System

Abstract

The system according to the embodiment aims to allow the elderly person to maintain mental and physical health and reduce loneliness through a pseudo family.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a display unit, an observation unit, and a notification unit. The reception unit receives an input of a natural language. The generation unit generates a conversation based on the information received by the reception unit. The display unit displays the conversation generated by the generation unit. The observation unit observes the health condition of the user through the conversation displayed by the display unit. The notification unit notifies the abnormality of the health condition observed by the observation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for elderly people living alone to live without feeling lonely, making it difficult to maintain their mental and physical health.

[0005] The system according to the embodiment aims to help elderly people maintain their mental and physical health through pseudo-family relationships and reduce their sense of loneliness. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a display unit, an observation unit, and a notification unit. The reception unit receives input in natural language. The generation unit generates a conversation based on information received by the reception unit. The display unit displays the conversation generated by the generation unit. The observation unit observes the user's health condition through the conversation displayed by the display unit. The notification unit notifies the user of any abnormalities in the health condition observed by the observation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to maintain their mental and physical health through a pseudo-family and reduce feelings of loneliness. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The simulated family generation system according to an embodiment of the present invention uses a generation AI to generate a simulated family so that elderly people can live independently without feeling lonely. This system aims to help elderly people maintain their mental and physical health through the simulated family and lead a healthy life. The simulated family generation system accepts natural language input, and the generation AI analyzes the commands to generate conversations for the simulated family. The generated conversations are displayed on a mannequin-shaped monitor, and the conversations take place. The content of the conversations covers a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI follows up with, "What was the hot spring like?" This natural conversation reduces the elderly's sense of loneliness. Furthermore, the generation AI provides information and reminders necessary for daily life. For example, it reminds the elderly, "You have a doctor's appointment at 10 o'clock today," to help them remember. The generation AI also monitors the elderly's health status from conversations and video, and notifies relatives if any abnormalities are detected. For example, if an elderly person says, "I haven't had much of an appetite lately," the AI ​​will notify their next of kin of this information, allowing for early intervention. This allows the pseudo-family generation system to provide emotional support to elderly people through their pseudo-family members, helping them live without feeling lonely. Health monitoring and notification functions also allow for early detection of abnormalities and appropriate responses. This allows the pseudo-family generation system to maintain the mental and physical health of elderly people, helping them live healthy lives. For example, this system allows elderly people to live alone without feeling lonely, providing emotional support. Health monitoring and notification functions also allow for early detection of abnormalities and appropriate responses.

[0029] A simulated family creation system according to an embodiment includes a reception unit, a generation unit, a display unit, an observation unit, and a notification unit. The reception unit receives input in natural language. Examples of natural languages ​​include, but are not limited to, Japanese, English, and other languages. The reception unit can receive, for example, voice input or text input. The reception unit can also estimate a user's emotions and adjust the natural language input method based on the estimated user emotions. For example, if a user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. The generation unit uses a generation AI to generate conversations based on the information received by the reception unit. Examples of the generation AI include, but are not limited to, GPT-4 (registered trademark) and Gemini. The generation unit generates conversations covering a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI can follow up with, "What was the hot spring like?" The display unit displays the conversation generated by the generation unit. The display unit displays the conversation generated on, for example, a mannequin-type monitor. The mannequin-type monitor may include, for example, a size, a display method, and interactive functions, but is not limited to these examples. The observation unit observes the user's health condition through the conversation displayed by the display unit. The health condition may include, for example, a heart rate, blood pressure, and facial expression analysis, but is not limited to these examples. The observation unit observes the user's health condition from the conversation and video and detects abnormalities early. The notification unit notifies the user of any abnormalities in the health condition observed by the observation unit. The notification unit notifies, for example, close relatives of the abnormal health condition. Close relatives include, for example, family members and relatives, but are not limited to these examples. As a result, the simulated family generation system according to the embodiment allows elderly people to receive psychological support through the simulated family, reduce feelings of loneliness, and detect abnormalities in their health early.

[0030] The generation unit can generate conversations using a generation AI. The generation unit generates conversations using a generation AI. Examples of generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit uses the generation AI to generate conversations covering a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI follows up with, "What was the hot spring like?" In this way, the generation AI can generate natural conversations and reduce the elderly person's sense of loneliness.

[0031] The generation unit can use a generation AI to generate conversations that cover multiple topics, such as hobbies, travel, and food, as well as health care and neighborhood gossip. The generation unit uses the generation AI to generate conversations that cover multiple topics, such as health care and neighborhood gossip, from topics such as hobbies, travel, and food. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit uses the generation AI to generate conversations that cover various topics, such as health care and neighborhood gossip, from topics such as hobbies, travel, and food. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI follows up with, "What was the hot spring like?" This allows for a variety of topics to be covered, enabling natural conversations with the elderly and reducing feelings of loneliness.

[0032] The display unit can display the generated conversation on a mannequin-type monitor. The display unit displays the conversation generated by the generation unit on the mannequin-type monitor. The mannequin-type monitor may have, for example, a size, a display method, an interactive function, etc., but is not limited to these examples. By displaying the generated conversation on the mannequin-type monitor, the display unit realizes a visually realistic conversation with the virtual family. In this way, by using the mannequin-type monitor, a visually realistic conversation with the virtual family is realized.

[0033] The observation unit can observe the user's health condition from conversation or video. The observation unit observes the user's health condition from conversation or video. Health conditions include, but are not limited to, heart rate, blood pressure, and facial expression analysis. The observation unit observes the user's health condition from conversation or video and detects abnormalities early. For example, the observation unit observes the health condition based on the content of the conversation or changes in facial expressions. The observation unit can also monitor the user's health condition in real time and detect abnormalities. This allows the health condition of elderly people to be observed through conversation or video and abnormalities to be detected early.

[0034] The notification unit can notify close relatives of any abnormalities in the health condition. The notification unit notifies close relatives of any abnormalities in the health condition observed by the observation unit. Close relatives include, but are not limited to, family members and relatives. The notification unit notifies close relatives of any abnormalities in the health condition, thereby enabling a prompt response. For example, if an abnormality in the user's health condition is detected, the notification unit notifies close relatives by email or telephone. The notification unit can also select an appropriate notification method depending on the content and urgency of the abnormality. This allows close relatives to be notified of any abnormalities in the elderly person's health condition early, enabling a prompt response.

[0035] The generation unit can generate conversations that convey information necessary for daily life and provide reminders. The generation unit uses a generation AI to generate conversations that convey information necessary for daily life and provide reminders. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit uses a generation AI to generate conversations that convey information necessary for daily life and provide reminders. For example, the generation AI can provide a reminder such as, "You have a hospital appointment at 10 o'clock today," to help the elderly person not forget. The generation unit can also use a generation AI to generate conversations that convey information necessary for daily life, such as when to take medicine or what to eat. This allows elderly people to receive information necessary for daily life and receive reminders, allowing them to live their daily lives smoothly.

[0036] The reception unit can analyze the elderly person's past conversation history and select the optimal input method. The reception unit analyzes the elderly person's past conversation history and selects the optimal input method. Input methods include, but are not limited to, voice input and text input, for example. The reception unit analyzes the elderly person's past conversation history and selects the optimal input method. For example, phrases frequently used by the elderly person in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the elderly person has used in the past. Furthermore, the reception unit can predict and suggest phrases that will be used during specific time periods based on the elderly person's past conversation history. This improves convenience for the elderly by providing the optimal input method based on the elderly person's past conversation history.

[0037] The reception unit can filter natural language input based on the elderly person's current health condition and mood. The reception unit filters natural language input based on the elderly person's current health condition and mood. Examples of filtering include, but are not limited to, excluding specific keywords and prioritizing the display of specific topics. The reception unit filters natural language input based on the elderly person's current health condition and mood. For example, if the elderly person is tired, the reception unit prioritizes simple questions and short conversations. Also, if the elderly person is in good health, the reception unit can accept detailed conversations and complex questions. Furthermore, if the elderly person is not feeling well, the reception unit can avoid health-related questions and prioritize relaxing topics. This allows for more appropriate conversations by providing input content that is appropriate for the elderly person's health condition and mood.

[0038] The reception unit can select the optimal input means according to the elderly person's input method when inputting natural language. The reception unit selects the optimal input means according to the elderly person's input method when inputting natural language. Input methods include, but are not limited to, for example, voice, text, and gesture. The reception unit selects the optimal input means according to the elderly person's input method when inputting natural language. For example, if the elderly person prefers voice input, voice input can be provided preferentially. Also, if the elderly person prefers text input, keyboard input can be provided preferentially. Furthermore, if the elderly person prefers gesture input, gesture input can be provided preferentially. In this way, a more comfortable interface can be realized by providing input means according to the elderly person's preferences.

[0039] The reception unit can preferentially accept highly relevant inputs in consideration of the geographical location information of the elderly person when inputting natural language. The reception unit preferentially accepts highly relevant inputs in consideration of the geographical location information of the elderly person when inputting natural language. Geographical location information includes, but is not limited to, examples of GPS data and address information. The reception unit preferentially accepts highly relevant inputs in consideration of the geographical location information of the elderly person when inputting natural language. For example, if the elderly person is at home, the reception unit preferentially accepts topics about the area around the elderly person's home. Also, if the elderly person is out, the reception unit can preferentially accept topics related to the destination. Furthermore, if the elderly person is traveling, the reception unit can preferentially accept topics related to the travel destination. This allows for more appropriate conversations by providing input content based on the geographical location information of the elderly person.

[0040] The reception unit can analyze the elderly person's social media activity and accept related input when natural language is input. The reception unit analyzes the elderly person's social media activity and accepts related input when natural language is input. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The reception unit analyzes the elderly person's social media activity and accepts related input when natural language is input. For example, the reception unit may accept related topics based on content shared by the elderly person on social media. Related topics may also be accepted based on the activities of the elderly person's friends on social media. Furthermore, the reception unit may analyze the content posted by the elderly person on social media and accept related topics. This allows for more appropriate conversations by providing input based on the elderly person's social media activity.

[0041] The reception unit can customize the input method by reflecting the elderly person's past feedback when inputting natural language. The reception unit customizes the input method by reflecting the elderly person's past feedback when inputting natural language. Feedback includes, for example, user ratings, comments, etc., but is not limited to these examples. The reception unit customizes the input method by reflecting the elderly person's past feedback when inputting natural language. For example, the reception unit can preferentially provide input methods that the elderly person has previously preferred. It can also eliminate input methods that the elderly person has previously avoided. Furthermore, it can suggest an optimal input method based on the elderly person's past feedback. In this way, a more comfortable interface can be realized by providing an input method based on the elderly person's past feedback.

[0042] The generation unit can adjust the level of detail of the conversation based on the importance of the topic when generating the conversation. The generation unit uses the generation AI to adjust the level of detail of the conversation based on the importance of the topic when generating the conversation. The importance of the topic includes, but is not limited to, for example, the user's level of interest and urgency. The generation unit uses the generation AI to adjust the level of detail of the conversation based on the importance of the topic when generating the conversation. For example, for an important topic, a conversation including detailed explanations can be generated. Also, for a general topic, a concise conversation can be generated. Furthermore, for a topic that is of high interest to elderly people, a conversation including detailed information can be generated. This allows for more appropriate conversations by providing a level of detail of the conversation according to the importance of the topic.

[0043] The generation unit can apply different conversation generation algorithms depending on the topic category when generating a conversation. The generation unit uses a generation AI to apply different conversation generation algorithms depending on the topic category when generating a conversation. Conversation generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms, for example. The generation unit uses a generation AI to apply different conversation generation algorithms depending on the topic category when generating a conversation. For example, in the case of a topic related to health care, a conversation including specialized information can be generated. In addition, in the case of a topic related to hobbies, a conversation including interesting content can be generated. Furthermore, in the case of a topic related to travel, a conversation including detailed travel information can be generated. In this way, by providing a conversation generation algorithm depending on the topic category, more appropriate conversations can be realized.

[0044] The generation unit can improve the accuracy of the conversation when generating the conversation by referring to the results of past conversations with the elderly. The generation unit uses the generation AI to improve the accuracy of the conversation when generating the conversation by referring to the results of past conversations with the elderly. Past conversation results include, for example, but are not limited to, the content of the conversation and the user's reactions. The generation unit uses the generation AI to improve the accuracy of the conversation when generating the conversation by referring to the results of past conversations with the elderly. For example, the generation unit can prioritize topics that the elderly liked in the past. It can also eliminate topics that the elderly avoided in the past. Furthermore, it can generate optimal conversation content based on the results of past conversations with the elderly. This allows for more appropriate conversations to be realized by providing conversation generation based on the results of past conversations.

[0045] The generation unit can determine the priority of a conversation based on the time when the topic was submitted when generating the conversation. The generation unit uses the generation AI to determine the priority of a conversation based on the time when the topic was submitted when generating the conversation. The time when the topic was submitted includes, for example, but is not limited to, the most recent topic, a past topic, etc. The generation unit uses the generation AI to determine the priority of a conversation based on the time when the topic was submitted when generating the conversation. For example, the most recent topic can be given priority. It is also possible to give priority to topics that the elderly person has previously stated they would like to talk about. It is also possible to give priority to topics related to seasons or events. This allows for more appropriate conversations by providing priority of conversations based on the time when the topic was submitted.

[0046] The generation unit can adjust the order of conversations based on the relevance of topics when generating a conversation. The generation unit uses the generation AI to adjust the order of conversations based on the relevance of topics when generating a conversation. Topic relevance includes, but is not limited to, topic similarity and user interest, for example. The generation unit uses the generation AI to adjust the order of conversations based on the relevance of topics when generating a conversation. For example, highly relevant topics can be covered consecutively. Less relevant topics can also be postponed. Furthermore, topics that are of high interest to elderly people can be prioritized. This allows for a more appropriate conversation by providing a conversation order based on the relevance of topics.

[0047] The generation unit can adjust the use of technical terms in the conversation according to the elderly person's level of expertise when generating the conversation. The generation unit uses the generation AI to adjust the use of technical terms in the conversation according to the elderly person's level of expertise when generating the conversation. Examples of expertise levels include, but are not limited to, past conversation history and the user's occupation. The generation unit uses the generation AI to adjust the use of technical terms in the conversation according to the elderly person's level of expertise when generating the conversation. For example, if the elderly person has technical expertise, the generation unit can use a lot of technical terms. Also, if the elderly person does not have technical expertise, the generation unit can explain things in simple terms. Furthermore, the generation unit can select appropriate terms according to the elderly person's level of expertise. This allows for more appropriate conversations to be provided by providing conversations according to the elderly person's level of expertise.

[0048] The display unit can select the optimal display method by referring to the elderly person's past operation history when displaying. The display unit uses AI to select the optimal display method by referring to the elderly person's past operation history when displaying. The operation history includes, for example, but is not limited to, past click history and scroll history. The display unit uses AI to select the optimal display method by referring to the elderly person's past operation history when displaying. For example, the display unit can prioritize display methods that the elderly person has preferred in the past. It can also eliminate display methods that the elderly person has avoided in the past. Furthermore, it can suggest the optimal display method based on the elderly person's past operation history. In this way, a more comfortable interface can be realized by providing a display method based on the past operation history.

[0049] The display unit can customize the display content according to the elderly person's current task when displaying the information. The display unit uses AI to customize the display content according to the elderly person's current task when displaying the information. Current tasks include, but are not limited to, health management, hobbies, and travel planning, for example. The display unit uses AI to customize the display content according to the elderly person's current task when displaying the information. For example, if the elderly person is managing their health, health-related information can be displayed with priority. Also, if the elderly person is enjoying a hobby, information related to the hobby can be displayed with priority. Furthermore, if the elderly person is planning a trip, travel-related information can be displayed with priority. This provides more appropriate information by providing display content according to the current task.

[0050] The display unit can adjust the display method according to the elderly person's visual and hearing condition when displaying. The display unit uses AI to adjust the display method according to the elderly person's visual and hearing condition when displaying. Examples of visual and hearing conditions include, but are not limited to, eyesight and hearing level. The display unit uses AI to adjust the display method according to the elderly person's visual and hearing condition when displaying. For example, if the elderly person's visual impairment is impaired, large characters and high contrast displays can be provided. Also, if the elderly person's hearing impairment is impaired, audio guidance can be emphasized. Furthermore, the optimal display method can be provided according to the elderly person's visual and hearing condition. As a result, a more comfortable interface can be realized by providing a display method according to the elderly person's visual and hearing condition.

[0051] The display unit can select the optimal display method by taking into consideration the elderly person's device information when displaying. The display unit uses AI to select the optimal display method by taking into consideration the elderly person's device information when displaying. Device information includes, but is not limited to, for example, the type of device and the screen size. The display unit uses AI to select the optimal display method by taking into consideration the elderly person's device information when displaying. For example, if the elderly person is using a smartphone, a display method that matches the screen size can be provided. Also, if the elderly person is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the elderly person is using a smartwatch, a simple and highly visible display method can be provided. As a result, a more comfortable interface can be realized by providing a display method based on device information.

[0052] The display unit can make the display content multilingual when displayed according to the elderly person's language setting. The display unit uses AI to make the display content multilingual when displayed according to the elderly person's language setting. Language settings include, but are not limited to, Japanese, English, and other languages. The display unit uses AI to make the display content multilingual when displayed according to the elderly person's language setting. For example, the display content is automatically set based on the language setting of the elderly person's device. In addition, a language switching function can be provided if the elderly person speaks multiple languages. Furthermore, if the elderly person selects a specific language, the display content can be provided in that language. In this way, by providing multilingual display content based on the language setting, more appropriate information can be provided.

[0053] The display unit can customize the display method by reflecting the elderly person's past feedback when displaying. The display unit uses AI to customize the display method by reflecting the elderly person's past feedback when displaying. Feedback includes, for example, user ratings, comments, etc., but is not limited to these examples. The display unit uses AI to customize the display method by reflecting the elderly person's past feedback when displaying. For example, the display unit can prioritize providing display methods that the elderly person has previously preferred. It can also eliminate display methods that the elderly person has previously avoided. Furthermore, it can suggest the optimal display method based on the elderly person's past feedback. In this way, a more comfortable interface can be realized by providing a display method based on past feedback.

[0054] The observation unit can optimize the observation algorithm by referring to the elderly person's past health data during observation. The observation unit uses AI to optimize the observation algorithm by referring to the elderly person's past health data during observation. Past health data includes, but is not limited to, past diagnosis results and health checkup data. The observation unit uses AI to optimize the observation algorithm by referring to the elderly person's past health data during observation. For example, the observation unit suggests an optimal observation method based on the elderly person's past health data. It can also provide an observation method for early detection of abnormalities from the elderly person's past health data. It can also analyze the elderly person's past health data and suggest the optimal timing for observation. This allows for more appropriate observation by providing an observation algorithm based on past health data.

[0055] The observation unit can analyze the elderly person's lifestyle rhythm during observation and select the optimal observation timing. The observation unit uses AI to analyze the elderly person's lifestyle rhythm during observation and select the optimal observation timing. Lifestyle rhythms include, but are not limited to, for example, sleep patterns and meal times. The observation unit uses AI to analyze the elderly person's lifestyle rhythm during observation and select the optimal observation timing. For example, the observation timing can be adjusted based on the elderly person's lifestyle rhythm. Also, observation can be performed during times when the elderly person is active. Furthermore, observation can be performed during times when the elderly person is relaxed. This allows for more appropriate observation by providing observation timing based on the elderly person's lifestyle rhythm.

[0056] The observation unit can improve the observation method by reflecting feedback from the elderly during observation. The observation unit uses AI to improve the observation method by reflecting feedback from the elderly during observation. Feedback includes, for example, but is not limited to, user ratings and comments. The observation unit uses AI to improve the observation method by reflecting feedback from the elderly during observation. For example, the observation method can be adjusted based on the feedback from the elderly. It can also provide observation methods preferred by the elderly preferentially. Furthermore, it can improve the accuracy of observation by reflecting feedback from the elderly. In this way, more appropriate observation can be achieved by providing observation methods based on feedback.

[0057] The observation unit can select an observation method by taking into account the geographical location information of the elderly person during observation. The observation unit uses AI to select an observation method by taking into account the geographical location information of the elderly person during observation. Geographical location information includes, but is not limited to, for example, GPS data and address information. The observation unit uses AI to select an observation method by taking into account the geographical location information of the elderly person during observation. For example, if the elderly person is at home, observation can be performed by taking into account the environment around the home. Also, if the elderly person is out, observation can be performed by taking into account the environment of the destination. Furthermore, if the elderly person is traveling, observation can be performed by taking into account the environment of the destination. This allows for more appropriate observation by providing an observation method based on geographical location information.

[0058] The observation unit can analyze the social media activity of the elderly person during observation to improve the accuracy of the observation. The observation unit uses AI to analyze the social media activity of the elderly person during observation to improve the accuracy of the observation. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The observation unit uses AI to analyze the social media activity of the elderly person during observation to improve the accuracy of the observation. For example, the content of posts on social media of the elderly person is analyzed to observe the health condition. Observation can also be performed by referring to the activities of the elderly person's friends on social media. Furthermore, the accuracy of the observation can be improved based on the check-in information of the elderly person on social media. This provides an observation method based on social media activity, thereby realizing more appropriate observation.

[0059] The observation unit can customize the observation method by reflecting the elderly person's past feedback during observation. The observation unit uses AI to customize the observation method by reflecting the elderly person's past feedback during observation. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The observation unit uses AI to customize the observation method by reflecting the elderly person's past feedback during observation. For example, the observation unit can suggest an optimal observation method based on the elderly person's past feedback. It can also provide observation methods that the elderly person has previously preferred preferentially. Furthermore, it can improve the accuracy of observation by reflecting the elderly person's past feedback. In this way, more appropriate observation can be achieved by providing an observation method based on past feedback.

[0060] The notification unit can optimize the notification algorithm by referring to the elderly person's past health data at the time of notification. The notification unit uses AI to optimize the notification algorithm by referring to the elderly person's past health data at the time of notification. Past health data includes, for example, past diagnosis results, health checkup data, etc., but is not limited to these examples. The notification unit uses AI to optimize the notification algorithm by referring to the elderly person's past health data at the time of notification. For example, the notification unit proposes an optimal notification method based on the elderly person's past health data. It can also provide a notification method for early detection of abnormalities from the elderly person's past health data. Furthermore, it can analyze the elderly person's past health data and propose the optimal notification timing. This allows for more appropriate notifications to be realized by providing a notification algorithm based on past health data.

[0061] The notification unit can analyze the elderly person's lifestyle rhythm at the time of notification and select the optimal notification timing. The notification unit uses AI to analyze the elderly person's lifestyle rhythm at the time of notification and select the optimal notification timing. Lifestyle rhythms include, but are not limited to, for example, sleep patterns and meal times. The notification unit uses AI to analyze the elderly person's lifestyle rhythm at the time of notification and select the optimal notification timing. For example, the notification unit adjusts the notification timing based on the elderly person's lifestyle rhythm. It is also possible to provide notifications during times when the elderly person is active. It is also possible to provide notifications during times when the elderly person is relaxed. This allows for more appropriate notifications by providing notification timing based on the elderly person's lifestyle rhythm.

[0062] The notification unit can improve the notification method by reflecting feedback from the elderly at the time of notification. The notification unit uses AI to improve the notification method by reflecting feedback from the elderly at the time of notification. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The notification unit uses AI to improve the notification method by reflecting feedback from the elderly at the time of notification. For example, the notification method can be adjusted based on the feedback from the elderly. It is also possible to provide notification methods preferred by the elderly preferentially. Furthermore, it is possible to improve the accuracy of notifications by reflecting feedback from the elderly. In this way, more appropriate notifications can be realized by providing notification methods based on feedback.

[0063] The notification unit can select a notification method taking into consideration the geographical location information of the elderly person at the time of notification. The notification unit uses AI to select a notification method taking into consideration the geographical location information of the elderly person at the time of notification. Geographical location information includes, but is not limited to, for example, GPS data, address information, etc. The notification unit uses AI to select a notification method taking into consideration the geographical location information of the elderly person at the time of notification. For example, if the elderly person is at home, the notification can be made taking into consideration the environment around the home. Also, if the elderly person is out, the notification can be made taking into consideration the environment of the destination. Furthermore, if the elderly person is traveling, the notification can be made taking into consideration the environment of the destination. In this way, more appropriate notifications can be realized by providing a notification method based on geographical location information.

[0064] The notification unit can analyze the social media activity of the elderly person at the time of notification to improve the accuracy of the notification. The notification unit uses AI to analyze the social media activity of the elderly person at the time of notification to improve the accuracy of the notification. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The notification unit uses AI to analyze the social media activity of the elderly person at the time of notification to improve the accuracy of the notification. For example, the content of posts made by the elderly person on social media can be analyzed to notify the elderly person of their health status. Notifications can also be made based on the activities of the elderly person's friends on social media. Furthermore, the accuracy of notifications can be improved based on the elderly person's check-in information on social media. This provides a notification method based on social media activity, thereby realizing more appropriate notifications.

[0065] The notification unit can customize the notification method by reflecting the elderly person's past feedback at the time of notification. The notification unit uses AI to customize the notification method by reflecting the elderly person's past feedback at the time of notification. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The notification unit uses AI to customize the notification method by reflecting the elderly person's past feedback at the time of notification. For example, the notification unit can suggest an optimal notification method based on the elderly person's past feedback. It can also prioritize notification methods that the elderly person preferred in the past. Furthermore, it can improve the accuracy of notifications by reflecting the elderly person's past feedback. In this way, more appropriate notifications can be achieved by providing notification methods based on past feedback.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The simulated family generation system can further include an image recognition unit. The image recognition unit analyzes the facial expressions and movements of the elderly person in real time and sends the results to the generation unit. For example, when the elderly person smiles, the image recognition unit analyzes the facial expression and sends it to the generation unit. The generation unit can generate an appropriate response based on the received information. The image recognition unit can also analyze the movements of the elderly person and use this information to estimate their health condition. This allows for a more accurate understanding of the elderly person's health condition and allows for appropriate responses. Furthermore, the image recognition unit can learn changes in the elderly person's facial expressions and movements and detect abnormalities early on.

[0068] The simulated family generation system can further include an environmental sensor unit. The environmental sensor unit monitors the elderly person's living environment in real time and transmits the information to the generation unit. For example, it collects information such as room temperature, humidity, and illuminance and transmits it to the generation unit. The generation unit can generate appropriate conversations based on the received information. For example, if the room temperature is high, it can generate a conversation such as, "It's hot today, isn't it?" The environmental sensor unit can also detect abnormal environmental changes and transmit the information to the notification unit. This allows for appropriate management of the elderly person's living environment and supports a comfortable life. Furthermore, the environmental sensor unit can learn the elderly person's living patterns and detect abnormalities early on.

[0069] The simulated family generation system can further include a learning unit. The learning unit learns the elderly person's past conversation history and behavioral patterns and provides feedback to the generation unit. For example, it learns topics that the elderly person has previously preferred and avoided and sends this to the generation unit. The generation unit can generate appropriate conversations based on the received information. The learning unit can also learn the elderly person's behavioral patterns and optimize the timing of reminders and notifications. This makes it possible to provide services that meet the individual needs of the elderly person. Furthermore, the learning unit can reflect the elderly person's feedback and improve the accuracy of the entire system.

[0070] The simulated family generation system can further include a health management unit. The health management unit collects health data of the elderly and provides feedback to the generation unit. For example, it collects data such as heart rate, blood pressure, and body temperature and sends it to the generation unit. The generation unit can generate appropriate conversations based on the received information. For example, if the heart rate is high, it can generate conversations such as "Let's take a short break." The health management unit can also detect abnormal health data and send it to the notification unit. This allows the elderly's health condition to be properly managed and abnormalities to be detected early. Furthermore, the health management unit can accumulate health data of the elderly and support long-term health management.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit accepts natural language input. Natural languages ​​include Japanese, English, and other languages. The reception unit can accept voice input and text input, estimate the user's emotions, and adjust the natural language input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Step 2: The generation unit uses a generation AI to generate a conversation based on the information received by the reception unit. Generation AIs include GPT-4 and Gemini. The generation unit generates conversations covering a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI will follow up with, "What was the hot spring like?" Step 3: The display unit displays the conversation generated by the generation unit. The display unit displays the conversation generated on a mannequin-type monitor. The mannequin-type monitor includes a size, a display method, an interactive function, etc. Step 4: The observation unit observes the user's health condition through the conversation displayed on the display unit. The health condition includes heart rate, blood pressure, and facial expression analysis. The observation unit observes the user's health condition from the conversation and video and detects abnormalities early. Step 5: The notification unit notifies the patient of any abnormal health condition observed by the observation unit. The notification unit notifies the patient's next of kin of the abnormal health condition. The next of kin includes family members, relatives, etc.

[0073] (Example 2) The simulated family generation system according to an embodiment of the present invention uses a generation AI to generate a simulated family so that elderly people can live independently without feeling lonely. This system aims to help elderly people maintain their mental and physical health through the simulated family and lead a healthy life. The simulated family generation system accepts natural language input, and the generation AI analyzes the commands to generate conversations for the simulated family. The generated conversations are displayed on a mannequin-shaped monitor, and the conversations take place. The content of the conversations covers a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI follows up with, "What was the hot spring like?" This natural conversation reduces the elderly's sense of loneliness. Furthermore, the generation AI provides information and reminders necessary for daily life. For example, it reminds the elderly, "You have a doctor's appointment at 10 o'clock today," to help them remember. The generation AI also monitors the elderly's health status from conversations and video, and notifies relatives if any abnormalities are detected. For example, if an elderly person says, "I haven't had much of an appetite lately," the AI ​​will notify their next of kin of this information, allowing for early intervention. This allows the pseudo-family generation system to provide emotional support to elderly people through their pseudo-family members, helping them live without feeling lonely. Health monitoring and notification functions also allow for early detection of abnormalities and appropriate responses. This allows the pseudo-family generation system to maintain the mental and physical health of elderly people, helping them live healthy lives. For example, this system allows elderly people to live alone without feeling lonely, providing emotional support. Health monitoring and notification functions also allow for early detection of abnormalities and appropriate responses.

[0074] A simulated family creation system according to an embodiment includes a reception unit, a generation unit, a display unit, an observation unit, and a notification unit. The reception unit receives input in natural language. Examples of natural languages ​​include, but are not limited to, Japanese, English, and other languages. The reception unit can receive, for example, voice input and text input. The reception unit can also estimate a user's emotions and adjust the natural language input method based on the estimated user emotions. For example, if a user is feeling stressed, a simple interface can be provided to minimize input steps. The generation unit uses a generation AI to generate conversations based on the information received by the reception unit. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit generates conversations covering a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI then follows up with, "What was the hot spring like?" The display unit displays the conversation generated by the generation unit. The display unit displays the conversation generated on, for example, a mannequin-type monitor. The mannequin-type monitor may include, for example, a size, a display method, and interactive functions, but is not limited to these examples. The observation unit observes the user's health condition through the conversation displayed by the display unit. The health condition may include, for example, a heart rate, blood pressure, and facial expression analysis, but is not limited to these examples. The observation unit observes the user's health condition from the conversation and video and detects abnormalities early. The notification unit notifies the user of any abnormalities in the health condition observed by the observation unit. The notification unit notifies, for example, close relatives of the abnormal health condition. Close relatives include, for example, family members and relatives, but are not limited to these examples. As a result, the simulated family generation system according to the embodiment allows elderly people to receive psychological support through the simulated family, reduce feelings of loneliness, and detect abnormalities in their health early.

[0075] The generation unit can generate conversations using a generation AI. The generation unit generates conversations using a generation AI. Examples of generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit uses the generation AI to generate conversations covering a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI follows up with, "What was the hot spring like?" In this way, the generation AI can generate natural conversations and reduce the elderly person's sense of loneliness.

[0076] The generation unit can use a generation AI to generate conversations that cover multiple topics, such as hobbies, travel, and food, as well as health care and neighborhood gossip. The generation unit uses the generation AI to generate conversations that cover multiple topics, such as health care and neighborhood gossip, from topics such as hobbies, travel, and food. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit uses the generation AI to generate conversations that cover various topics, such as health care and neighborhood gossip, from topics such as hobbies, travel, and food. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI follows up with, "What was the hot spring like?" This allows for a variety of topics to be covered, enabling natural conversations with the elderly and reducing feelings of loneliness.

[0077] The display unit can display the generated conversation on a mannequin-type monitor. The display unit displays the conversation generated by the generation unit on the mannequin-type monitor. The mannequin-type monitor may have, for example, a size, a display method, an interactive function, etc., but is not limited to these examples. By displaying the generated conversation on the mannequin-type monitor, the display unit realizes a visually realistic conversation with the virtual family. In this way, by using the mannequin-type monitor, a visually realistic conversation with the virtual family is realized.

[0078] The observation unit can observe the user's health condition from conversation or video. The observation unit observes the user's health condition from conversation or video. Health conditions include, but are not limited to, heart rate, blood pressure, and facial expression analysis. The observation unit observes the user's health condition from conversation or video and detects abnormalities early. For example, the observation unit observes the health condition based on the content of the conversation or changes in facial expressions. The observation unit can also monitor the user's health condition in real time and detect abnormalities. This allows the health condition of elderly people to be observed through conversation or video and abnormalities to be detected early.

[0079] The notification unit can notify close relatives of any abnormalities in the health condition. The notification unit notifies close relatives of any abnormalities in the health condition observed by the observation unit. Close relatives include, but are not limited to, family members and relatives. The notification unit notifies close relatives of any abnormalities in the health condition, thereby enabling a prompt response. For example, if an abnormality in the user's health condition is detected, the notification unit notifies close relatives by email or telephone. The notification unit can also select an appropriate notification method depending on the content and urgency of the abnormality. This allows close relatives to be notified of any abnormalities in the elderly person's health condition early, enabling a prompt response.

[0080] The generation unit can generate conversations that convey information necessary for daily life and provide reminders. The generation unit uses a generation AI to generate conversations that convey information necessary for daily life and provide reminders. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The generation unit uses a generation AI to generate conversations that convey information necessary for daily life and provide reminders. For example, the generation AI can provide a reminder such as, "You have a hospital appointment at 10 o'clock today," to help the elderly person not forget. The generation unit can also use a generation AI to generate conversations that convey information necessary for daily life, such as when to take medicine or what to eat. This allows elderly people to receive information necessary for daily life and receive reminders, allowing them to live their daily lives smoothly.

[0081] The reception unit can estimate a user's emotion and adjust the natural language input method based on the estimated user emotion. The reception unit estimates a user's emotion and adjusts the natural language input method based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. The reception unit estimates a user's emotion and adjusts the natural language input method based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick natural language input. This provides an input method that corresponds to the user's emotion, thereby achieving a more comfortable interface.

[0082] The reception unit can analyze the elderly person's past conversation history and select the optimal input method. The reception unit analyzes the elderly person's past conversation history and selects the optimal input method. Input methods include, but are not limited to, voice input and text input, for example. The reception unit analyzes the elderly person's past conversation history and selects the optimal input method. For example, phrases frequently used by the elderly person in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the elderly person has used in the past. Furthermore, the reception unit can predict and suggest phrases that will be used during specific time periods based on the elderly person's past conversation history. This improves convenience for the elderly by providing the optimal input method based on the elderly person's past conversation history.

[0083] The reception unit can filter natural language input based on the elderly person's current health condition and mood. The reception unit filters natural language input based on the elderly person's current health condition and mood. Examples of filtering include, but are not limited to, excluding specific keywords and prioritizing the display of specific topics. The reception unit filters natural language input based on the elderly person's current health condition and mood. For example, if the elderly person is tired, the reception unit prioritizes simple questions and short conversations. Also, if the elderly person is in good health, the reception unit can accept detailed conversations and complex questions. Furthermore, if the elderly person is not feeling well, the reception unit can avoid health-related questions and prioritize relaxing topics. This allows for more appropriate conversations by providing input content that is appropriate for the elderly person's health condition and mood.

[0084] The reception unit can select the optimal input means according to the elderly person's input method when inputting natural language. The reception unit selects the optimal input means according to the elderly person's input method when inputting natural language. Input methods include, but are not limited to, for example, voice, text, and gesture. The reception unit selects the optimal input means according to the elderly person's input method when inputting natural language. For example, if the elderly person prefers voice input, voice input can be provided preferentially. Also, if the elderly person prefers text input, keyboard input can be provided preferentially. Furthermore, if the elderly person prefers gesture input, gesture input can be provided preferentially. In this way, a more comfortable interface can be realized by providing input means according to the elderly person's preferences.

[0085] The reception unit can estimate the user's emotion and determine the priority of input content based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of input content based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. The reception unit estimates the user's emotion and determines the priority of input content based on the estimated user's emotion. For example, if the user is nervous, the reception unit can prioritize topics that will help the user relax. Also, if the user is relaxed, the reception unit can prompt the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize input of important information. This allows for more appropriate conversations by providing a priority of input content according to the user's emotion.

[0086] The reception unit can preferentially accept highly relevant inputs in consideration of the geographical location information of the elderly person when inputting natural language. The reception unit preferentially accepts highly relevant inputs in consideration of the geographical location information of the elderly person when inputting natural language. Geographical location information includes, but is not limited to, examples of GPS data and address information. The reception unit preferentially accepts highly relevant inputs in consideration of the geographical location information of the elderly person when inputting natural language. For example, if the elderly person is at home, the reception unit preferentially accepts topics about the area around the elderly person's home. Also, if the elderly person is out, the reception unit can preferentially accept topics related to the destination. Furthermore, if the elderly person is traveling, the reception unit can preferentially accept topics related to the travel destination. This allows for more appropriate conversations by providing input content based on the geographical location information of the elderly person.

[0087] The reception unit can analyze the elderly person's social media activity and accept related input when natural language is input. The reception unit analyzes the elderly person's social media activity and accepts related input when natural language is input. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The reception unit analyzes the elderly person's social media activity and accepts related input when natural language is input. For example, the reception unit may accept related topics based on content shared by the elderly person on social media. Related topics may also be accepted based on the activities of the elderly person's friends on social media. Furthermore, the reception unit may analyze the content posted by the elderly person on social media and accept related topics. This allows for more appropriate conversations by providing input based on the elderly person's social media activity.

[0088] The reception unit can customize the input method by reflecting the elderly person's past feedback when inputting natural language. The reception unit customizes the input method by reflecting the elderly person's past feedback when inputting natural language. Feedback includes, for example, user ratings, comments, etc., but is not limited to these examples. The reception unit customizes the input method by reflecting the elderly person's past feedback when inputting natural language. For example, the reception unit can preferentially provide input methods that the elderly person has previously preferred. It can also eliminate input methods that the elderly person has previously avoided. Furthermore, it can suggest an optimal input method based on the elderly person's past feedback. In this way, a more comfortable interface can be realized by providing an input method based on the elderly person's past feedback.

[0089] The generation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user emotions. The generation unit uses a generation AI to estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The generation unit uses a generation AI to estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a conversation that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a short, to-the-point conversation. Furthermore, if the user is excited, the generation unit can generate a conversation that adds visually stimulating effects. This allows for more natural conversations by providing a way of expressing the conversation according to the user's emotions.

[0090] The generation unit can adjust the level of detail of the conversation based on the importance of the topic when generating the conversation. The generation unit uses the generation AI to adjust the level of detail of the conversation based on the importance of the topic when generating the conversation. The importance of the topic includes, but is not limited to, for example, the user's level of interest and urgency. The generation unit uses the generation AI to adjust the level of detail of the conversation based on the importance of the topic when generating the conversation. For example, for an important topic, a conversation including detailed explanations can be generated. Also, for a general topic, a concise conversation can be generated. Furthermore, for a topic that is of high interest to elderly people, a conversation including detailed information can be generated. This allows for more appropriate conversations by providing a level of detail of the conversation according to the importance of the topic.

[0091] The generation unit can apply different conversation generation algorithms depending on the topic category when generating a conversation. The generation unit uses a generation AI to apply different conversation generation algorithms depending on the topic category when generating a conversation. Conversation generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms, for example. The generation unit uses a generation AI to apply different conversation generation algorithms depending on the topic category when generating a conversation. For example, in the case of a topic related to health care, a conversation including specialized information can be generated. In addition, in the case of a topic related to hobbies, a conversation including interesting content can be generated. Furthermore, in the case of a topic related to travel, a conversation including detailed travel information can be generated. In this way, by providing a conversation generation algorithm depending on the topic category, more appropriate conversations can be realized.

[0092] The generation unit can improve the accuracy of the conversation when generating the conversation by referring to the results of past conversations with the elderly. The generation unit uses the generation AI to improve the accuracy of the conversation when generating the conversation by referring to the results of past conversations with the elderly. Past conversation results include, for example, but are not limited to, the content of the conversation and the user's reactions. The generation unit uses the generation AI to improve the accuracy of the conversation when generating the conversation by referring to the results of past conversations with the elderly. For example, the generation unit can prioritize topics that the elderly liked in the past. It can also eliminate topics that the elderly avoided in the past. Furthermore, it can generate optimal conversation content based on the results of past conversations with the elderly. This allows for more appropriate conversations to be realized by providing conversation generation based on the results of past conversations.

[0093] The generation unit can estimate the user's emotions and adjust the length of the conversation based on the estimated user emotions. The generation unit uses a generation AI to estimate the user's emotions and adjust the length of the conversation based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. The generation unit uses a generation AI to estimate the user's emotions and adjust the length of the conversation based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point conversation. Alternatively, if the user is relaxed, the generation unit can generate a longer conversation with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a conversation with visually stimulating effects. This allows for more appropriate conversations by providing a conversation length that corresponds to the user's emotions.

[0094] The generation unit can determine the priority of a conversation based on the time when the topic was submitted when generating the conversation. The generation unit uses the generation AI to determine the priority of a conversation based on the time when the topic was submitted when generating the conversation. The time when the topic was submitted includes, for example, but is not limited to, the most recent topic, a past topic, etc. The generation unit uses the generation AI to determine the priority of a conversation based on the time when the topic was submitted when generating the conversation. For example, the most recent topic can be given priority. It is also possible to give priority to topics that the elderly person has previously stated they would like to talk about. It is also possible to give priority to topics related to seasons or events. This allows for more appropriate conversations by providing priority of conversations based on the time when the topic was submitted.

[0095] The generation unit can adjust the order of conversations based on the relevance of topics when generating a conversation. The generation unit uses the generation AI to adjust the order of conversations based on the relevance of topics when generating a conversation. Topic relevance includes, but is not limited to, topic similarity and user interest, for example. The generation unit uses the generation AI to adjust the order of conversations based on the relevance of topics when generating a conversation. For example, highly relevant topics can be covered consecutively. Less relevant topics can also be postponed. Furthermore, topics that are of high interest to elderly people can be prioritized. This allows for a more appropriate conversation by providing a conversation order based on the relevance of topics.

[0096] The generation unit can adjust the use of technical terms in the conversation according to the elderly person's level of expertise when generating the conversation. The generation unit uses the generation AI to adjust the use of technical terms in the conversation according to the elderly person's level of expertise when generating the conversation. Examples of expertise levels include, but are not limited to, past conversation history and the user's occupation. The generation unit uses the generation AI to adjust the use of technical terms in the conversation according to the elderly person's level of expertise when generating the conversation. For example, if the elderly person has technical expertise, the generation unit can use a lot of technical terms. Also, if the elderly person does not have technical expertise, the generation unit can explain things in simple terms. Furthermore, the generation unit can select appropriate terms according to the elderly person's level of expertise. This allows for more appropriate conversations to be provided by providing conversations according to the elderly person's level of expertise.

[0097] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. The display unit uses AI to estimate the user's emotions and adjust the display method based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The display unit uses AI to estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This provides a display method that corresponds to the user's emotions, thereby realizing a more comfortable interface.

[0098] The display unit can select the optimal display method by referring to the elderly person's past operation history when displaying. The display unit uses AI to select the optimal display method by referring to the elderly person's past operation history when displaying. The operation history includes, for example, but is not limited to, past click history and scroll history. The display unit uses AI to select the optimal display method by referring to the elderly person's past operation history when displaying. For example, the display unit can prioritize display methods that the elderly person has preferred in the past. It can also eliminate display methods that the elderly person has avoided in the past. Furthermore, it can suggest the optimal display method based on the elderly person's past operation history. In this way, a more comfortable interface can be realized by providing a display method based on the past operation history.

[0099] The display unit can customize the display content according to the elderly person's current task when displaying the information. The display unit uses AI to customize the display content according to the elderly person's current task when displaying the information. Current tasks include, but are not limited to, health management, hobbies, and travel planning, for example. The display unit uses AI to customize the display content according to the elderly person's current task when displaying the information. For example, if the elderly person is managing their health, health-related information can be displayed with priority. Also, if the elderly person is enjoying a hobby, information related to the hobby can be displayed with priority. Furthermore, if the elderly person is planning a trip, travel-related information can be displayed with priority. This provides more appropriate information by providing display content according to the current task.

[0100] The display unit can adjust the display method according to the elderly person's visual and hearing condition when displaying. The display unit uses AI to adjust the display method according to the elderly person's visual and hearing condition when displaying. Examples of visual and hearing conditions include, but are not limited to, eyesight and hearing level. The display unit uses AI to adjust the display method according to the elderly person's visual and hearing condition when displaying. For example, if the elderly person's visual impairment is impaired, large characters and high contrast displays can be provided. Also, if the elderly person's hearing impairment is impaired, audio guidance can be emphasized. Furthermore, the optimal display method can be provided according to the elderly person's visual and hearing condition. As a result, a more comfortable interface can be realized by providing a display method according to the elderly person's visual and hearing condition.

[0101] The display unit can estimate the user's emotions and determine the priority of display content based on the estimated user emotions. The display unit uses AI to estimate the user's emotions and determine the priority of display content based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The display unit uses AI to estimate the user's emotions and determine the priority of display content based on the estimated user emotions. For example, if the user is nervous, content that helps the user relax can be displayed preferentially. Also, if the user is relaxed, detailed information can be displayed preferentially. Furthermore, if the user is in a hurry, important information can be displayed preferentially. This provides more appropriate information by prioritizing display content according to the user's emotions.

[0102] The display unit can select the optimal display method by taking into consideration the elderly person's device information when displaying. The display unit uses AI to select the optimal display method by taking into consideration the elderly person's device information when displaying. Device information includes, but is not limited to, for example, the type of device and the screen size. The display unit uses AI to select the optimal display method by taking into consideration the elderly person's device information when displaying. For example, if the elderly person is using a smartphone, a display method that matches the screen size can be provided. Also, if the elderly person is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the elderly person is using a smartwatch, a simple and highly visible display method can be provided. As a result, a more comfortable interface can be realized by providing a display method based on device information.

[0103] The display unit can make the display content multilingual when displayed according to the elderly person's language setting. The display unit uses AI to make the display content multilingual when displayed according to the elderly person's language setting. Language settings include, but are not limited to, Japanese, English, and other languages. The display unit uses AI to make the display content multilingual when displayed according to the elderly person's language setting. For example, the display content is automatically set based on the language setting of the elderly person's device. In addition, a language switching function can be provided if the elderly person speaks multiple languages. Furthermore, if the elderly person selects a specific language, the display content can be provided in that language. In this way, by providing multilingual display content based on the language setting, more appropriate information can be provided.

[0104] The display unit can customize the display method by reflecting the elderly person's past feedback when displaying. The display unit uses AI to customize the display method by reflecting the elderly person's past feedback when displaying. Feedback includes, for example, user ratings, comments, etc., but is not limited to these examples. The display unit uses AI to customize the display method by reflecting the elderly person's past feedback when displaying. For example, the display unit can prioritize providing display methods that the elderly person has previously preferred. It can also eliminate display methods that the elderly person has previously avoided. Furthermore, it can suggest the optimal display method based on the elderly person's past feedback. In this way, a more comfortable interface can be realized by providing a display method based on past feedback.

[0105] The observation unit can estimate the user's emotions and adjust the health condition observation method based on the estimated user emotions. The observation unit uses AI to estimate the user's emotions and adjust the health condition observation method based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The observation unit uses AI to estimate the user's emotions and adjust the health condition observation method based on the estimated user emotions. For example, if the user is nervous, the observation unit performs observation in a relaxing environment. Also, if the user is relaxed, detailed health condition observation can be performed. Furthermore, if the user is in a hurry, brief health condition observation can be performed. This allows for more appropriate health condition observation by providing a health condition observation method that corresponds to the user's emotions.

[0106] The observation unit can optimize the observation algorithm by referring to the elderly person's past health data during observation. The observation unit uses AI to optimize the observation algorithm by referring to the elderly person's past health data during observation. Past health data includes, but is not limited to, past diagnosis results and health checkup data. The observation unit uses AI to optimize the observation algorithm by referring to the elderly person's past health data during observation. For example, the observation unit suggests an optimal observation method based on the elderly person's past health data. It can also provide an observation method for early detection of abnormalities from the elderly person's past health data. It can also analyze the elderly person's past health data and suggest the optimal timing for observation. This allows for more appropriate observation by providing an observation algorithm based on past health data.

[0107] The observation unit can analyze the elderly person's lifestyle rhythm during observation and select the optimal observation timing. The observation unit uses AI to analyze the elderly person's lifestyle rhythm during observation and select the optimal observation timing. Lifestyle rhythms include, but are not limited to, for example, sleep patterns and meal times. The observation unit uses AI to analyze the elderly person's lifestyle rhythm during observation and select the optimal observation timing. For example, the observation timing can be adjusted based on the elderly person's lifestyle rhythm. Also, observation can be performed during times when the elderly person is active. Furthermore, observation can be performed during times when the elderly person is relaxed. This allows for more appropriate observation by providing observation timing based on the elderly person's lifestyle rhythm.

[0108] The observation unit can improve the observation method by reflecting feedback from the elderly during observation. The observation unit uses AI to improve the observation method by reflecting feedback from the elderly during observation. Feedback includes, for example, but is not limited to, user ratings and comments. The observation unit uses AI to improve the observation method by reflecting feedback from the elderly during observation. For example, the observation method can be adjusted based on the feedback from the elderly. It can also provide observation methods preferred by the elderly preferentially. Furthermore, it can improve the accuracy of observation by reflecting feedback from the elderly. In this way, more appropriate observation can be achieved by providing observation methods based on feedback.

[0109] The observation unit can estimate the user's emotions and prioritize the observation results based on the estimated user emotions. The observation unit uses AI to estimate the user's emotions and prioritize the observation results based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The observation unit uses AI to estimate the user's emotions and prioritize the observation results based on the estimated user emotions. For example, if the user is nervous, the observation unit prioritizes observation of relaxing content. Also, if the user is relaxed, detailed observation results can be prioritized. Furthermore, if the user is in a hurry, important observation results can be prioritized. In this way, more appropriate information can be provided by prioritizing observation results according to the user's emotions.

[0110] The observation unit can select an observation method by taking into account the geographical location information of the elderly person during observation. The observation unit uses AI to select an observation method by taking into account the geographical location information of the elderly person during observation. Geographical location information includes, but is not limited to, for example, GPS data and address information. The observation unit uses AI to select an observation method by taking into account the geographical location information of the elderly person during observation. For example, if the elderly person is at home, observation can be performed by taking into account the environment around the home. Also, if the elderly person is out, observation can be performed by taking into account the environment of the destination. Furthermore, if the elderly person is traveling, observation can be performed by taking into account the environment of the destination. This allows for more appropriate observation by providing an observation method based on geographical location information.

[0111] The observation unit can analyze the social media activity of the elderly person during observation to improve the accuracy of the observation. The observation unit uses AI to analyze the social media activity of the elderly person during observation to improve the accuracy of the observation. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The observation unit uses AI to analyze the social media activity of the elderly person during observation to improve the accuracy of the observation. For example, the content of posts on social media of the elderly person is analyzed to observe the health condition. Observation can also be performed by referring to the activities of the elderly person's friends on social media. Furthermore, the accuracy of the observation can be improved based on the check-in information of the elderly person on social media. This provides an observation method based on social media activity, thereby realizing more appropriate observation.

[0112] The observation unit can customize the observation method by reflecting the elderly person's past feedback during observation. The observation unit uses AI to customize the observation method by reflecting the elderly person's past feedback during observation. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The observation unit uses AI to customize the observation method by reflecting the elderly person's past feedback during observation. For example, the observation unit can suggest an optimal observation method based on the elderly person's past feedback. It can also provide observation methods that the elderly person has previously preferred preferentially. Furthermore, it can improve the accuracy of observation by reflecting the elderly person's past feedback. In this way, more appropriate observation can be achieved by providing an observation method based on past feedback.

[0113] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. The notification unit uses AI to estimate the user's emotions and adjust the notification method based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The notification unit uses AI to estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, the notification can be made in a calm voice. Alternatively, if the user is relaxed, the notification can be made in a cheerful voice. Furthermore, if the user is in a hurry, the notification can be made quickly and concisely. This allows for more appropriate notifications by providing a notification method that corresponds to the user's emotions.

[0114] The notification unit can optimize the notification algorithm by referring to the elderly person's past health data at the time of notification. The notification unit uses AI to optimize the notification algorithm by referring to the elderly person's past health data at the time of notification. Past health data includes, for example, past diagnosis results, health checkup data, etc., but is not limited to these examples. The notification unit uses AI to optimize the notification algorithm by referring to the elderly person's past health data at the time of notification. For example, the notification unit proposes an optimal notification method based on the elderly person's past health data. It can also provide a notification method for early detection of abnormalities from the elderly person's past health data. Furthermore, it can analyze the elderly person's past health data and propose the optimal notification timing. This allows for more appropriate notifications to be realized by providing a notification algorithm based on past health data.

[0115] The notification unit can analyze the elderly person's lifestyle rhythm at the time of notification and select the optimal notification timing. The notification unit uses AI to analyze the elderly person's lifestyle rhythm at the time of notification and select the optimal notification timing. Lifestyle rhythms include, but are not limited to, for example, sleep patterns and meal times. The notification unit uses AI to analyze the elderly person's lifestyle rhythm at the time of notification and select the optimal notification timing. For example, the notification unit adjusts the notification timing based on the elderly person's lifestyle rhythm. It is also possible to provide notifications during times when the elderly person is active. It is also possible to provide notifications during times when the elderly person is relaxed. This allows for more appropriate notifications by providing notification timing based on the elderly person's lifestyle rhythm.

[0116] The notification unit can improve the notification method by reflecting feedback from the elderly at the time of notification. The notification unit uses AI to improve the notification method by reflecting feedback from the elderly at the time of notification. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The notification unit uses AI to improve the notification method by reflecting feedback from the elderly at the time of notification. For example, the notification method can be adjusted based on the feedback from the elderly. It is also possible to provide notification methods preferred by the elderly preferentially. Furthermore, it is possible to improve the accuracy of notifications by reflecting feedback from the elderly. In this way, more appropriate notifications can be realized by providing notification methods based on feedback.

[0117] The notification unit can estimate the user's emotions and determine the priority of notification content based on the estimated user emotions. The notification unit uses AI to estimate the user's emotions and determine the priority of notification content based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The notification unit uses AI to estimate the user's emotions and determine the priority of notification content based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize notification of relaxing content. Also, if the user is relaxed, the notification unit can prioritize notification of detailed information. Furthermore, if the user is in a hurry, the notification unit can prioritize notification of important information. This allows for more appropriate notifications by providing a priority order of notification content according to the user's emotions.

[0118] The notification unit can select a notification method taking into consideration the geographical location information of the elderly person at the time of notification. The notification unit uses AI to select a notification method taking into consideration the geographical location information of the elderly person at the time of notification. Geographical location information includes, but is not limited to, for example, GPS data, address information, etc. The notification unit uses AI to select a notification method taking into consideration the geographical location information of the elderly person at the time of notification. For example, if the elderly person is at home, the notification can be made taking into consideration the environment around the home. Also, if the elderly person is out, the notification can be made taking into consideration the environment of the destination. Furthermore, if the elderly person is traveling, the notification can be made taking into consideration the environment of the destination. In this way, more appropriate notifications can be realized by providing a notification method based on geographical location information.

[0119] The notification unit can analyze the social media activity of the elderly person at the time of notification to improve the accuracy of the notification. The notification unit uses AI to analyze the social media activity of the elderly person at the time of notification to improve the accuracy of the notification. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The notification unit uses AI to analyze the social media activity of the elderly person at the time of notification to improve the accuracy of the notification. For example, the content of posts made by the elderly person on social media can be analyzed to notify the elderly person of their health status. Notifications can also be made based on the activities of the elderly person's friends on social media. Furthermore, the accuracy of notifications can be improved based on the elderly person's check-in information on social media. This provides a notification method based on social media activity, thereby realizing more appropriate notifications.

[0120] The notification unit can customize the notification method by reflecting the elderly person's past feedback at the time of notification. The notification unit uses AI to customize the notification method by reflecting the elderly person's past feedback at the time of notification. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The notification unit uses AI to customize the notification method by reflecting the elderly person's past feedback at the time of notification. For example, the notification unit can suggest an optimal notification method based on the elderly person's past feedback. It can also prioritize notification methods that the elderly person preferred in the past. Furthermore, it can improve the accuracy of notifications by reflecting the elderly person's past feedback. In this way, more appropriate notifications can be achieved by providing notification methods based on past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, observation unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives natural language input using the microphone 38B or touch panel 38A of the smart device 14. The generation unit generates a conversation using a generation AI by the specific processing unit 290 of the data processing device 12. The display unit displays the generated conversation using the display 40A of the smart device 14. The observation unit observes the user's health condition using the camera 42 of the smart device 14. The notification unit notifies close relatives of any abnormalities in the health condition by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, observation unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives natural language input using the microphone 238 of the smart glasses 214. The generation unit generates a conversation using a generation AI by the specific processing unit 290 of the data processing device 12. The display unit displays the generated conversation using the display of the smart glasses 214. The observation unit observes the user's health condition using the camera 42 of the smart glasses 214. The notification unit notifies close relatives of any abnormalities in the health condition by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, observation unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives input of natural language using the microphone 238 of the headset type terminal 314. The generation unit generates a conversation using a generation AI by the specific processing unit 290 of the data processing device 12. The display unit displays the generated conversation by using the display 343 of the headset type terminal 314. The observation unit observes the user's health condition using the camera 42 of the headset type terminal 314. The notification unit notifies close relatives of any abnormalities in the health condition by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, observation unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives input of natural language using the microphone 238 of the robot 414. The generation unit generates a conversation using a generation AI by the specific processing unit 290 of the data processing device 12. The display unit displays the generated conversation using the display of the robot 414. The observation unit observes the user's health condition using the camera 42 of the robot 414. The notification unit notifies close relatives of any abnormalities in the health condition by the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The simulated family generation system can further include a speech recognition unit. The speech recognition unit recognizes the elderly person's speech in real time and sends it to the generation unit. For example, when an elderly person says, "The weather is nice today," the speech recognition unit converts the content into text and sends it to the generation unit. The generation unit can generate an appropriate response based on the received text. The speech recognition unit can also analyze the elderly person's speech rate and volume and send it to the emotion estimation unit. This makes it possible to more accurately estimate the elderly person's emotions and provide an appropriate response. Furthermore, the speech recognition unit can improve recognition accuracy by learning the elderly person's speech patterns and understanding the characteristics of their speech.

[0123] The simulated family generation system can further include an image recognition unit. The image recognition unit analyzes the facial expressions and movements of the elderly person in real time and sends the results to the generation unit. For example, when the elderly person smiles, the image recognition unit analyzes the facial expression and sends it to the generation unit. The generation unit can generate an appropriate response based on the received information. The image recognition unit can also analyze the movements of the elderly person and use this information to estimate their health condition. This allows for a more accurate understanding of the elderly person's health condition and allows for appropriate responses. Furthermore, the image recognition unit can learn changes in the elderly person's facial expressions and movements and detect abnormalities early on.

[0124] The simulated family generation system can further include an environmental sensor unit. The environmental sensor unit monitors the elderly person's living environment in real time and transmits the information to the generation unit. For example, it collects information such as room temperature, humidity, and illuminance and transmits it to the generation unit. The generation unit can generate appropriate conversations based on the received information. For example, if the room temperature is high, it can generate a conversation such as, "It's hot today, isn't it?" The environmental sensor unit can also detect abnormal environmental changes and transmit the information to the notification unit. This allows for appropriate management of the elderly person's living environment and supports a comfortable life. Furthermore, the environmental sensor unit can learn the elderly person's living patterns and detect abnormalities early on.

[0125] The simulated family generation system can further include a learning unit. The learning unit learns the elderly person's past conversation history and behavioral patterns and provides feedback to the generation unit. For example, it learns topics that the elderly person has previously preferred and avoided and sends this to the generation unit. The generation unit can generate appropriate conversations based on the received information. The learning unit can also learn the elderly person's behavioral patterns and optimize the timing of reminders and notifications. This makes it possible to provide services that meet the individual needs of the elderly person. Furthermore, the learning unit can reflect the elderly person's feedback and improve the accuracy of the entire system.

[0126] The pseudo-family generation system can further include a voice synthesis unit. The voice synthesis unit reproduces the conversation generated by the generation unit in a natural voice. For example, if the generation unit generates a conversation such as "How was your day today?", the voice synthesis unit can reproduce the content in a natural voice. The voice synthesis unit can also adjust the tone and speed of the voice according to the elderly person's emotions. This makes the conversation more natural and friendly. Furthermore, the voice synthesis unit can customize the type of voice according to the elderly person's preferences. For example, various voices can be selected, such as a male voice, a female voice, a young voice, or an old voice.

[0127] The simulated family generation system may further include an emotion feedback unit. The emotion feedback unit monitors the emotions of the elderly in real time and provides feedback to the generation unit. For example, if the elderly smiles during a conversation, the emotion feedback unit sends that information to the generation unit. The generation unit can adjust the content and tone of the conversation based on the received information. The emotion feedback unit can also learn changes in the elderly's emotions and detect abnormalities early on. This allows appropriate responses to be taken according to the elderly's emotions. Furthermore, the emotion feedback unit can accumulate emotional data of the elderly and analyze long-term emotional trends.

[0128] The simulated family generation system can further include a health management unit. The health management unit collects health data of the elderly and provides feedback to the generation unit. For example, it collects data such as heart rate, blood pressure, and body temperature and sends it to the generation unit. The generation unit can generate appropriate conversations based on the received information. For example, if the heart rate is high, it can generate conversations such as "Let's take a short break." The health management unit can also detect abnormal health data and send it to the notification unit. This allows the elderly's health condition to be properly managed and abnormalities to be detected early. Furthermore, the health management unit can accumulate health data of the elderly and support long-term health management.

[0129] The pseudo-family generation system can further include a reminder unit. The reminder unit manages the schedules and tasks of the elderly and reminds them at appropriate times. For example, it can remind them to take their medicine, make hospital appointments, and complete daily tasks. The reminder unit can also learn the elderly's daily rhythm and provide optimal reminder timing. The reminder unit can also adjust the reminder method according to the elderly's emotions. For example, if the elderly is relaxed, it can remind them in a gentle tone. This helps the elderly remember to perform tasks and live their daily lives smoothly. Furthermore, the reminder unit can reflect the elderly's feedback to improve the accuracy of reminders.

[0130] The simulated family generation system can further include an entertainment unit. The entertainment unit provides entertainment content according to the elderly's hobbies and interests. For example, it can provide content such as music, movies, and games. The entertainment unit can also learn the elderly's past usage history and suggest optimal content. The entertainment unit can also adjust the type of content and the way it is provided according to the elderly's emotions. For example, if the elderly is relaxed, it can provide calm music. This allows the elderly to have a good time and maintain their mental health. Furthermore, the entertainment unit can reflect the elderly's feedback and improve the quality of the content.

[0131] The simulated family generation system may further include a communication unit. The communication unit supports communication between the elderly and their family and friends. For example, it may support video calls and sending and receiving messages. The communication unit may also adjust the communication method according to the elderly's emotions. For example, if the elderly feels lonely, it may suggest a video call with family or friends. The communication unit may also learn the elderly's past communication history and suggest the optimal communication method. This allows the elderly to live without feeling lonely. Furthermore, the communication unit may reflect the elderly's feedback and improve the quality of communication.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The reception unit accepts natural language input. Natural languages ​​include Japanese, English, and other languages. The reception unit can accept voice input and text input, estimate the user's emotions, and adjust the natural language input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Step 2: The generation unit uses a generation AI to generate a conversation based on the information received by the reception unit. Generation AIs include GPT-4 and Gemini. The generation unit generates conversations covering a variety of topics, from hobbies, travel, and food to health care and neighborhood gossip. For example, if the generation AI asks, "Have you traveled anywhere recently?" and the elderly person replies, "I went to a hot spring last week," the generation AI will follow up with, "What was the hot spring like?" Step 3: The display unit displays the conversation generated by the generation unit. The display unit displays the conversation generated on a mannequin-type monitor. The mannequin-type monitor includes a size, a display method, an interactive function, etc. Step 4: The observation unit observes the user's health condition through the conversation displayed on the display unit. The health condition includes heart rate, blood pressure, and facial expression analysis. The observation unit observes the user's health condition from the conversation and video and detects abnormalities early. Step 5: The notification unit notifies the patient of any abnormal health condition observed by the observation unit. The notification unit notifies the patient's next of kin of the abnormal health condition. The next of kin includes family members, relatives, etc.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input in natural language; a generation unit that generates a conversation based on the information received by the reception unit; a display unit that displays the conversation generated by the generation unit; an observation unit that observes the health condition of the user through the conversation displayed by the display unit; a notification unit that notifies the user of an abnormality in the health condition observed by the observation unit. A system characterized by:

2. The generation unit Generate conversations using generative AI 2. The system of claim 1.

3. The generation unit Generative AI generates conversations covering multiple topics, from hobbies, travel, and food to health care and neighborhood gossip.

2. The system of claim 1.

4. The display unit Display the generated conversation on a mannequin monitor 2. The system of claim 1.

5. The observation unit is Observing the user's health status through conversation or video 2. The system of claim 1.

6. The notification unit Notify next of kin of any abnormal health condition 2. The system of claim 1.

7. The generation unit Generate conversations to provide information and reminders necessary for daily life 2. The system of claim 1.

8. The reception unit Estimate user emotions and adjust natural language input methods based on the estimated user emotions 2. The system of claim 1.

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