system

Abstract

The system according to this embodiment aims to provide companionship and health care to people living in sparsely populated areas. [Solution] The system according to the embodiment comprises a personality formation unit, a keyword extraction unit, an information transmission unit, a health monitoring unit, and a care provision unit. The personality formation unit converses with the user and forms a personality through conversation with the user. The keyword extraction unit extracts keywords from the conversation based on the personality formed by the personality formation unit. The information transmission unit conveys information based on the keywords extracted by the keyword extraction unit. The health monitoring unit monitors the user's health status. The care provision unit provides care and information based on the data collected by the health monitoring unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for people living in depopulated areas to receive appropriate conversation partners or healthcare.

[0005] The system according to the embodiment aims to provide conversation partners and healthcare for people living in depopulated areas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a personality formation unit, a keyword extraction unit, an information transmission unit, a health monitoring unit, and a care provision unit. The personality formation unit engages in conversation with the user and forms a personality through this conversation. The keyword extraction unit extracts keywords from the conversation based on the personality formed by the personality formation unit. The information transmission unit conveys information based on the keywords extracted by the keyword extraction unit. The health monitoring unit monitors the user's health status. The care provision unit provides care and information based on the data collected by the health monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide companionship and health care to people living in sparsely populated areas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The support system according to an embodiment of the present invention is a system that utilizes generative AI to support people in need of care living in sparsely populated areas. This support system provides a partner using generative AI and functions as a concierge that provides "health, necessary care, and information" as well as a "conversation partner." First, the generative AI converses with the user and automatically creates a personality that is easy for the user to talk to. This allows the user to enjoy conversations naturally and increases the amount of conversation. Next, the generative AI extracts keywords from the conversation and conveys the information to family members or caregivers living in distant locations as needed. Furthermore, the generative AI monitors the user's health condition and provides necessary care and information. Through this system, people in need of care living in sparsely populated areas can efficiently coordinate with family members or caregivers living in distant locations and live with peace of mind.

[0029] The support system according to this embodiment comprises a personality formation unit, a keyword extraction unit, an information transmission unit, a health monitoring unit, and a care provision unit. The personality formation unit converses with the user and forms a personality through conversation with the user. The personality formation unit provides topics based on the user's hobbies and interests, for example, using a generative AI. The generative AI can, for example, analyze the user's past conversation history and select the most appropriate topics and tone. The keyword extraction unit extracts keywords from the conversation based on the personality formed by the personality formation unit. The keyword extraction unit analyzes the context of the conversation using a generative AI and prioritizes extracting highly relevant keywords. The information transmission unit conveys information based on the keywords extracted by the keyword extraction unit. The information transmission unit conveys information to family members or caregivers living in remote locations based on the extracted keywords. The health monitoring unit monitors the user's health status. The health monitoring unit constantly monitors the user's health status using a generative AI and provides necessary care promptly. The care provision unit provides care and information based on the data collected by the health monitoring unit. The care provision unit, for example, provides users with optimal care and information based on data collected using generative AI. This allows the support system according to the embodiment to efficiently provide necessary information and care through user conversations.

[0030] The personality-building section can provide topics based on the user's hobbies and interests using generative AI. For example, the generative AI can provide topics based on the user's hobbies and interests. For example, the generative AI can provide topics that the user has talked about in the past. For example, the generative AI can provide information related to topics that the user is interested in. For example, the generative AI can provide the latest information on the user's favorite hobbies. As a result, by providing topics based on the user's hobbies and interests, conversations become more natural and enjoyable.

[0031] The keyword extraction unit can extract keywords from a conversation using a generative AI. For example, the generative AI can extract important keywords from the conversation. For example, the generative AI can analyze the context of the conversation and prioritize the extraction of highly relevant keywords. For example, the generative AI can extract words that appear frequently in the conversation. For example, the generative AI can extract keywords related to the topic of the conversation. This allows for the efficient transmission of necessary information by extracting important keywords from the conversation.

[0032] The information transmission unit can transmit information to family members and caregivers living in remote locations based on extracted keywords. For example, the information transmission unit can transmit information using text messages, voice messages, or video calls. This allows for the rapid transmission of necessary information to family members and caregivers living in distant locations.

[0033] The health monitoring unit can monitor the user's health status using generative AI. For example, the generative AI can continuously monitor the user's health status. For example, the generative AI can measure vital signs. For example, the generative AI can analyze behavioral patterns. For example, the generative AI can monitor the user's health status using sensors. This allows for the rapid provision of necessary care by continuously monitoring the user's health status.

[0034] The care delivery unit can provide care and information based on data collected by the generative AI. The generative AI can, for example, provide optimal care and information to the user based on the collected data. For example, the generative AI can provide medical advice. For example, the generative AI can provide lifestyle guidance. For example, the generative AI can provide emergency response. This allows the system to provide optimal care and information to the user based on the collected data.

[0035] The personality development unit can analyze the user's past conversation history and select topics and tones. The generative AI, for example, analyzes the user's past conversation history and selects the most suitable topics and tones. The generative AI can, for example, reintroduce topics that the user has frequently discussed in the past. The generative AI can, for example, speak in a similar tone to those the user has preferred in the past. The generative AI can, for example, avoid topics that the user has avoided in the past. As a result, by selecting the most suitable topics and tones based on past conversation history, conversations become smoother.

[0036] The personality development unit can customize language and expressions based on the user's living environment and cultural background. For example, the generative AI customizes appropriate language and expressions based on the user's living environment and cultural background. For example, if the user lives in a specific region, the generative AI can incorporate the dialect and language of that region. For example, if the generative AI has a specific cultural background, it can provide expressions and topics appropriate to that culture. For example, the generative AI can select appropriate topics and language according to the user's living environment. As a result, by providing language and expressions that are tailored to the user's living environment and cultural background, conversations become more approachable.

[0037] The personality-building unit can provide region-specific topics by considering the user's geographical location. For example, if the user lives in a particular region, the generating AI can discuss events and news from that region. If the user is traveling, the generating AI can provide tourist information and local topics from their destination. If the user is interested in a particular region, the generating AI can provide topics related to that region. By providing topics based on the user's geographical location, the conversation becomes more engaging.

[0038] The personality development unit can analyze a user's social media activity and provide relevant topics. For example, the generative AI can analyze a user's social media activity and provide relevant topics. For example, the generative AI can provide topics based on topics that a user frequently posts about on social media. For example, the generative AI can provide relevant topics based on the content of accounts that a user follows on social media. For example, the generative AI can provide topics based on topics that a user participates in on social media. This makes conversations more engaging by providing topics based on the user's social media activity.

[0039] The keyword extraction unit can analyze the context of the conversation and prioritize the extraction of highly relevant keywords. The generation AI, for example, can analyze the context of the conversation and prioritize the extraction of highly relevant keywords. The generation AI can, for example, prioritize the extraction of words that appear frequently in the conversation. The generation AI can, for example, prioritize the extraction of keywords related to the topic of the conversation. The generation AI can, for example, extract important keywords along the flow of the conversation. As a result, by extracting keywords based on the context of the conversation, more appropriate information can be provided.

[0040] The keyword extraction unit can extract frequently occurring keywords by referring to the user's past conversation history. The generating AI, for example, can extract frequently occurring keywords by referring to the user's past conversation history. The generating AI can, for example, extract words that the user has used frequently in the past. The generating AI can, for example, extract keywords related to topics the user has talked about in the past. The generating AI can, for example, extract important keywords from the user's past conversation history. This allows for the provision of more appropriate information by extracting keywords based on past conversation history.

[0041] The keyword extraction unit can extract region-specific keywords by considering the user's geographical location. The generating AI, for example, extracts region-specific keywords by considering the user's geographical location. For example, if the user lives in a specific region, the generating AI can extract keywords related to that region. For example, if the user is traveling, the generating AI can extract keywords related to the travel destination. For example, if the user is interested in a specific region, the generating AI can extract keywords related to that region. This allows for the provision of more appropriate information by extracting keywords based on the user's geographical location.

[0042] The keyword extraction unit can analyze a user's social media activity and extract relevant keywords. The generation AI, for example, can analyze a user's social media activity and extract relevant keywords. The generation AI can extract keywords related to topics that a user frequently posts about on social media. The generation AI can extract keywords related to the content of accounts that a user follows on social media. The generation AI can extract keywords related to groups and communities that a user participates in on social media. By extracting keywords based on the user's social media activity, more appropriate information can be provided.

[0043] The information transmission unit can select the optimal transmission method when transmitting information, taking into account the recipient's attribute information. For example, the generating AI selects the optimal transmission method when transmitting information, taking into account the recipient's attribute information. For example, if the recipient is elderly, the generating AI can select a visually easy-to-understand transmission method. For example, if the recipient is young, the generating AI can select a transmission method that utilizes digital media. For example, if the recipient is busy, the generating AI can select a transmission method that conveys the main points in a short amount of time. As a result, information transmission becomes more effective by selecting the optimal transmission method based on the recipient's attribute information.

[0044] The information transmission unit can select the timing of information transmission by referring to past transmission history. The generating AI, for example, selects the optimal timing of information transmission by referring to past transmission history. The generating AI can select the optimal transmission timing based on the time periods when the recipient previously received information. The generating AI can transmit information based on the timing preferred by the recipient in the past. The generating AI can select the optimal timing based on the recipient's past responses. As a result, information reception becomes more effective by transmitting information at the optimal timing based on past transmission history.

[0045] The information transmission unit can select the optimal transmission method when transmitting information, taking into account the recipient's geographical location. The generating AI, for example, selects the optimal transmission method when transmitting information, taking into account the recipient's geographical location. For example, if the recipient lives in a specific region, the generating AI can select a transmission method suitable for that region. For example, if the recipient is traveling, the generating AI can select a transmission method suitable for their travel destination. For example, if the recipient is interested in a specific region, the generating AI can transmit information related to that region. By selecting the optimal transmission method based on the recipient's geographical location, information transmission becomes more effective.

[0046] The information transmission unit can analyze the recipient's social media activity and transmit relevant information during information transmission. The generating AI, for example, can analyze the recipient's social media activity and transmit relevant information during information transmission. For example, the generating AI can transmit information related to topics the recipient frequently posts about on social media. For example, the generating AI can transmit information related to the content of accounts the recipient follows on social media. For example, the generating AI can transmit information related to groups and communities the recipient participates in on social media. This makes information reception more effective by transmitting information based on the recipient's social media activity.

[0047] The health monitoring unit can detect abnormal values ​​early by referring to the user's past health data. The generating AI can, for example, refer to the user's past health data to detect abnormal values ​​early. The generating AI can, for example, refer to the user's past blood pressure data to detect abnormal fluctuations. The generating AI can, for example, refer to the user's past heart rate data to detect abnormal patterns. The generating AI can, for example, refer to the user's past body temperature data to detect abnormal increases or decreases. This enables a rapid response by early detection of abnormal values ​​based on past health data.

[0048] The health monitoring unit can customize monitoring items based on the user's lifestyle and environment. For example, the generating AI can customize monitoring items based on the user's lifestyle and environment. For instance, if the user exercises frequently, the generating AI can focus on monitoring exercise volume and heart rate. For example, if the user is on a specific diet, the generating AI can monitor their diet. For example, if the user lives in a particular environment, the generating AI can monitor health risks associated with that environment. This allows for more appropriate health management by customizing monitoring items according to the user's lifestyle and environment.

[0049] The health monitoring unit can monitor region-specific health risks by considering the user's geographical location. The generating AI, for example, monitors region-specific health risks by considering the user's geographical location. For example, if the user lives in a specific region, the generating AI can monitor health risks associated with that region. For example, if the user is traveling, the generating AI can monitor health risks associated with the travel destination. For example, if the user is interested in a specific region, the generating AI can monitor health risks associated with that region. This enables more appropriate health management through monitoring health risks based on the user's geographical location.

[0050] The health monitoring unit can analyze users' social media activity and monitor relevant health information. For example, the generating AI can analyze users' social media activity and monitor relevant health information. For example, the generating AI can monitor health-related topics that users frequently post on social media. For example, the generating AI can monitor the content of health-related accounts that users follow on social media. For example, the generating AI can monitor topics in health-related groups and communities that users participate in on social media. This enables more appropriate health management by monitoring health information based on users' social media activity.

[0051] The care provider can provide the optimal care plan by referring to the user's past care history. The generating AI can, for example, refer to the user's past care history to provide the optimal care plan. The generating AI can, for example, provide the optimal care plan based on the care the user has received in the past. The generating AI can, for example, select effective care methods from the user's past care history. The generating AI can, for example, analyze the user's past care history and provide the necessary care. This enables more effective care by providing the optimal care plan based on past care history.

[0052] The care delivery unit can customize appropriate care methods based on the user's living environment and cultural background. For example, the generating AI customizes appropriate care methods based on the user's living environment and cultural background. For example, if the user lives in a specific region, the generating AI can provide care methods suitable for that region. For example, if the user has a specific cultural background, the generating AI can provide care methods suitable for that culture. For example, the generating AI can select appropriate care methods according to the user's living environment. This allows for the provision of more appropriate care by customizing care methods according to the user's living environment and cultural background.

[0053] The care provider can offer region-specific care methods by considering the user's geographical location. For example, the generating AI can offer region-specific care methods by considering the user's geographical location. For example, if the user lives in a specific region, the generating AI can offer care methods suitable for that region. For example, if the user is traveling, the generating AI can offer care methods suitable for their travel destination. For example, if the user is interested in a specific region, the generating AI can offer care methods related to that region. This allows for more appropriate care to be provided by offering care methods based on the user's geographical location.

[0054] The care provision unit can analyze a user's social media activity and provide relevant care information. The generating AI, for example, can analyze a user's social media activity and provide relevant care information. The generating AI can, for example, provide care information based on health-related topics that a user frequently posts about on social media. The generating AI can, for example, provide care information based on the content of health-related accounts that a user follows on social media. The generating AI can, for example, provide care information based on topics in health-related groups and communities that a user participates in on social media. This allows for the provision of more appropriate care by providing care information based on the user's social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The support system can further analyze the user's daily routine and provide care and information at the optimal time. For example, if a user wakes up at the same time every morning, the system can perform health checks at that time. Also, if a user tends to relax during certain times, the system can provide relaxing content during those times. Furthermore, if a user often feels anxious at night, the system can send reassuring messages at night. This enables care and information provision tailored to the user's daily rhythm, resulting in more effective support.

[0057] The support system can further monitor the user's diet and provide dietary advice tailored to their health condition. For example, if a user has high blood pressure, it can suggest a low-sodium diet. If a user has diabetes, it can suggest a low-carbohydrate diet. Furthermore, if a user is nutritionally deficient, it can suggest a balanced diet. This enables dietary advice tailored to the user's health condition, supporting a healthier lifestyle.

[0058] The support system can further monitor the user's exercise habits and provide appropriate exercise plans. For example, if a user is not getting enough exercise, it can suggest simple stretches or walking. If a user is exercising excessively, it can suggest adequate rest. Furthermore, if a user prefers a particular exercise, it can provide a plan that incorporates that exercise. This ensures that users receive appropriate exercise plans tailored to their exercise habits, supporting their health maintenance.

[0059] The support system can further provide relevant event information based on the user's hobbies and interests. For example, if a user is interested in music, it can provide information on nearby concerts. If a user is interested in sports, it can provide information on local sporting events. Furthermore, if a user is interested in art, it can provide information on exhibitions and workshops. This provides event information tailored to the user's hobbies and interests, improving their quality of life.

[0060] The support system can further utilize the user's geographical location information to provide information on region-specific health risks. For example, if a user lives in an area with a high risk of hay fever, it can provide pollen information. Similarly, if a user lives in an area with a high risk of heatstroke, it can provide information on heatstroke prevention. Furthermore, if a user lives in an area with a high risk of infectious diseases, it can provide information on infectious disease prevention. This provides information on region-specific health risks, improving the user's health management.

[0061] The support system can further analyze users' social media activity and provide relevant health and care information. For example, if a user frequently posts about health on social media, it can provide health advice based on that content. If a user follows specific health-related accounts, it can provide updates from those accounts. Furthermore, if a user participates in health-related groups or communities, it can provide information about those groups' activities. This allows for more appropriate support by providing health and care information based on the user's social media activity.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The personality formation unit engages in conversation with the user and forms a personality through these conversations. For example, it uses generative AI to provide topics based on the user's hobbies and interests, and analyzes past conversation history to select the most suitable topics and tone. Step 2: The keyword extraction unit extracts keywords from the conversation based on the personality formed by the personality formation unit. For example, it uses a generative AI to analyze the context of the conversation and prioritizes extracting keywords that are highly relevant. Step 3: The information transmission unit transmits information based on the keywords extracted by the keyword extraction unit. For example, it transmits information to family members or caregivers living in remote locations based on the extracted keywords. Step 4: The health monitoring unit monitors the user's health status. For example, it uses generative AI to constantly monitor the user's health status and quickly provide necessary care. Step 5: The care delivery department provides care and information based on the data collected by the health monitoring department. For example, it provides optimal care and information to the user based on the data collected using generative AI.

[0064] (Example of form 2) The support system according to an embodiment of the present invention is a system that utilizes generative AI to support people in need of care living in sparsely populated areas. This support system provides a partner using generative AI and functions as a concierge that provides "health, necessary care, and information" as well as a "conversation partner." First, the generative AI converses with the user and automatically creates a personality that is easy for the user to talk to. This allows the user to enjoy conversations naturally and increases the amount of conversation. Next, the generative AI extracts keywords from the conversation and conveys the information to family members or caregivers living in distant locations as needed. Furthermore, the generative AI monitors the user's health condition and provides necessary care and information. Through this system, people in need of care living in sparsely populated areas can efficiently coordinate with family members or caregivers living in distant locations and live with peace of mind.

[0065] The support system according to this embodiment comprises a personality formation unit, a keyword extraction unit, an information transmission unit, a health monitoring unit, and a care provision unit. The personality formation unit converses with the user and forms a personality through conversation with the user. The personality formation unit provides topics based on the user's hobbies and interests, for example, using a generative AI. The generative AI can, for example, analyze the user's past conversation history and select the most appropriate topics and tone. The keyword extraction unit extracts keywords from the conversation based on the personality formed by the personality formation unit. The keyword extraction unit analyzes the context of the conversation using a generative AI and prioritizes extracting highly relevant keywords. The information transmission unit conveys information based on the keywords extracted by the keyword extraction unit. The information transmission unit conveys information to family members or caregivers living in remote locations based on the extracted keywords. The health monitoring unit monitors the user's health status. The health monitoring unit constantly monitors the user's health status using a generative AI and provides necessary care promptly. The care provision unit provides care and information based on the data collected by the health monitoring unit. The care provision unit, for example, provides users with optimal care and information based on data collected using generative AI. This allows the support system according to the embodiment to efficiently provide necessary information and care through user conversations.

[0066] The personality-building section can provide topics based on the user's hobbies and interests using generative AI. For example, the generative AI can provide topics based on the user's hobbies and interests. For example, the generative AI can provide topics that the user has talked about in the past. For example, the generative AI can provide information related to topics that the user is interested in. For example, the generative AI can provide the latest information on the user's favorite hobbies. As a result, by providing topics based on the user's hobbies and interests, conversations become more natural and enjoyable.

[0067] The keyword extraction unit can extract keywords from a conversation using a generative AI. For example, the generative AI can extract important keywords from the conversation. For example, the generative AI can analyze the context of the conversation and prioritize the extraction of highly relevant keywords. For example, the generative AI can extract words that appear frequently in the conversation. For example, the generative AI can extract keywords related to the topic of the conversation. This allows for the efficient transmission of necessary information by extracting important keywords from the conversation.

[0068] The information transmission unit can transmit information to family members and caregivers living in remote locations based on extracted keywords. For example, the information transmission unit can transmit information using text messages, voice messages, or video calls. This allows for the rapid transmission of necessary information to family members and caregivers living in distant locations.

[0069] The health monitoring unit can monitor the user's health status using generative AI. For example, the generative AI can continuously monitor the user's health status. For example, the generative AI can measure vital signs. For example, the generative AI can analyze behavioral patterns. For example, the generative AI can monitor the user's health status using sensors. This allows for the rapid provision of necessary care by continuously monitoring the user's health status.

[0070] The care delivery unit can provide care and information based on data collected by the generative AI. The generative AI can, for example, provide optimal care and information to the user based on the collected data. For example, the generative AI can provide medical advice. For example, the generative AI can provide lifestyle guidance. For example, the generative AI can provide emergency response. This allows the system to provide optimal care and information to the user based on the collected data.

[0071] The personality formation unit can estimate the user's emotions and dynamically adjust the personality to be more approachable based on those emotions. For example, the generating AI can estimate the user's emotions and dynamically adjust the personality to be more approachable based on those emotions. For example, if the user is stressed, the generating AI can form a personality that speaks in a calm, relaxing tone. For example, if the user is excited, the generating AI can form a personality that speaks in an energetic tone that shares and empathizes with the excitement. For example, if the user is sad, the generating AI can form a personality that speaks in a comforting, gentle tone. This makes conversations more natural and comfortable by forming a personality that is more approachable according to the user's emotions.

[0072] The personality development unit can analyze the user's past conversation history and select topics and tones. The generative AI, for example, analyzes the user's past conversation history and selects the most suitable topics and tones. The generative AI can, for example, reintroduce topics that the user has frequently discussed in the past. The generative AI can, for example, speak in a similar tone to those the user has preferred in the past. The generative AI can, for example, avoid topics that the user has avoided in the past. As a result, by selecting the most suitable topics and tones based on past conversation history, conversations become smoother.

[0073] The personality development unit can customize language and expressions based on the user's living environment and cultural background. For example, the generative AI customizes appropriate language and expressions based on the user's living environment and cultural background. For example, if the user lives in a specific region, the generative AI can incorporate the dialect and language of that region. For example, if the generative AI has a specific cultural background, it can provide expressions and topics appropriate to that culture. For example, the generative AI can select appropriate topics and language according to the user's living environment. As a result, by providing language and expressions that are tailored to the user's living environment and cultural background, conversations become more approachable.

[0074] The personality-forming unit can estimate the user's emotions and adjust the timing of the conversation start based on those emotions. For example, the generative AI can estimate the user's emotions and adjust the timing of the conversation start based on those emotions. For example, if the user is relaxed, the generative AI can start the conversation slowly. For example, if the user is in a hurry, the generative AI can start the conversation quickly. For example, if the user is excited, the generative AI can start the conversation at a time that shares that excitement. This makes the conversation more natural and comfortable by starting it at a time that matches the user's emotions.

[0075] The personality-building unit can provide region-specific topics by considering the user's geographical location. For example, if the user lives in a particular region, the generating AI can discuss events and news from that region. If the user is traveling, the generating AI can provide tourist information and local topics from their destination. If the user is interested in a particular region, the generating AI can provide topics related to that region. By providing topics based on the user's geographical location, the conversation becomes more engaging.

[0076] The personality development unit can analyze a user's social media activity and provide relevant topics. For example, the generative AI can analyze a user's social media activity and provide relevant topics. For example, the generative AI can provide topics based on topics that a user frequently posts about on social media. For example, the generative AI can provide relevant topics based on the content of accounts that a user follows on social media. For example, the generative AI can provide topics based on topics that a user participates in on social media. This makes conversations more engaging by providing topics based on the user's social media activity.

[0077] The keyword extraction unit can estimate the user's emotions and adjust the importance of keywords based on those emotions. The generating AI, for example, estimates the user's emotions and adjusts the importance of keywords based on those emotions. For example, if the user is excited, the generating AI can prioritize extracting keywords related to excitement. For example, if the user is relaxed, the generating AI can prioritize extracting keywords related to relaxation. For example, if the user is stressed, the generating AI can prioritize extracting keywords related to stress. By adjusting the importance of keywords according to the user's emotions, more appropriate information can be extracted.

[0078] The keyword extraction unit can analyze the context of the conversation and prioritize the extraction of highly relevant keywords. The generation AI, for example, can analyze the context of the conversation and prioritize the extraction of highly relevant keywords. The generation AI can, for example, prioritize the extraction of words that appear frequently in the conversation. The generation AI can, for example, prioritize the extraction of keywords related to the topic of the conversation. The generation AI can, for example, extract important keywords along the flow of the conversation. As a result, by extracting keywords based on the context of the conversation, more appropriate information can be provided.

[0079] The keyword extraction unit can extract frequently occurring keywords by referring to the user's past conversation history. The generating AI, for example, can extract frequently occurring keywords by referring to the user's past conversation history. The generating AI can, for example, extract words that the user has used frequently in the past. The generating AI can, for example, extract keywords related to topics the user has talked about in the past. The generating AI can, for example, extract important keywords from the user's past conversation history. This allows for the provision of more appropriate information by extracting keywords based on past conversation history.

[0080] The keyword extraction unit can estimate the user's emotions and adjust the frequency of keyword extraction based on the estimated emotions. The generating AI, for example, estimates the user's emotions and adjusts the frequency of keyword extraction based on the estimated emotions. For example, if the user is excited, the generating AI can increase the frequency of keywords related to excitement. For example, if the user is relaxed, the generating AI can increase the frequency of keywords related to relaxation. For example, if the user is stressed, the generating AI can increase the frequency of keywords related to stress. By adjusting the frequency of keyword extraction according to the user's emotions, more appropriate information can be provided.

[0081] The keyword extraction unit can extract region-specific keywords by considering the user's geographical location. The generating AI, for example, extracts region-specific keywords by considering the user's geographical location. For example, if the user lives in a specific region, the generating AI can extract keywords related to that region. For example, if the user is traveling, the generating AI can extract keywords related to the travel destination. For example, if the user is interested in a specific region, the generating AI can extract keywords related to that region. This allows for the provision of more appropriate information by extracting keywords based on the user's geographical location.

[0082] The keyword extraction unit can analyze a user's social media activity and extract relevant keywords. The generation AI, for example, can analyze a user's social media activity and extract relevant keywords. The generation AI can extract keywords related to topics that a user frequently posts about on social media. The generation AI can extract keywords related to the content of accounts that a user follows on social media. The generation AI can extract keywords related to groups and communities that a user participates in on social media. By extracting keywords based on the user's social media activity, more appropriate information can be provided.

[0083] The information transmission unit can estimate the user's emotions and adjust the method of information transmission based on the estimated emotions. For example, the generative AI can estimate the user's emotions and adjust the method of information transmission based on the estimated emotions. For example, if the user is tense, the generative AI can transmit information in a calm tone. For example, if the user is relaxed, the generative AI can select a transmission method that includes detailed information. For example, if the user is in a hurry, the generative AI can select a fast transmission method that gets straight to the point. In this way, by adjusting the method of information transmission according to the user's emotions, more appropriate information can be provided.

[0084] The information transmission unit can select the optimal transmission method when transmitting information, taking into account the recipient's attribute information. For example, the generating AI selects the optimal transmission method when transmitting information, taking into account the recipient's attribute information. For example, if the recipient is elderly, the generating AI can select a visually easy-to-understand transmission method. For example, if the recipient is young, the generating AI can select a transmission method that utilizes digital media. For example, if the recipient is busy, the generating AI can select a transmission method that conveys the main points in a short amount of time. As a result, information transmission becomes more effective by selecting the optimal transmission method based on the recipient's attribute information.

[0085] The information transmission unit can select the timing of information transmission by referring to past transmission history. The generating AI, for example, selects the optimal timing of information transmission by referring to past transmission history. The generating AI can select the optimal transmission timing based on the time periods when the recipient previously received information. The generating AI can transmit information based on the timing preferred by the recipient in the past. The generating AI can select the optimal timing based on the recipient's past responses. As a result, information reception becomes more effective by transmitting information at the optimal timing based on past transmission history.

[0086] The information transmission unit can estimate the user's emotions and determine the priority of information based on those emotions. For example, the generative AI can estimate the user's emotions and determine the priority of information based on those emotions. For example, if the user is stressed, the generative AI can prioritize the transmission of important information. For example, if the user is relaxed, the generative AI can prioritize the transmission of detailed information. For example, if the user is in a hurry, the generative AI can prioritize the transmission of concise information. This allows for the prioritization of more important information based on the user's emotions.

[0087] The information transmission unit can select the optimal transmission method when transmitting information, taking into account the recipient's geographical location. The generating AI, for example, selects the optimal transmission method when transmitting information, taking into account the recipient's geographical location. For example, if the recipient lives in a specific region, the generating AI can select a transmission method suitable for that region. For example, if the recipient is traveling, the generating AI can select a transmission method suitable for their travel destination. For example, if the recipient is interested in a specific region, the generating AI can transmit information related to that region. By selecting the optimal transmission method based on the recipient's geographical location, information transmission becomes more effective.

[0088] The information transmission unit can analyze the recipient's social media activity and transmit relevant information during information transmission. The generating AI, for example, can analyze the recipient's social media activity and transmit relevant information during information transmission. For example, the generating AI can transmit information related to topics the recipient frequently posts about on social media. For example, the generating AI can transmit information related to the content of accounts the recipient follows on social media. For example, the generating AI can transmit information related to groups and communities the recipient participates in on social media. This makes information reception more effective by transmitting information based on the recipient's social media activity.

[0089] The health monitoring unit can estimate the user's emotions and adjust the frequency of health monitoring based on the estimated emotions. For example, the generating AI can estimate the user's emotions and adjust the frequency of health monitoring based on those emotions. For example, the generating AI can increase the monitoring frequency if the user is stressed. For example, the generating AI can decrease the monitoring frequency if the user is relaxed. For example, the generating AI can appropriately adjust the monitoring frequency if the user is excited. This allows for more appropriate health management by adjusting the monitoring frequency according to the user's emotions.

[0090] The health monitoring unit can detect abnormal values ​​early by referring to the user's past health data. The generating AI can, for example, refer to the user's past health data to detect abnormal values ​​early. The generating AI can, for example, refer to the user's past blood pressure data to detect abnormal fluctuations. The generating AI can, for example, refer to the user's past heart rate data to detect abnormal patterns. The generating AI can, for example, refer to the user's past body temperature data to detect abnormal increases or decreases. This enables a rapid response by early detection of abnormal values ​​based on past health data.

[0091] The health monitoring unit can customize monitoring items based on the user's lifestyle and environment. For example, the generating AI can customize monitoring items based on the user's lifestyle and environment. For instance, if the user exercises frequently, the generating AI can focus on monitoring exercise volume and heart rate. For example, if the user is on a specific diet, the generating AI can monitor their diet. For example, if the user lives in a particular environment, the generating AI can monitor health risks associated with that environment. This allows for more appropriate health management by customizing monitoring items according to the user's lifestyle and environment.

[0092] The health monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, the generating AI can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if the user is stressed, the generating AI can provide a simple and easy-to-read display method. For example, if the user is relaxed, the generating AI can provide a display method that includes detailed information. For example, if the user is in a hurry, the generating AI can provide a display method that gets straight to the point. This makes it easier to understand the monitoring results by adjusting the display method according to the user's emotions.

[0093] The health monitoring unit can monitor region-specific health risks by considering the user's geographical location. The generating AI, for example, monitors region-specific health risks by considering the user's geographical location. For example, if the user lives in a specific region, the generating AI can monitor health risks associated with that region. For example, if the user is traveling, the generating AI can monitor health risks associated with the travel destination. For example, if the user is interested in a specific region, the generating AI can monitor health risks associated with that region. This enables more appropriate health management through monitoring health risks based on the user's geographical location.

[0094] The health monitoring unit can analyze users' social media activity and monitor relevant health information. For example, the generating AI can analyze users' social media activity and monitor relevant health information. For example, the generating AI can monitor health-related topics that users frequently post on social media. For example, the generating AI can monitor the content of health-related accounts that users follow on social media. For example, the generating AI can monitor topics in health-related groups and communities that users participate in on social media. This enables more appropriate health management by monitoring health information based on users' social media activity.

[0095] The care delivery unit can estimate the user's emotions and adjust the care delivery method based on the estimated emotions. The generative AI, for example, estimates the user's emotions and adjusts the care delivery method based on the estimated emotions. For example, if the user is tense, the generative AI can provide care in a calm tone. For example, if the user is relaxed, the generative AI can provide detailed care information. For example, if the user is in a hurry, the generative AI can provide concise and quick care. This allows for the provision of more appropriate care by adjusting the care delivery method according to the user's emotions.

[0096] The care provider can provide the optimal care plan by referring to the user's past care history. The generating AI can, for example, refer to the user's past care history to provide the optimal care plan. The generating AI can, for example, provide the optimal care plan based on the care the user has received in the past. The generating AI can, for example, select effective care methods from the user's past care history. The generating AI can, for example, analyze the user's past care history and provide the necessary care. This enables more effective care by providing the optimal care plan based on past care history.

[0097] The care delivery unit can customize appropriate care methods based on the user's living environment and cultural background. For example, the generating AI customizes appropriate care methods based on the user's living environment and cultural background. For example, if the user lives in a specific region, the generating AI can provide care methods suitable for that region. For example, if the user has a specific cultural background, the generating AI can provide care methods suitable for that culture. For example, the generating AI can select appropriate care methods according to the user's living environment. This allows for the provision of more appropriate care by customizing care methods according to the user's living environment and cultural background.

[0098] The care delivery unit can estimate the user's emotions and determine the priority of care based on those emotions. The generative AI, for example, estimates the user's emotions and determines the priority of care based on those emotions. For example, if the user is stressed, the generative AI can prioritize providing important care. For example, if the user is relaxed, the generative AI can prioritize providing detailed care. For example, if the user is in a hurry, the generative AI can prioritize providing concise care. In this way, by determining the priority of care according to the user's emotions, more important care can be prioritized.

[0099] The care provider can offer region-specific care methods by considering the user's geographical location. For example, the generating AI can offer region-specific care methods by considering the user's geographical location. For example, if the user lives in a specific region, the generating AI can offer care methods suitable for that region. For example, if the user is traveling, the generating AI can offer care methods suitable for their travel destination. For example, if the user is interested in a specific region, the generating AI can offer care methods related to that region. This allows for more appropriate care to be provided by offering care methods based on the user's geographical location.

[0100] The care provision unit can analyze a user's social media activity and provide relevant care information. The generating AI, for example, can analyze a user's social media activity and provide relevant care information. The generating AI can, for example, provide care information based on health-related topics that a user frequently posts about on social media. The generating AI can, for example, provide care information based on the content of health-related accounts that a user follows on social media. The generating AI can, for example, provide care information based on topics in health-related groups and communities that a user participates in on social media. This allows for the provision of more appropriate care by providing care information based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the personality formation unit, keyword extraction unit, information transmission unit, health monitoring unit, and care provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the personality formation unit is implemented by the control unit 46A of the smart device 14 and forms a personality through conversation with the user. The keyword extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts keywords from the conversation. The information transmission unit is implemented by the identification processing unit 290 of the data processing unit 12 and conveys information based on the extracted keywords. The health monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the user's health status. The care provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides care and information based on the collected data. === Hard Collateral 1-2 === Each of the multiple elements described above, including the personality formation unit, keyword extraction unit, information transmission unit, health monitoring unit, and care provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the personality formation unit is implemented by the control unit 46A of the smart glasses 214 and forms a personality through conversation with the user. The keyword extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts keywords from the conversation. The information transmission unit is implemented by the identification processing unit 290 of the data processing unit 12 and conveys information based on the extracted keywords. The health monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's health status. The care provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides care and information based on the collected data. === Hard Collateral 1-3 === Each of the multiple elements described above, including the personality formation unit, keyword extraction unit, information transmission unit, health monitoring unit, and care provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the personality formation unit is implemented by the control unit 46A of the headset terminal 314 and forms a personality through conversation with the user. The keyword extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts keywords from the conversation. The information transmission unit is implemented by the identification processing unit 290 of the data processing unit 12 and transmits information based on the extracted keywords. The health monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the user's health status. The care provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides care and information based on the collected data. === Hard Collateral 1-4 === Each of the multiple elements described above, including the personality formation unit, keyword extraction unit, information transmission unit, health monitoring unit, and care provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the personality formation unit is implemented by the control unit 46A of the robot 414 and forms a personality through conversation with the user. The keyword extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts keywords from the conversation. The information transmission unit is implemented by the identification processing unit 290 of the data processing unit 12 and conveys information based on the extracted keywords. The health monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the user's health status. The care provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides care and information based on the collected data.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The support system can further analyze the user's daily routine and provide care and information at the optimal time. For example, if a user wakes up at the same time every morning, the system can perform health checks at that time. Also, if a user tends to relax during certain times, the system can provide relaxing content during those times. Furthermore, if a user often feels anxious at night, the system can send reassuring messages at night. This enables care and information provision tailored to the user's daily rhythm, resulting in more effective support.

[0103] The support system can further estimate the user's emotions and provide music and video content based on those estimates. For example, if a user is stressed, it can provide relaxing music. If a user is sad, it can provide uplifting video content. Furthermore, if a user is excited, it can provide energetic music that allows them to share in that excitement. This enables the provision of content tailored to the user's emotions, effectively supporting their mood.

[0104] The support system can further monitor the user's diet and provide dietary advice tailored to their health condition. For example, if a user has high blood pressure, it can suggest a low-sodium diet. If a user has diabetes, it can suggest a low-carbohydrate diet. Furthermore, if a user is nutritionally deficient, it can suggest a balanced diet. This enables dietary advice tailored to the user's health condition, supporting a healthier lifestyle.

[0105] The support system can further monitor the user's exercise habits and provide appropriate exercise plans. For example, if a user is not getting enough exercise, it can suggest simple stretches or walking. If a user is exercising excessively, it can suggest adequate rest. Furthermore, if a user prefers a particular exercise, it can provide a plan that incorporates that exercise. This ensures that users receive appropriate exercise plans tailored to their exercise habits, supporting their health maintenance.

[0106] The support system can further estimate the user's emotions and adjust the frequency of communication based on those estimates. For example, if the user is feeling lonely, it can communicate more frequently. If the user is stressed, it can provide relaxing conversations at appropriate intervals. Furthermore, if the user is busy, it can limit communication to the bare minimum. This adjusts the frequency of communication according to the user's emotions, providing more comfortable support.

[0107] The support system can further provide relevant event information based on the user's hobbies and interests. For example, if a user is interested in music, it can provide information on nearby concerts. If a user is interested in sports, it can provide information on local sporting events. Furthermore, if a user is interested in art, it can provide information on exhibitions and workshops. This provides event information tailored to the user's hobbies and interests, improving their quality of life.

[0108] The support system can further estimate the user's emotions and adjust the content and timing of reminders based on those emotions. For example, if the user is feeling stressed, a relaxing reminder can be set. If the user is busy, important tasks can be prioritized and reminders can be set. Furthermore, if the user is relaxed, reminders can be set at a more relaxed time. This adjusts the content and timing of reminders according to the user's emotions, providing more effective support.

[0109] The support system can further utilize the user's geographical location information to provide information on region-specific health risks. For example, if a user lives in an area with a high risk of hay fever, it can provide pollen information. Similarly, if a user lives in an area with a high risk of heatstroke, it can provide information on heatstroke prevention. Furthermore, if a user lives in an area with a high risk of infectious diseases, it can provide information on infectious disease prevention. This provides information on region-specific health risks, improving the user's health management.

[0110] The support system can further estimate the user's emotions and provide daily advice based on those estimates. For example, if the user is stressed, it can advise on relaxation methods and stress relief techniques. If the user is tired, it can advise on the importance of rest and effective ways to rest. Furthermore, if the user is feeling energetic, it can suggest new challenges or activities. This provides daily advice tailored to the user's emotions, improving their quality of life.

[0111] The support system can further analyze users' social media activity and provide relevant health and care information. For example, if a user frequently posts about health on social media, it can provide health advice based on that content. If a user follows specific health-related accounts, it can provide updates from those accounts. Furthermore, if a user participates in health-related groups or communities, it can provide information about those groups' activities. This allows for more appropriate support by providing health and care information based on the user's social media activity.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The personality formation unit engages in conversation with the user and forms a personality through these conversations. For example, it uses generative AI to provide topics based on the user's hobbies and interests, and analyzes past conversation history to select the most suitable topics and tone. Step 2: The keyword extraction unit extracts keywords from the conversation based on the personality formed by the personality formation unit. For example, it uses a generative AI to analyze the context of the conversation and prioritizes extracting keywords that are highly relevant. Step 3: The information transmission unit transmits information based on the keywords extracted by the keyword extraction unit. For example, it transmits information to family members or caregivers living in remote locations based on the extracted keywords. Step 4: The health monitoring unit monitors the user's health status. For example, it uses generative AI to constantly monitor the user's health status and quickly provide necessary care. Step 5: The care delivery department provides care and information based on the data collected by the health monitoring department. For example, it provides optimal care and information to the user based on the data collected using generative AI.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A personality formation unit that engages in conversations with users and forms their personality through those conversations, A keyword extraction unit extracts keywords from a conversation based on the personality formed by the personality formation unit, An information transmission unit that transmits information based on the keywords extracted by the keyword extraction unit, The health monitoring department monitors the user's health status, The system includes a care provision unit that provides care and information based on data collected by the health monitoring unit. A system characterized by the following features.

2. The personality formation unit is, The AI ​​generates topics based on the user's hobbies and interests. The system according to feature 1.

3. The keyword extraction unit, Generative AI extracts keywords from conversations. The system according to feature 1.

4. The aforementioned information transmission unit is Based on the extracted keywords, information will be conveyed to family members and caregivers living in remote locations. The system according to feature 1.

5. The aforementioned health monitoring unit, The AI ​​generates data to monitor the user's health status. The system according to feature 1.

6. The aforementioned care provision unit is Provide care and information based on data collected by generative AI. The system according to feature 1.

7. The personality formation unit is, It estimates the user's emotions and dynamically adjusts the personality to be more approachable based on those estimated emotions. The system according to feature 1.

8. The personality formation unit is, Analyze the user's past conversation history to select topics and tone. The system according to feature 1.

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