expression recognition technology, it detects the movement and changes of various parts of the face to identify emotions such as smiles, anger, sadness, and surprise. For example, it analyzes the degree to which the eyebrows are raised and the movement of the corners of the mouth to determine the senior's emotional state. Furthermore, the emotion recognition unit can learn from the senior's past emotional data and continuously improve its emotion recognition algorithm. By accumulating past voice and facial expression data and using machine learning algorithms, it improves the accuracy of emotion recognition. As a result, the emotion recognition unit can recognize seniors' emotions with high accuracy and take appropriate action.

[0031] The dialogue provider unit provides personalized dialogues based on the emotions recognized by the emotion recognition unit. For example, when a senior is sad, the dialogue provider unit can offer words of encouragement. Specifically, it selects appropriate words and phrases according to the senior's emotional state and generates natural dialogue. For example, it might offer words of encouragement such as, "Today must have been tough. But tomorrow will surely be a better day." The dialogue provider unit can also offer words of empathy when a senior is happy. For example, it might offer words of empathy such as, "That's wonderful! I'm so happy for you." Furthermore, when a senior is anxious, the dialogue provider unit can offer reassuring dialogue. For example, it might offer reassuring words such as, "It's okay, I'm here, so don't worry." The dialogue provider unit can generate appropriate dialogues according to the senior's emotional state and provide emotional care for the senior. In addition, the dialogue provider unit can collect the senior's responses and feedback and continuously improve the content and method of the dialogue. As a result, the dialogue provider unit can provide personalized dialogues to seniors and support their mental health.

[0032] The Exercise Suggestion Department proposes relaxation exercises based on the dialogues provided by the Dialogue Provision Department. For example, when a senior is feeling stressed, the Exercise Suggestion Department can propose relaxation exercises. Specifically, it can propose relaxation exercises such as deep breathing, meditation, and light stretching to reduce the senior's stress. The Exercise Suggestion Department can also propose active exercises when the senior is feeling energetic. For example, it can propose active exercises such as light walking or simple gymnastics to maintain the senior's physical fitness. Furthermore, when a senior is feeling down, the Exercise Suggestion Department can propose exercises to lift their spirits. For example, it can propose dance exercises set to cheerful music or walks in nature to improve the senior's mood. The Exercise Suggestion Department can propose appropriate exercises according to the senior's emotional state and physical condition, supporting their mental and physical health. In addition, the Exercise Suggestion Department can collect data on the seniors' exercise participation and feedback, and continuously improve the content and methods of the exercises. As a result, the Exercise Suggestion Department can propose effective exercises to seniors, maintaining and improving their health.

[0033] The Connection Promotion Department promotes social connections based on exercises proposed by the Exercise Proposal Department. For example, when seniors feel lonely, the Connection Promotion Department can encourage participation in group chats. Specifically, it can propose group chats related to themes and hobbies that seniors are interested in, providing opportunities for seniors to interact with others. The Connection Promotion Department can also propose community events when seniors are in a sociable mood. For example, it can propose local events or online gatherings to encourage seniors to participate actively. Furthermore, the Connection Promotion Department can provide individual support when seniors feel anxious. For example, it can propose individual consultations with professional counselors or support staff, providing a safe environment for seniors to seek advice. The Connection Promotion Department can provide appropriate social connections according to seniors' emotional states and needs, thereby reducing their feelings of isolation. In addition, the Connection Promotion Department can collect senior participation data and feedback to continuously improve the content and methods of connections. In this way, the Connection Promotion Department can provide effective social connections to seniors and support their mental health.

[0034] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0035] The following briefly describes the processing flow for example form 1.

[0036] Step 1: The emotion recognition unit recognizes the senior's emotions. For example, the emotion recognition unit can analyze the senior's tone of voice and speaking speed to detect subtle changes in emotion. It can also analyze the senior's facial expressions in real time and instantly recognize changes in emotion. Furthermore, the emotion recognition unit can learn from the senior's past emotional data and continuously improve its emotion recognition algorithm. Step 2: The dialogue provider unit provides personalized dialogue based on the emotions recognized by the emotion recognition unit. For example, when a senior is sad, the dialogue provider unit can offer words of encouragement. It can also offer words of empathy when a senior is happy. Furthermore, when a senior is anxious, the dialogue provider unit can offer reassuring dialogue. Step 3: The exercise suggestion unit suggests relaxation exercises based on the dialogue provided by the dialogue provision unit. For example, the exercise suggestion unit can suggest relaxation exercises when a senior is feeling stressed. It can also suggest active exercises when a senior is feeling energetic. Furthermore, it can suggest mood-boosting exercises when a senior is feeling down. Step 4: The Connection Promotion Department promotes social connections based on exercises suggested by the Exercise Proposal Department. For example, when seniors are feeling lonely, the Connection Promotion Department can encourage them to join group chats. They can also suggest community events when seniors are in a sociable mood. Furthermore, they can provide individual support when seniors are feeling anxious.

[0037] (Example of form 2) A virtual well-being companion (VWBC) according to an embodiment of the present invention is a platform that provides mental support to seniors. To enhance senior mental health care, the VWBC provides conversational AI, personalized exercises, relaxation content, and features that promote social connection. For example, the VWBC is an AI companion that seniors can easily talk to, providing support through daily emotional management and dialogue. The generative AI performs emotion recognition and personalized dialogue. Next, the VWBC suggests relaxation exercises, meditation, and mindfulness sessions. The generative AI uses a smart recommendation algorithm to suggest the most suitable mental health program for each day. Furthermore, the VWBC provides the ability to connect to online group chats and communities. The generative AI utilizes natural language processing to provide a simple interface that is easily accessible to seniors. Finally, the VWBC provides easy-to-follow operation guides and support for using digital devices. VWBC targets seniors aged 65 and over, particularly those living alone or struggling with limited social connections, addressing issues such as loneliness, social isolation, declining mental health, lack of communication with family and friends, and difficulty adapting to digital devices. This allows the Virtual Wellbeing Companion (VWBC) to provide emotional support and enhance mental health care for seniors.

[0038] The virtual well-being companion (VWBC) according to this embodiment comprises an emotion recognition unit, a dialogue provision unit, an exercise suggestion unit, and a connection promotion unit. The emotion recognition unit recognizes the emotions of seniors. The emotion recognition unit can, for example, analyze the tone of voice and speaking speed of seniors to detect subtle changes in emotion. The emotion recognition unit can also analyze the facial expressions of seniors in real time and instantly recognize changes in emotion. Furthermore, the emotion recognition unit can learn from the seniors' past emotional data and continuously improve its emotion recognition algorithm. The dialogue provision unit provides personalized dialogue based on the emotions recognized by the emotion recognition unit. For example, the dialogue provision unit can provide words of encouragement when seniors are sad. It can also provide words of empathy when seniors are happy. Furthermore, it can provide reassuring dialogue when seniors are anxious. The exercise suggestion unit suggests relaxation exercises based on the dialogue provided by the dialogue provision unit. For example, the exercise suggestion unit can suggest relaxation exercises when seniors are feeling stressed. Furthermore, the exercise suggestion unit can suggest active exercises when seniors are feeling energetic. Additionally, it can suggest mood-boosting exercises when seniors are feeling down. The connection promotion unit facilitates social connections based on the exercises suggested by the exercise suggestion unit. For example, when seniors are feeling lonely, the connection promotion unit can encourage participation in group chats. It can also suggest community events when seniors are feeling sociable. Furthermore, the connection promotion unit can provide individual support when seniors are feeling anxious. Thus, the virtual well-being companion (VWBC) according to this embodiment can provide personalized conversations and exercises based on seniors' emotions and promote social connections.

[0039] The emotion recognition unit recognizes the emotions of seniors. For example, it can analyze the tone of voice and speaking speed of seniors to detect subtle changes in emotion. Specifically, it uses voice analysis technology to extract parameters such as pitch, intensity, rhythm, and speed, and identifies changes in emotion based on these parameters. For example, if the tone of voice is low and the speaking speed is slow, it is determined that there is a high possibility that the senior is sad. The emotion recognition unit can also analyze the senior's facial expressions in real time and instantly recognize changes in emotion. Using facial expression recognition technology, it detects the movement and changes of various parts of the face to identify emotions such as smiles, anger, sadness, and surprise. For example, it analyzes the degree to which the eyebrows are raised and the movement of the corners of the mouth to determine the senior's emotional state. Furthermore, the emotion recognition unit can learn from the senior's past emotional data and continuously improve its emotion recognition algorithm. By accumulating past voice and facial expression data and using machine learning algorithms, it improves the accuracy of emotion recognition. As a result, the emotion recognition unit can recognize seniors' emotions with high accuracy and take appropriate action.

[0040] The dialogue provider unit provides personalized dialogues based on the emotions recognized by the emotion recognition unit. For example, when a senior is sad, the dialogue provider unit can offer words of encouragement. Specifically, it selects appropriate words and phrases according to the senior's emotional state and generates natural dialogue. For example, it might offer words of encouragement such as, "Today must have been tough. But tomorrow will surely be a better day." The dialogue provider unit can also offer words of empathy when a senior is happy. For example, it might offer words of empathy such as, "That's wonderful! I'm so happy for you." Furthermore, when a senior is anxious, the dialogue provider unit can offer reassuring dialogue. For example, it might offer reassuring words such as, "It's okay, I'm here, so don't worry." The dialogue provider unit can generate appropriate dialogues according to the senior's emotional state and provide emotional care for the senior. In addition, the dialogue provider unit can collect the senior's responses and feedback and continuously improve the content and method of the dialogue. As a result, the dialogue provider unit can provide personalized dialogues to seniors and support their mental health.

[0041] The Exercise Suggestion Department proposes relaxation exercises based on the dialogues provided by the Dialogue Provision Department. For example, when a senior is feeling stressed, the Exercise Suggestion Department can propose relaxation exercises. Specifically, it can propose relaxation exercises such as deep breathing, meditation, and light stretching to reduce the senior's stress. The Exercise Suggestion Department can also propose active exercises when the senior is feeling energetic. For example, it can propose active exercises such as light walking or simple gymnastics to maintain the senior's physical fitness. Furthermore, when a senior is feeling down, the Exercise Suggestion Department can propose exercises to lift their spirits. For example, it can propose dance exercises set to cheerful music or walks in nature to improve the senior's mood. The Exercise Suggestion Department can propose appropriate exercises according to the senior's emotional state and physical condition, supporting their mental and physical health. In addition, the Exercise Suggestion Department can collect data on the seniors' exercise participation and feedback, and continuously improve the content and methods of the exercises. As a result, the Exercise Suggestion Department can propose effective exercises to seniors, maintaining and improving their health.

[0042] The Connection Promotion Department promotes social connections based on exercises proposed by the Exercise Proposal Department. For example, when seniors feel lonely, the Connection Promotion Department can encourage participation in group chats. Specifically, it can propose group chats related to themes and hobbies that seniors are interested in, providing opportunities for seniors to interact with others. The Connection Promotion Department can also propose community events when seniors are in a sociable mood. For example, it can propose local events or online gatherings to encourage seniors to participate actively. Furthermore, the Connection Promotion Department can provide individual support when seniors feel anxious. For example, it can propose individual consultations with professional counselors or support staff, providing a safe environment for seniors to seek advice. The Connection Promotion Department can provide appropriate social connections according to seniors' emotional states and needs, thereby reducing their feelings of isolation. In addition, the Connection Promotion Department can collect senior participation data and feedback to continuously improve the content and methods of connections. In this way, the Connection Promotion Department can provide effective social connections to seniors and support their mental health.

[0043] The emotion recognition unit can estimate the emotions of seniors and improve the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can analyze the tone of voice and speaking speed of seniors to detect subtle changes in emotion. The emotion recognition unit can also analyze the facial expressions of seniors in real time and instantly recognize changes in emotion. Furthermore, the emotion recognition unit can learn from the past emotional data of seniors and continuously improve the emotion recognition algorithm. This allows for improved accuracy of emotion recognition by estimating the emotions of seniors. Some or all of the above processing in the emotion recognition unit may be performed using generative AI, or it may be performed without generative AI. For example, the emotion recognition unit can input the tone of voice and speaking speed of seniors into the generative AI and have the generative AI perform the detection of subtle changes in emotion.

[0044] The emotion recognition unit can analyze a senior's past emotional data and optimize the emotion recognition algorithm. For example, the emotion recognition unit can analyze a senior's past conversation history and identify emotional patterns. It can also cluster a senior's past emotional data to understand emotional trends. Furthermore, the emotion recognition unit can adjust the parameters of the emotion recognition model using the senior's past emotional data. This allows the emotion recognition algorithm to be optimized by analyzing the senior's past emotional data. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input a senior's past emotional data into a generative AI and have the generative AI adjust the parameters of the emotion recognition model.

[0045] The emotion recognition unit can recognize emotions by analyzing the senior's voice tone and facial expressions during emotion recognition. For example, the emotion recognition unit can analyze changes in the senior's voice tone in real time and detect changes in emotion. The emotion recognition unit can also capture the senior's facial expressions with a camera and recognize changes in emotion. Furthermore, the emotion recognition unit can improve the accuracy of emotion recognition by combining the senior's voice tone and facial expressions. This allows for accurate emotion recognition by analyzing the senior's voice tone and facial expressions. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input data on the senior's voice tone and facial expressions into a generative AI and have the generative AI perform emotion recognition.

[0046] The emotion recognition unit can estimate the senior's emotions and adjust the timing of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can perform emotion recognition when the senior is relaxed and obtain accurate data. It can also perform emotion recognition when the senior is stressed and provide appropriate support. Furthermore, the emotion recognition unit can perform emotion recognition when there is a significant change in the senior's emotions and respond quickly. By adjusting the timing of emotion recognition based on the senior's emotions, accurate data can be obtained. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input the senior's emotional data into a generative AI and have the generative AI perform the adjustment of the timing of emotion recognition.

[0047] The emotion recognition unit can recognize emotions by considering the senior's living environment and daily activity patterns. For example, the emotion recognition unit can analyze the senior's living environment (residence, surrounding sounds, etc.) and reflect this in emotion recognition. The emotion recognition unit can also recognize emotions by considering the senior's daily activity patterns (wake-up time, meal times, etc.). Furthermore, the emotion recognition unit can improve the accuracy of emotion recognition by combining the senior's living environment and activity patterns. This means that the accuracy of emotion recognition can be improved by considering the senior's living environment and daily activity patterns. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input data on the senior's living environment and daily activity patterns into a generative AI and have the generative AI perform the task of improving the accuracy of emotion recognition.

[0048] The emotion recognition unit can recognize emotions by analyzing the senior's social media activity during emotion recognition. For example, the emotion recognition unit can analyze the content of the senior's social media posts to grasp emotional trends. It can also analyze the senior's comments and reactions on social media to recognize emotions. Furthermore, the emotion recognition unit can improve the accuracy of emotion recognition by combining the senior's social media activity with other emotional data. This allows for improved accuracy of emotion recognition by analyzing the senior's social media activity. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input the content of the senior's social media posts into a generative AI and have the generative AI perform emotion recognition.

[0049] The dialogue provider can estimate the senior's emotions and adjust the content of the dialogue based on those estimated emotions. For example, if the senior is sad, the dialogue provider can offer words of encouragement. If the senior is happy, the dialogue provider can offer words of empathy. Furthermore, if the senior is anxious, the dialogue provider can offer reassuring dialogue. By adjusting the content of the dialogue based on the senior's emotions, a more appropriate dialogue can be provided. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's emotional data into a generative AI and have the generative AI perform the adjustment of the dialogue content.

[0050] The dialogue provider unit can select the most appropriate dialogue content by referring to the senior's past dialogue history when providing dialogue. For example, the dialogue provider unit can select a favorable topic from the senior's past dialogue history. The dialogue provider unit can also analyze the senior's past dialogue history and identify topics to avoid. Furthermore, the dialogue provider unit can smooth the flow of the dialogue based on the senior's past dialogue history. In this way, the optimal dialogue content can be selected by referring to the senior's past dialogue history. Some or all of the above processing in the dialogue provider unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider unit can input the senior's past dialogue history into a generative AI and have the generative AI select the optimal dialogue content.

[0051] The dialogue provider can customize the dialogue topics based on the senior's interests and preferences when providing dialogue. For example, the dialogue provider can select dialogue topics based on the senior's hobbies and interests. It can also customize the dialogue content based on topics the senior has recently become interested in. Furthermore, the dialogue provider can adjust the depth and level of detail of the dialogue according to the senior's interests and preferences. This allows for more interesting dialogues to be provided by customizing the dialogue topics based on the senior's interests and preferences. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input data on the senior's interests and preferences into a generative AI and have the generative AI perform the customization of dialogue topics.

[0052] The dialogue provider can estimate the senior's emotions and adjust the length of the dialogue based on the estimated emotions. For example, the dialogue provider can provide a short dialogue when the senior is tired. It can also provide a longer dialogue when the senior is relaxed. Furthermore, it can provide a concise dialogue when the senior is in a hurry. By adjusting the length of the dialogue based on the senior's emotions, a more appropriate dialogue can be provided. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's emotion data into a generative AI and have the generative AI adjust the length of the dialogue.

[0053] The dialogue provider can determine the timing of a dialogue based on the senior's daily rhythm. For example, the dialogue provider can provide a morning greeting to coincide with the senior's wake-up time. It can also provide dialogue related to meals to coincide with the senior's meal times. Furthermore, it can provide relaxing dialogue before the senior goes to bed. By determining the timing of a dialogue based on the senior's daily rhythm, it is possible to provide dialogue at a more appropriate time. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's daily rhythm data into a generative AI and have the generative AI determine the timing of the dialogue.

[0054] The dialogue provider unit can adjust the dialogue content by referring to the senior's communication history with family and friends when providing dialogue. For example, the dialogue provider unit can adjust the dialogue content based on the senior's recent conversations with family. It can also refer to the senior's communication history with friends to provide common topics. Furthermore, the dialogue provider unit can adjust the tone of the dialogue considering the senior's relationship with family and friends. This allows for the provision of more appropriate dialogue content by referring to the senior's communication history with family and friends. Some or all of the above processing in the dialogue provider unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider unit can input the senior's communication history with family and friends into a generative AI and have the generative AI perform the adjustment of the dialogue content.

[0055] The exercise suggestion unit can estimate the senior's emotions and adjust the type of exercise based on those emotions. For example, if the senior is feeling stressed, the exercise suggestion unit can suggest relaxation exercises. If the senior is feeling energetic, the exercise suggestion unit can also suggest active exercises. Furthermore, if the senior is feeling depressed, the exercise suggestion unit can suggest mood-boosting exercises. By adjusting the type of exercise based on the senior's emotions, it is possible to provide more appropriate exercises. Some or all of the above processing in the exercise suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise suggestion unit can input the senior's emotional data into a generative AI and have the generative AI perform the adjustment of the type of exercise.

[0056] The exercise suggestion unit can select the most suitable exercise by referring to the senior's past exercise history when suggesting exercises. For example, the exercise suggestion unit can select preferred exercises from the senior's past exercise history. The exercise suggestion unit can also analyze the senior's past exercise history and identify exercises to avoid. Furthermore, the exercise suggestion unit can adjust the frequency and intensity of exercises based on the senior's past exercise history. This allows the system to select the most suitable exercise by referring to the senior's past exercise history. Some or all of the above processing in the exercise suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise suggestion unit can input the senior's past exercise history into a generative AI and have the generative AI select the most suitable exercise.

[0057] The exercise suggestion unit can customize exercises based on the senior's health condition and fitness level when suggesting exercises. For example, the exercise suggestion unit can suggest exercises that are not strenuous, taking into account the senior's health condition. It can also adjust the intensity of the exercises according to the senior's fitness level. Furthermore, the exercise suggestion unit can select the type of exercise based on the senior's health condition and fitness level. In this way, by customizing exercises based on the senior's health condition and fitness level, it can provide exercises that are not strenuous. Some or all of the above processing in the exercise suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise suggestion unit can input data on the senior's health condition and fitness level into a generation AI and have the generation AI perform the exercise customization.

[0058] The exercise suggestion unit can estimate the senior's emotions and adjust the exercise frequency based on those emotions. For example, when a senior is feeling stressed, the exercise suggestion unit can frequently suggest relaxation exercises. When a senior is feeling energetic, the exercise suggestion unit can also suggest active exercises at a moderate frequency. Furthermore, when a senior is feeling depressed, the exercise suggestion unit can regularly suggest mood-boosting exercises. By adjusting the exercise frequency based on the senior's emotions, more appropriate exercises can be provided. Some or all of the above processing in the exercise suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise suggestion unit can input senior's emotional data into a generative AI and have the generative AI adjust the exercise frequency.

[0059] The exercise suggestion unit can suggest exercises while considering the senior's living environment and activity patterns. For example, the exercise suggestion unit can suggest appropriate exercises by considering the senior's living environment (residence, surrounding noise, etc.). It can also suggest exercises according to the senior's daily activity patterns (wake-up time, meal times, etc.). Furthermore, the exercise suggestion unit can combine the senior's living environment and activity patterns to suggest the optimal exercise. This allows for the provision of more appropriate exercises by considering the senior's living environment and activity patterns. Some or all of the above processing in the exercise suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise suggestion unit can input data on the senior's living environment and activity patterns into a generation AI and have the generation AI execute exercise suggestions.

[0060] The exercise suggestion unit can analyze seniors' social media activity and suggest exercises when proposing exercises. For example, the exercise suggestion unit can analyze the content of seniors' social media posts and suggest appropriate exercises. It can also analyze seniors' comments and reactions on social media and suggest exercises. Furthermore, the exercise suggestion unit can combine seniors' social media activity with other data to suggest the most suitable exercises. This allows for the provision of more appropriate exercises by analyzing seniors' social media activity. Some or all of the above processing in the exercise suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise suggestion unit can input the content of seniors' social media posts into a generative AI and have the generative AI execute exercise suggestions.

[0061] The connection promotion unit can estimate the emotions of seniors and adjust connection promotion methods based on the estimated emotions. For example, when a senior is feeling lonely, the connection promotion unit can encourage participation in a group chat. It can also suggest community events when a senior is in a sociable mood. Furthermore, it can provide individual support when a senior is feeling anxious. By adjusting connection promotion methods based on seniors' emotions, more appropriate connection promotion can be provided. Some or all of the above processing in the connection promotion unit may be performed using a generative AI, or not. For example, the connection promotion unit can input senior emotion data into a generative AI and have the generative AI perform the adjustment of connection promotion methods.

[0062] The connection promotion unit can select the optimal connection promotion method by referring to the senior's past social activity history when promoting connections. For example, the connection promotion unit can select desirable activities from the senior's past social activity history. The connection promotion unit can also analyze the senior's past social activity history and identify activities to be avoided. Furthermore, the connection promotion unit can adjust the frequency and method of connection promotion based on the senior's past social activity history. In this way, the optimal connection promotion method can be selected by referring to the senior's past social activity history. Some or all of the above processing in the connection promotion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the connection promotion unit can input the senior's past social activity history into a generative AI and have the generative AI select the optimal connection promotion method.

[0063] The connection promotion unit can customize the means of promoting connections based on the senior's interests and preferences. For example, the connection promotion unit can select means of promoting connections based on the senior's hobbies and interests. It can also customize means of promoting connections based on topics the senior has recently become interested in. Furthermore, the connection promotion unit can adjust the depth and level of detail of the connection promotion according to the senior's interests and preferences. By customizing the means of promoting connections based on the senior's interests and preferences, it is possible to provide more appropriate connection promotion. Some or all of the above processing in the connection promotion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the connection promotion unit can input senior interest data into a generative AI and have the generative AI perform the customization of the means of promoting connections.

[0064] The connection promotion unit can estimate the emotions of seniors and determine the priority of connection promotion based on the estimated emotions. For example, when a senior is feeling lonely, the connection promotion unit can prioritize connection promotion. It can also balance other activities when a senior is in a sociable mood. Furthermore, when a senior is feeling anxious, the connection promotion unit can prioritize connection promotion and provide a sense of security. In this way, by determining the priority of connection promotion based on the senior's emotions, more appropriate connection promotion can be provided. Some or all of the above processing in the connection promotion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the connection promotion unit can input senior emotion data into a generative AI and have the generative AI perform the determination of the priority of connection promotion.

[0065] The Connection Promotion Unit can select the most suitable connection promotion method when promoting connections, taking into account the senior's geographical location information. For example, the Connection Promotion Unit can suggest community events in the senior's area of ​​residence. It can also introduce nearby support groups based on the senior's geographical location information. Furthermore, the Connection Promotion Unit can propose a balanced combination of online and offline connection promotion methods, taking the senior's geographical location information into consideration. This allows for the provision of more appropriate connection promotion methods by considering the senior's geographical location information. Some or all of the above processing in the Connection Promotion Unit may be performed using or without a generative AI. For example, the Connection Promotion Unit can input the senior's geographical location information into a generative AI and have the generative AI select the most suitable connection promotion method.

[0066] The Connection Promotion Unit can analyze seniors' social media activities and propose means of promoting connections during the connection promotion process. For example, the Connection Promotion Unit can analyze the content of seniors' social media posts and propose appropriate means of promoting connections. It can also analyze seniors' comments and reactions on social media and propose means of promoting connections. Furthermore, the Connection Promotion Unit can combine seniors' social media activities with other data to propose the most suitable means of promoting connections. This allows for the provision of more appropriate means of promoting connections by analyzing seniors' social media activities. Some or all of the above processing in the Connection Promotion Unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the Connection Promotion Unit can input the content of seniors' social media posts into a generative AI and have the generative AI propose means of promoting connections.

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

[0068] The emotion recognition unit can estimate the emotions of seniors and improve the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can analyze the tone of voice and speaking speed of seniors to detect subtle changes in emotion. It can also analyze the facial expressions of seniors in real time and instantly recognize changes in emotion. Furthermore, the emotion recognition unit can learn from the past emotional data of seniors and continuously improve its emotion recognition algorithm. This allows it to improve the accuracy of emotion recognition by estimating the emotions of seniors. Some or all of the above processing in the emotion recognition unit may be performed using generative AI, or it may be performed without using generative AI. For example, the emotion recognition unit can input the tone of voice and speaking speed of seniors into the generative AI and have the generative AI perform the detection of subtle changes in emotion.

[0069] The emotion recognition unit can analyze a senior's past emotional data and optimize the emotion recognition algorithm. For example, the emotion recognition unit can analyze a senior's past conversation history and identify emotional patterns. It can also cluster a senior's past emotional data to understand emotional trends. Furthermore, the emotion recognition unit can adjust the parameters of the emotion recognition model using the senior's past emotional data. This allows for the optimization of the emotion recognition algorithm by analyzing the senior's past emotional data. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input a senior's past emotional data into a generative AI and have the generative AI adjust the parameters of the emotion recognition model.

[0070] The emotion recognition unit can recognize emotions by analyzing the tone of voice and facial expressions of seniors during emotion recognition. For example, the emotion recognition unit can analyze changes in the tone of voice of seniors in real time and detect changes in emotion. The emotion recognition unit can also capture the senior's facial expressions with a camera and recognize changes in emotion. Furthermore, the emotion recognition unit can improve the accuracy of emotion recognition by combining the tone of voice and facial expressions of seniors. As a result, emotions can be accurately recognized by analyzing the tone of voice and facial expressions of seniors. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input data on the tone of voice and facial expressions of seniors into a generative AI and have the generative AI perform emotion recognition.

[0071] The emotion recognition unit can estimate the senior's emotions and adjust the timing of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can perform emotion recognition when the senior is relaxed and obtain accurate data. It can also perform emotion recognition when the senior is stressed and provide appropriate support. Furthermore, the emotion recognition unit can perform emotion recognition when there is a significant change in the senior's emotions and respond quickly. By adjusting the timing of emotion recognition based on the senior's emotions, accurate data can be obtained. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input the senior's emotional data into a generative AI and have the generative AI perform the adjustment of the timing of emotion recognition.

[0072] The emotion recognition unit can recognize emotions by considering the senior's living environment and daily activity patterns. For example, the emotion recognition unit can analyze the senior's living environment (residence, surrounding sounds, etc.) and reflect this in emotion recognition. It can also recognize emotions by considering the senior's daily activity patterns (wake-up time, meal times, etc.). Furthermore, the emotion recognition unit can improve the accuracy of emotion recognition by combining the senior's living environment and activity patterns. This allows for improved accuracy of emotion recognition by considering the senior's living environment and daily activity patterns. Some or all of the above processing in the emotion recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion recognition unit can input data on the senior's living environment and daily activity patterns into a generative AI and have the generative AI perform the task of improving the accuracy of emotion recognition.

[0073] The dialogue provider can estimate the senior's emotions and adjust the content of the dialogue based on those emotions. For example, when the senior is sad, the dialogue provider can offer words of encouragement. When the senior is happy, the dialogue provider can offer words of empathy. Furthermore, when the senior is anxious, the dialogue provider can offer reassuring dialogue. By adjusting the content of the dialogue based on the senior's emotions, a more appropriate dialogue can be provided. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's emotional data into a generative AI and have the generative AI adjust the content of the dialogue.

[0074] The dialogue provider can select the most appropriate dialogue content by referring to the senior's past dialogue history when providing dialogue. For example, the dialogue provider can select a favorable topic from the senior's past dialogue history. The dialogue provider can also analyze the senior's past dialogue history and identify topics to avoid. Furthermore, the dialogue provider can smooth the flow of the dialogue based on the senior's past dialogue history. In this way, the optimal dialogue content can be selected by referring to the senior's past dialogue history. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's past dialogue history into a generative AI and have the generative AI select the optimal dialogue content.

[0075] The dialogue provider can customize the conversation topics based on the senior's interests and preferences when providing dialogue. For example, the dialogue provider can select conversation topics based on the senior's hobbies and interests. It can also customize the conversation content based on topics the senior has recently become interested in. Furthermore, the dialogue provider can adjust the depth and level of detail of the conversation according to the senior's interests and preferences. This allows for more interesting conversations by customizing the conversation topics based on the senior's interests and preferences. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input senior interest data into a generative AI and have the generative AI perform the customization of the conversation topics.

[0076] The dialogue provider can estimate the senior's emotions and adjust the length of the dialogue based on the estimated emotions. For example, the dialogue provider can provide a short dialogue when the senior is tired. It can also provide a longer dialogue when the senior is relaxed. Furthermore, it can provide a concise dialogue when the senior is in a hurry. By adjusting the length of the dialogue based on the senior's emotions, a more appropriate dialogue can be provided. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's emotion data into a generative AI and have the generative AI adjust the length of the dialogue.

[0077] The dialogue provider can determine the timing of a dialogue based on the senior's daily rhythm. For example, the dialogue provider can provide a morning greeting to coincide with the senior's wake-up time. It can also provide dialogue related to meals to coincide with the senior's meal times. Furthermore, it can provide relaxing dialogue before the senior goes to bed. By determining the timing of a dialogue based on the senior's daily rhythm, it is possible to provide dialogue at a more appropriate time. Some or all of the above processing in the dialogue provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue provider can input the senior's daily rhythm data into a generative AI and have the generative AI determine the timing of the dialogue.

[0078] The following briefly describes the processing flow for example form 2.

[0079] Step 1: The emotion recognition unit recognizes the senior's emotions. For example, the emotion recognition unit can analyze the senior's tone of voice and speaking speed to detect subtle changes in emotion. It can also analyze the senior's facial expressions in real time and instantly recognize changes in emotion. Furthermore, the emotion recognition unit can learn from the senior's past emotional data and continuously improve its emotion recognition algorithm. Step 2: The dialogue provider unit provides personalized dialogue based on the emotions recognized by the emotion recognition unit. For example, when a senior is sad, the dialogue provider unit can offer words of encouragement. It can also offer words of empathy when a senior is happy. Furthermore, when a senior is anxious, the dialogue provider unit can offer reassuring dialogue. Step 3: The exercise suggestion unit suggests relaxation exercises based on the dialogue provided by the dialogue provision unit. For example, the exercise suggestion unit can suggest relaxation exercises when a senior is feeling stressed. It can also suggest active exercises when a senior is feeling energetic. Furthermore, it can suggest mood-boosting exercises when a senior is feeling down. Step 4: The Connection Promotion Department promotes social connections based on exercises suggested by the Exercise Proposal Department. For example, when seniors are feeling lonely, the Connection Promotion Department can encourage them to join group chats. They can also suggest community events when seniors are in a sociable mood. Furthermore, they can provide individual support when seniors are feeling anxious.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0082] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] Each of the multiple elements described above, including the emotion recognition unit, dialogue provision unit, exercise suggestion unit, and connection promotion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 38B of the smart device 14 to detect the senior's facial expressions and voice tone, and the control unit 46A recognizes the emotion. The dialogue provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates a dialogue based on the senior's emotion using the emotion identification model 59. The exercise suggestion unit is implemented in the control unit 46A of the smart device 14, for example, and suggests relaxation exercises according to the senior's condition. The connection promotion unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and suggests group chats and community events that the senior can participate in. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0086] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0090] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0091] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0092] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0093] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0094] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements described above, including the emotion recognition unit, dialogue provision unit, exercise suggestion unit, and connection promotion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect the senior's facial expressions and voice tone, and the control unit 46A recognizes the emotion. The dialogue provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates a dialogue based on the senior's emotion using the emotion identification model 59. The exercise suggestion unit is implemented in the control unit 46A of the smart glasses 214, for example, and suggests relaxation exercises according to the senior's condition. The connection promotion unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and suggests group chats and community events that the senior can participate in. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0100] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0101] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the emotion recognition unit, dialogue provision unit, exercise suggestion unit, and connection promotion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect the senior's facial expressions and voice tone, and the control unit 46A recognizes the emotion. The dialogue provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates dialogue based on the senior's emotion using the emotion identification model 59. The exercise suggestion unit is implemented in the control unit 46A of the headset terminal 314, for example, and suggests relaxation exercises according to the senior's condition. The connection promotion unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and suggests group chats and community events that the senior can participate in. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0117] As shown in Figure 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0124] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0127] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the emotion recognition unit, dialogue provision unit, exercise suggestion unit, and connection promotion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 238 of the robot 414 to detect the senior's facial expressions and tone of voice, and the control unit 46A recognizes the emotion. The dialogue provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates a dialogue based on the senior's emotion using the emotion identification model 59. The exercise suggestion unit is implemented in the control unit 46A of the robot 414, for example, and suggests relaxation exercises according to the senior's condition. The connection promotion unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and suggests group chats and community events that the senior can participate in. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0135] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0136] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0137] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0141] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0142] 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.

[0143] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0144] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0145] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0146] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0148] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0149] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0150] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0151] (Note 1) An emotion recognition unit that recognizes the emotions of seniors, A dialogue provider unit that provides personalized dialogue based on the emotions recognized by the emotion recognition unit, An exercise suggestion unit proposes relaxation exercises based on the dialogue provided by the aforementioned dialogue provision unit, The system comprises a connection promotion unit that promotes social connections based on exercises proposed by the exercise proposal unit. A system characterized by the following features. (Note 2) The emotion recognition unit, This system estimates the emotions of seniors and improves the accuracy of emotion recognition based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The emotion recognition unit, Analyze senior citizens' past emotional data to optimize their emotion recognition algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The emotion recognition unit, During emotion recognition, the system analyzes the senior's voice tone and facial expressions to recognize their emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The emotion recognition unit, It estimates the emotions of seniors and adjusts the timing of emotion recognition based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The emotion recognition unit, When recognizing emotions, the senior's living environment and daily activity patterns are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 7) The emotion recognition unit, When recognizing emotions, the social media activity of seniors is analyzed to recognize their emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dialogue provider unit, It estimates the emotions of seniors and adjusts the content of the conversation based on the estimated emotions of the seniors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned dialogue provider unit, When providing dialogue options, the system selects the most suitable dialogue content by referring to the senior's past dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned dialogue provider unit, When providing dialogue, customize the topics of the dialogue based on the interests and concerns of the senior participants. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned dialogue provider unit, It estimates the senior's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned dialogue provider unit, When providing dialogue, the timing of the dialogue is determined based on the senior's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned dialogue provider unit, When providing dialogue, we adjust the content of the dialogue by referring to the communication history of the senior's family and friends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned exercise suggestion unit, The system estimates the emotions of seniors and adjusts the type of exercise based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned exercise suggestion unit, When suggesting exercises, the system selects the most suitable exercises by referring to the senior's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned exercise suggestion unit, When suggesting exercises, customize them based on the senior's health condition and fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned exercise suggestion unit, The system estimates the emotions of seniors and adjusts the frequency of exercise based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned exercise suggestion unit, When suggesting exercises, we take into account the lifestyle and activity patterns of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned exercise suggestion unit, When suggesting exercises, we analyze the social media activity of seniors to propose suitable exercises. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned connection promotion unit is We estimate the emotions of seniors and adjust connection-promoting methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned connection promotion unit is When promoting connections, the optimal method for promoting connections is selected by referring to the senior's past social activity history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned connection promotion unit is When promoting connections, customize the methods of connection based on the interests and concerns of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned connection promotion unit is The system estimates the emotions of seniors and determines priority for promoting connection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned connection promotion unit is When promoting connections, the optimal method for promoting connections will be selected by considering the geographical location information of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned connection promotion unit is When promoting connections, we analyze seniors' social media activity and propose ways to facilitate connections. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An emotion recognition unit that recognizes the emotions of seniors, A dialogue provider unit that provides personalized dialogue based on the emotions recognized by the emotion recognition unit, An exercise suggestion unit proposes relaxation exercises based on the dialogue provided by the aforementioned dialogue provision unit, The system comprises a connection promotion unit that promotes social connections based on exercises proposed by the exercise proposal unit. A system characterized by the following features.

2. The emotion recognition unit, This system estimates the emotions of seniors and improves the accuracy of emotion recognition based on the estimated emotions of seniors. The system according to feature 1.

3. The emotion recognition unit, Analyze senior citizens' past emotional data to optimize their emotion recognition algorithms. The system according to feature 1.

4. The emotion recognition unit, During emotion recognition, the system analyzes the senior's voice tone and facial expressions to recognize their emotions. The system according to feature 1.

5. The emotion recognition unit, It estimates the emotions of seniors and adjusts the timing of emotion recognition based on the estimated emotions of seniors. The system according to feature 1.

6. The emotion recognition unit, When recognizing emotions, the senior's living environment and daily activity patterns are taken into consideration. The system according to feature 1.

7. The emotion recognition unit, When recognizing emotions, the social media activity of seniors is analyzed to recognize their emotions. The system according to feature 1.

8. The aforementioned dialogue provider unit, It estimates the emotions of seniors and adjusts the content of the conversation based on the estimated emotions of the seniors. The system according to feature 1.

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