system

Abstract

The system according to this embodiment aims to facilitate online communication among the elderly and prevent social isolation. [Solution] The system according to the embodiment comprises a reception unit, a facilitation unit, an interpretation unit, and a provision unit. The reception unit receives user input. The facilitation unit facilitates the conversation based on the information received by the reception unit. The interpretation unit interprets the conversation facilitated by the facilitation unit and conveys it to other users. The provision unit provides health consultations and lifestyle advice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for the elderly to communicate smoothly online, and there is a lack of effective means to prevent social isolation.

[0005] The system according to the embodiment aims to enable the elderly to communicate smoothly online and prevent social isolation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a facilitation unit, an interpretation unit, and a provision unit. The reception unit receives user input. The facilitation unit facilitates the conversation based on the information received by the reception unit. The interpretation unit interprets the conversation facilitated by the facilitation unit and conveys it to other users. The provision unit provides health consultations and lifestyle advice. [Effects of the Invention]

[0007] The system according to this embodiment can enable elderly people to interact online smoothly and prevent social isolation. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 online community platform according to an embodiment of the present invention is an innovative system developed for elderly people living alone. This system utilizes the latest generative AI technology to provide a virtual space for interaction that is easy for elderly people unfamiliar with IT to use. Users can participate in themed forums based on their interests and experiences using voice control or simple touch controls. In each forum, the generative AI acts as a conversation facilitator, supporting interaction between users. The generative AI also appropriately interprets what the elderly people say and, when necessary, conveys it clearly to other users, thereby enabling smooth communication. Furthermore, this platform is not merely a place for interaction, but also has functions to comprehensively support the lives of the elderly, such as health consultations, lifestyle advice, and the hosting of online events. It can also collaborate with family members and caregivers to share the social participation status and health status of the elderly. This service prevents social isolation among the elderly, promotes the building of new relationships, and supports participation in the digital society. As a result, the quality of life (QOL) of the elderly can be improved, enabling them to enjoy a vibrant old age. Thus, the online community platform can comprehensively support the lives of the elderly and prevent social isolation.

[0029] The online community platform according to this embodiment comprises a reception unit, a facilitation unit, an interpretation unit, and a provision unit. The reception unit receives user input. The reception unit can, for example, receive voice operations or touch operations. The reception unit can analyze user input using generative AI and perform appropriate processing. The facilitation unit facilitates conversations based on the information received by the reception unit. The facilitation unit can provide themed forums based on users' interests and experiences using generative AI. The facilitation unit can support interaction between users using generative AI. The interpretation unit interprets conversations facilitated by the facilitation unit and conveys them to other users. The interpretation unit can appropriately interpret statements made by elderly people using generative AI and convey them to other users in an easily understandable way. The interpretation unit can perform contextual understanding and semantic analysis using generative AI. The provision unit provides health consultations and lifestyle advice. The provision unit can provide appropriate advice based on users' health status and living conditions using generative AI. The provision unit can support the hosting of online events using generative AI. The service provider can use generation AI to support collaboration with family members and caregivers. This allows the online community platform, according to this embodiment, to comprehensively support the lives of the elderly and prevent social isolation.

[0030] The reception desk receives user input. For example, it can accept voice and touch input. Specifically, with voice input, the user speaks into a microphone, and speech recognition technology converts the content into text, which is then entered into the system. With touch input, the user touches the screen of their smartphone or tablet to select input content or enter text. The reception desk can analyze user input using generative AI and perform appropriate processing. Generative AI utilizes natural language processing technology to understand user statements and input content and generate contextually appropriate responses. For example, if a user asks, "What's the weather like today?", the generative AI analyzes the question and provides current weather information. Similarly, if a user asks a health-related question, the generative AI analyzes the question and provides appropriate health advice. Furthermore, the reception desk can record the user's input history and generate personalized responses based on past interactions. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0031] The Facilitation Department facilitates conversations based on information received by the Reception Department. Using generative AI, the Facilitation Department can provide themed forums based on users' interests and experiences. Specifically, when a user provides a topic about their hobbies or interests, the generative AI analyzes the information and suggests relevant themed forums. For example, if a user is interested in gardening, the generative AI will suggest a gardening forum, facilitating interaction with other gardening enthusiasts. Furthermore, the Facilitation Department can support user-to-user interaction using generative AI. The generative AI analyzes user statements and actions, providing conversation starters at appropriate times. For example, when a user joins a new forum, the generative AI might display a message such as "Please introduce yourself," encouraging interaction with other users. In addition, the Facilitation Department can learn users' interests and behavioral patterns, providing each user with optimal interaction opportunities. This allows the Facilitation Department to facilitate smoother communication among users and revitalize the online community.

[0032] The interpretation unit interprets the conversation facilitated by the facilitation unit and conveys it to other users. Using generative AI, the interpretation unit can appropriately interpret the statements of elderly users and convey them clearly to other users. Specifically, the generative AI analyzes what the elderly user says, understands the context and intent, and then reconstructs it in a way that is easy for other users to understand. For example, if an elderly user says, "I used to grow flowers in my garden a lot," the generative AI will convey this statement to other users as, "This person enjoys gardening and used to grow flowers in their garden." Furthermore, the interpretation unit can use generative AI to perform contextual understanding and semantic analysis. The generative AI considers the flow of the conversation and background information to accurately grasp the intent and emotion of the statement. This allows the interpretation unit to facilitate communication between users and prevent misunderstandings and problems. In addition, the interpretation unit can also support communication between users who speak different languages. The generative AI can translate statements in real time, enabling smooth conversations between users who speak different languages. This enables the interpreter to facilitate smooth communication within online communities with diverse user bases.

[0033] The service department provides health consultations and lifestyle advice. Using generative AI, the service department can provide appropriate advice based on the user's health condition and lifestyle. Specifically, when a user asks a health-related question, the generative AI analyzes the question and provides advice based on medical databases and expert knowledge. For example, if a user asks, "My lower back has been hurting lately, what should I do?", the generative AI will suggest appropriate stretching methods and lifestyle improvements for that symptom. The service department can also use generative AI to support the hosting of online events. The generative AI assists with event planning and operation, managing participants and scheduling. For example, when hosting online events such as health seminars or hobby workshops, the generative AI registers participants, sends reminders, and supports the event's progress. Furthermore, the service department can use generative AI to support coordination with family members and caregivers. The generative AI appropriately communicates the user's health condition and lifestyle to family members and caregivers and provides necessary support. For example, if a user's health condition deteriorates, the generative AI notifies family members and caregivers of this information and prompts appropriate action. This allows the service provider to comprehensively support users' health and lifestyle, and provide an environment where they can live with peace of mind.

[0034] The service provider includes an event provider that supports the hosting of online events. The event provider can plan and manage online events using generative AI. The event provider can host online events such as webinars, virtual meetings, and live streaming events. The event provider can provide events based on users' interests using generative AI. The event provider can support the progress of events using generative AI. In this way, the lives of the elderly can be enriched by supporting the hosting of online events. Some or all of the above-mentioned processes in the event provider may be performed using generative AI or not. For example, the event provider can input an event plan based on users' interests into the generative AI, and the generative AI can generate the details of the event.

[0035] The service unit includes a collaboration unit that supports coordination with family members and caregivers. The collaboration unit can share information with family members and caregivers using a generation AI. For example, the collaboration unit can share the user's social participation status and health status with family members and caregivers. The collaboration unit can use the generation AI to grasp the user's living situation in real time and notify family members and caregivers. The collaboration unit can use the generation AI to monitor the user's health status and notify family members and caregivers if there is an abnormality. This supports coordination with family members and caregivers, enabling the sharing of information about the elderly person's social participation status and health status. Some or all of the above-described processes in the collaboration unit may be performed using the generation AI or not. For example, the collaboration unit can input data monitoring the user's health status into the generation AI, which can then detect and notify of an abnormality.

[0036] The reception desk accepts voice and touch inputs. The reception desk can utilize speech recognition and touchscreen technologies using generative AI. For example, the reception desk can use speech recognition technology to analyze the user's voice input and perform appropriate processing. The reception desk can use touchscreen technology to accept the user's touch inputs. The reception desk can use generative AI to simplify user operation, making it easy for elderly people unfamiliar with IT to use. This makes it easy for elderly people unfamiliar with IT to use by accepting voice and touch inputs. Some or all of the above-mentioned processing in the reception desk may be performed using generative AI, or it may be performed without generative AI. For example, the reception desk can use speech recognition technology to input the user's voice input into the generative AI, and the generative AI can analyze the voice and perform appropriate processing.

[0037] The promotion unit provides themed forums based on users' interests and experiences. Using generative AI, the promotion unit can analyze users' interests and experiences and provide appropriate themed forums. For example, the promotion unit can provide a "gardening forum" for users who enjoy gardening, allowing them to interact with other gardening enthusiasts. The promotion unit can support user interaction using generative AI. Using generative AI, the promotion unit acts as a conversation facilitator, facilitating natural conversations between users. This promotes user interaction by providing themed forums based on users' interests and experiences. Some or all of the above-described processes in the promotion unit may be performed using generative AI, or they may not. For example, the promotion unit can input user interests and experiences into the generative AI, which can then provide appropriate themed forums.

[0038] The interpretation unit appropriately interprets the statements of elderly people and conveys them clearly to other users. The interpretation unit can analyze the statements of elderly people using generative AI and make appropriate interpretations. For example, if an elderly person asks, "What is the name of this flower?", the generative AI can convey that question to other users and obtain answers. The interpretation unit can accurately grasp the intent of a statement by performing contextual understanding and semantic analysis using generative AI. The interpretation unit can convey the content of a statement to other users in an easy-to-understand manner using generative AI. This enables smooth communication by appropriately interpreting the statements of elderly people and conveying them clearly to other users. Some or all of the above processing in the interpretation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the interpretation unit can input the statements of elderly people into generative AI, which can analyze the intent of the statements and convey them to other users.

[0039] The reception desk analyzes the user's past input history and selects the optimal reception method. The reception desk can use generative AI to analyze the user's past input history and select an appropriate reception method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (voice, touch, etc.). The reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can suggest relevant input methods by referring to content that the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without generative AI. For example, the reception desk can input the user's past input history into the generative AI, and the generative AI can select the optimal reception method.

[0040] The reception unit filters input based on the user's current areas of interest. The reception unit can use generative AI to analyze the user's current areas of interest and perform appropriate filtering. For example, the reception unit can prioritize input related to themes the user has recently become interested in. The reception unit can analyze the user's past areas of interest and filter related input. The reception unit can filter input based on the themes of the forums the user is currently participating in. This allows the reception unit to prioritize input that is highly relevant by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input the user's current areas of interest into the generative AI, which can then perform appropriate filtering.

[0041] The reception unit prioritizes receiving inputs that are highly relevant based on the user's geographical location information. The reception unit can analyze the user's geographical location information using a generative AI and prioritize receiving appropriate inputs. For example, if the user is in a specific region, the reception unit can prioritize receiving inputs related to that region. If the user is traveling, the reception unit can prioritize receiving inputs related to their travel destination. If the user is at home, the reception unit can prioritize receiving inputs related to their home area. By prioritizing the reception of highly relevant inputs based on the user's geographical location information, the reception unit can receive more appropriate inputs. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant inputs.

[0042] The reception unit analyzes the user's social media activity when receiving input and accepts relevant input. The reception unit can use generative AI to analyze the user's social media activity and accept appropriate input. For example, the reception unit can prioritize accepting input related to themes that the user frequently posts about on social media. The reception unit can also prioritize accepting input related to themes that the user's social media friends are interested in. The reception unit can analyze the user's social media activity history and accept relevant input. This allows the reception unit to prioritize accepting relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input the user's social media activity into the generative AI, and the generative AI can accept relevant input.

[0043] The facilitator selects the optimal facilitator method based on the user's interests and experiences when facilitating a conversation. The facilitator can use generative AI to analyze the user's interests and experiences and select an appropriate conversation facilitator method. For example, if the user is interested in gardening, the generative AI can provide topics related to gardening. If the user is interested in travel, the generative AI can provide topics related to travel. If the user is interested in cooking, the generative AI can provide topics related to cooking. By selecting the optimal facilitator method based on the user's interests and experiences, a more effective conversation can be facilitated. Some or all of the above-described processes in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's interests and experiences into the generative AI, which can then select an appropriate conversation facilitator method.

[0044] The facilitator improves the accuracy of conversation facilitation by referring to the user's past conversation history. The facilitator can use generative AI to analyze the user's past conversation history and select an appropriate conversation facilitation method. For example, the facilitator can provide relevant topics based on what the user has said in the past. The facilitator can analyze themes of interest from the user's past conversation history and provide topics. The facilitator can facilitate conversation by referring to themes of forums the user has participated in in the past. In this way, the accuracy of facilitation can be improved by referring to the user's past conversation history. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's past conversation history into the generative AI, which can then select an appropriate conversation facilitation method.

[0045] The facilitator prioritizes promoting conversations that are highly relevant based on the user's geographical location information during conversation facilitation. The facilitator can analyze the user's geographical location information using generative AI and prioritize promoting appropriate conversations. For example, if the user is in a specific region, the facilitator can offer topics related to that region. If the user is traveling, the facilitator can offer topics related to their travel destination. If the user is at home, the facilitator can offer topics related to their home area. This allows for more appropriate conversations to be provided by prioritizing and promoting highly relevant conversations based on the user's geographical location information. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's geographical location information into the generative AI, which can then prioritize promoting highly relevant conversations.

[0046] The facilitator analyzes the user's social media activity and facilitates relevant conversations during conversation facilitation. The facilitator can use generative AI to analyze the user's social media activity and facilitate appropriate conversations. For example, the facilitator can provide topics related to themes the user frequently posts about on social media. The facilitator can provide topics related to themes that the user's social media friends are interested in. The facilitator can analyze the user's social media activity history and provide relevant topics. This allows the facilitator to prioritize providing relevant conversations by analyzing the user's social media activity. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's social media activity into the generative AI, which can then facilitate relevant conversations.

[0047] The interpretation unit improves the accuracy of its interpretations by referring to the user's past utterance history when interpreting utterances. The interpretation unit can use a generative AI to analyze the user's past utterance history and select an appropriate method for interpreting utterances. For example, the interpretation unit can interpret relevant utterances based on what the user has said in the past. The interpretation unit can analyze themes of interest from the user's past utterance history and interpret utterances. The interpretation unit can interpret utterances by referring to themes of forums the user has participated in in the past. In this way, the accuracy of interpretation can be improved by referring to the user's past utterance history. Some or all of the above processing in the interpretation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the interpretation unit can input the user's past utterance history into a generative AI, and the generative AI can select an appropriate method for interpreting utterances.

[0048] The interpretation unit interprets utterances while considering the user's attribute information. The interpretation unit can use a generative AI to analyze the user's attribute information and select an appropriate method for interpreting the utterance. For example, the interpretation unit can interpret utterances based on the user's age and gender. The interpretation unit can interpret utterances based on the user's occupation and hobbies. The interpretation unit can interpret utterances based on the user's place of residence and cultural background. This allows for more appropriate interpretations by considering the user's attribute information. Some or all of the above-described processes in the interpretation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the interpretation unit can input the user's attribute information into a generative AI, which can then select an appropriate method for interpreting the utterance.

[0049] The interpretation unit, when interpreting utterances, prioritizes interpreting utterances that are highly relevant based on the user's geographical location information. The interpretation unit can analyze the user's geographical location information using a generative AI and prioritize the interpretation of appropriate utterances. For example, if the user is in a specific region, the interpretation unit can prioritize interpreting utterances related to that region. If the user is traveling, the interpretation unit can prioritize interpreting utterances related to the travel destination. If the user is at home, the interpretation unit can prioritize interpreting utterances related to the area around the user's home. This allows for more appropriate interpretations by prioritizing the interpretation of utterances that are highly relevant based on the user's geographical location information. Some or all of the above processing in the interpretation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the interpretation unit can input the user's geographical location information into a generative AI, which can then prioritize the interpretation of utterances that are highly relevant.

[0050] The interpretation unit analyzes the user's social media activity and interprets relevant statements when interpreting statements. The interpretation unit can use generative AI to analyze the user's social media activity and interpret appropriate statements. For example, the interpretation unit can prioritize interpreting statements related to themes that the user frequently posts about on social media. The interpretation unit can also interpret statements related to themes that the user's social media friends are interested in. The interpretation unit can analyze the user's social media activity history and interpret relevant statements. This allows the interpretation unit to prioritize interpreting relevant statements by analyzing the user's social media activity. Some or all of the above processing in the interpretation unit may be performed using generative AI or not. For example, the interpretation unit can input the user's social media activity into generative AI, which can then interpret relevant statements.

[0051] The service provider provides optimal advice by referring to the user's past consultation history when providing advice. The service provider can use a generative AI to analyze the user's past consultation history and select appropriate advice. For example, the service provider can provide relevant advice based on the content of past consultations the user has had. The service provider can analyze the user's areas of interest from their past consultation history and provide advice. The service provider can provide optimal advice by referring to advice the user has received in the past. In this way, the service provider can provide optimal advice by referring to the user's past consultation history. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's past consultation history into a generative AI, and the generative AI can select appropriate advice.

[0052] The service provider customizes the content of the advice based on the user's current health condition when providing advice. The service provider can use a generative AI to analyze the user's health condition and select appropriate advice. For example, if the user is tired, the generative AI can provide advice recommending rest. If the user is seeking healthy exercise, the generative AI can provide advice regarding exercise. If the user is feeling unwell, the generative AI can provide advice recommending that the user visit a medical institution. By customizing the content of the advice based on the user's current health condition, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using the generative AI, or it may be performed without using the generative AI. For example, the service provider can input the user's health condition data into the generative AI, and the generative AI can select appropriate advice.

[0053] The service provider provides optimal advice based on the user's geographical location information. The service provider can use a generative AI to analyze the user's geographical location information and select appropriate advice. For example, if the user is in a specific region, the service provider can provide advice related to that region. If the user is traveling, the service provider can provide advice related to their travel destination. If the user is at home, the service provider can provide advice related to their home area. By providing optimal advice based on the user's geographical location information, the service provider can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's geographical location information into a generative AI, which can then select appropriate advice.

[0054] The service provider analyzes the user's social media activity and provides relevant advice when offering advice. The service provider can use generative AI to analyze the user's social media activity and select appropriate advice. For example, the service provider can provide advice related to themes that the user frequently posts about on social media. The service provider can provide advice related to themes that the user's social media friends are interested in. The service provider can analyze the user's social media activity history and provide relevant advice. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's social media activity into generative AI, and the generative AI can select relevant advice.

[0055] The Events Department provides the most suitable events when an event is being held, by referring to the user's past participation history. The Events Department can use generative AI to analyze the user's past participation history and select appropriate events. For example, the Events Department can provide relevant events based on events the user has previously participated in. The Events Department can analyze themes of interest from the user's past participation history and provide events accordingly. The Events Department can provide the most suitable events by referring to the user's evaluation of events they have previously participated in. In this way, the most suitable events can be provided by referring to the user's past participation history. Some or all of the above processing in the Events Department may be performed using generative AI, or it may be performed without generative AI. For example, the Events Department can input the user's past participation history into generative AI, and the generative AI can select appropriate events.

[0056] The Events Unit provides the most suitable events based on the user's geographical location information when an event is being held. The Events Unit can use generative AI to analyze the user's geographical location information and select appropriate events. For example, if the user is in a specific region, the Events Unit can provide events related to that region. If the user is traveling, the Events Unit can provide events related to their travel destination. If the user is at home, the Events Unit can provide events related to their home area. By providing the most suitable events based on the user's geographical location information, the Events Unit can provide more appropriate events. Some or all of the above processing in the Events Unit may be performed using generative AI, or it may be performed without generative AI. For example, the Events Unit can input the user's geographical location information into the generative AI, which can then select appropriate events.

[0057] The integration unit provides the optimal integration method by referring to the user's past integration history during integration. The integration unit can use a generative AI to analyze the user's past integration history and select an appropriate integration method. For example, the integration unit can provide relevant integration methods based on the content the user has previously integrated with. The integration unit can analyze themes of interest from the user's past integration history and provide integration methods. The integration unit can provide the optimal integration method by referring to an evaluation of the content the user has previously integrated with. In this way, the optimal integration method can be provided by referring to the user's past integration history. Some or all of the above processing in the integration unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the integration unit can input the user's past integration history into a generative AI, and the generative AI can select an appropriate integration method.

[0058] The integration unit provides the optimal integration method based on the user's geographical location information during integration. The integration unit can analyze the user's geographical location information using a generative AI and select an appropriate integration method. For example, if the user is in a specific region, the integration unit can provide an integration method related to that region. If the user is traveling, the integration unit can provide an integration method related to the travel destination. If the user is at home, the integration unit can provide an integration method related to the area around the user's home. This allows for more appropriate integration by providing the optimal integration method based on the user's geographical location information. Some or all of the above processing in the integration unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the integration unit can input the user's geographical location information into a generative AI, which can then select an appropriate integration method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk not only receives user input but can also provide appropriate feedback based on that input. For example, if a user enters a question, the reception desk can use generative AI to provide an immediate answer to that question. Also, if a user enters a message of gratitude, the reception desk can use generative AI to provide an appropriate response. Furthermore, if a user expresses dissatisfaction, the reception desk can use generative AI to suggest solutions to that dissatisfaction. In this way, by providing immediate feedback on user input, user satisfaction can be improved.

[0061] The service provider can not only support the hosting of online events but also collect user feedback and incorporate it into future events. For example, they can conduct a survey of users after the event and use the results to improve the content of the next event. They can also add a feature that allows users to provide real-time feedback during the event. Furthermore, they can analyze user feedback to determine whether specific themes or event formats are popular. This allows them to leverage user feedback to deliver more engaging events.

[0062] The collaboration unit not only facilitates information sharing with family members and caregivers, but can also adjust the timing of collaboration based on the user's daily routine. For example, if the user is a morning person, the collaboration unit can share information during the morning hours. Similarly, if the user is a night owl, the collaboration unit can share information during the evening hours. Furthermore, if the user's daily routine changes, the collaboration unit can automatically adjust the timing of collaboration accordingly. This allows for more effective collaboration by aligning information sharing with the user's daily rhythm.

[0063] The reception desk can accept not only voice and touch input, but also gesture input. For example, if a user waves their hand, the reception desk can recognize the gesture and provide an appropriate response. Similarly, if a user points, the reception desk can perform the action indicated. Furthermore, if a user performs a specific gesture, the reception desk can provide a customized response corresponding to that gesture. This allows for more intuitive operation by accepting gesture input in addition to voice and touch input.

[0064] The promotion team can not only provide themed forums based on users' interests and experiences, but can also suggest new themes based on users' learning history. For example, it can analyze the content of forums that users have previously participated in and suggest related new themes. Furthermore, if a user shows a high level of interest in a particular theme, the promotion team can create a new forum related to that theme. In addition, if a user develops new interests through their activities in the forums, the promotion team can suggest new themes based on those interests. This allows the promotion team to leverage users' learning history to provide a wider variety of themed forums.

[0065] The interpretation unit can not only appropriately interpret the statements of elderly users and convey them clearly to other users, but can also interpret them while considering the tone and nuances of the statements. For example, if an elderly person tells a joke, the interpretation unit can understand the tone of the joke and convey it to other users with humor. Also, if an elderly person expresses gratitude, the interpretation unit can accurately convey the nuance of that gratitude. Furthermore, if an elderly person expresses dissatisfaction, the interpretation unit can consider the tone of that dissatisfaction and make an appropriate interpretation. In this way, by interpreting statements while considering their tone and nuances, more natural communication can be achieved.

[0066] The reception desk can analyze the user's past input history and select the optimal reception method, as well as learn the user's input patterns to perform predictive input. For example, if a user frequently uses a particular phrase, the reception desk can predict that phrase and complete the input. Also, if a user tends to perform a particular operation at a specific time, the reception desk can suggest an appropriate operation for that time. Furthermore, by learning the user's input patterns, the reception desk can understand the user's intent more accurately and provide an appropriate response. In this way, learning the user's input patterns and performing predictive input enables a smoother user experience.

[0067] The reception system not only filters input based on the user's current areas of interest, but can also track and adapt to changes in the user's areas of interest in real time. For example, if a user begins to show interest in a new topic, the reception system can detect this change and prioritize receiving relevant input. Furthermore, if a user loses interest in a particular topic, the reception system can filter out input related to that topic. By analyzing changes in the user's areas of interest, the reception system can provide customized feedback tailored to the user's interests. This allows for a more personalized service by adapting to changes in the user's areas of interest.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives user input. The reception desk can accept voice and touch input, analyze user input using generative AI, and perform appropriate processing. Step 2: The Facilitation Department facilitates conversations based on the information received by the Reception Department. The Facilitation Department can use generative AI to provide themed forums based on users' interests and experiences, supporting interaction among users. Step 3: The interpretation unit interprets the conversation facilitated by the facilitation unit and conveys it to other users. The interpretation unit can appropriately interpret the elderly person's statements using generative AI and convey them clearly to other users. It can also perform contextual understanding and semantic analysis. Step 4: The service provider offers health consultations and lifestyle advice. Using generative AI, the service provider can provide appropriate advice based on the user's health condition and lifestyle. It can also support the hosting of online events and collaboration with family members and caregivers.

[0070] (Example of form 2) The online community platform according to an embodiment of the present invention is an innovative system developed for elderly people living alone. This system utilizes the latest generative AI technology to provide a virtual space for interaction that is easy for elderly people unfamiliar with IT to use. Users can participate in themed forums based on their interests and experiences using voice control or simple touch controls. In each forum, the generative AI acts as a conversation facilitator, supporting interaction between users. The generative AI also appropriately interprets what the elderly people say and, when necessary, conveys it clearly to other users, thereby enabling smooth communication. Furthermore, this platform is not merely a place for interaction, but also has functions to comprehensively support the lives of the elderly, such as health consultations, lifestyle advice, and the hosting of online events. It can also collaborate with family members and caregivers to share the social participation status and health status of the elderly. This service prevents social isolation among the elderly, promotes the building of new relationships, and supports participation in the digital society. As a result, the quality of life (QOL) of the elderly can be improved, enabling them to enjoy a vibrant old age. Thus, the online community platform can comprehensively support the lives of the elderly and prevent social isolation.

[0071] The online community platform according to this embodiment comprises a reception unit, a facilitation unit, an interpretation unit, and a provision unit. The reception unit receives user input. The reception unit can, for example, receive voice operations or touch operations. The reception unit can analyze user input using generative AI and perform appropriate processing. The facilitation unit facilitates conversations based on the information received by the reception unit. The facilitation unit can provide themed forums based on users' interests and experiences using generative AI. The facilitation unit can support interaction between users using generative AI. The interpretation unit interprets conversations facilitated by the facilitation unit and conveys them to other users. The interpretation unit can appropriately interpret statements made by elderly people using generative AI and convey them to other users in an easily understandable way. The interpretation unit can perform contextual understanding and semantic analysis using generative AI. The provision unit provides health consultations and lifestyle advice. The provision unit can provide appropriate advice based on users' health status and living conditions using generative AI. The provision unit can support the hosting of online events using generative AI. The service provider can use generation AI to support collaboration with family members and caregivers. This allows the online community platform, according to this embodiment, to comprehensively support the lives of the elderly and prevent social isolation.

[0072] The reception desk receives user input. For example, it can accept voice and touch input. Specifically, with voice input, the user speaks into a microphone, and speech recognition technology converts the content into text, which is then entered into the system. With touch input, the user touches the screen of their smartphone or tablet to select input content or enter text. The reception desk can analyze user input using generative AI and perform appropriate processing. Generative AI utilizes natural language processing technology to understand user statements and input content and generate contextually appropriate responses. For example, if a user asks, "What's the weather like today?", the generative AI analyzes the question and provides current weather information. Similarly, if a user asks a health-related question, the generative AI analyzes the question and provides appropriate health advice. Furthermore, the reception desk can record the user's input history and generate personalized responses based on past interactions. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0073] The Facilitation Department facilitates conversations based on information received by the Reception Department. Using generative AI, the Facilitation Department can provide themed forums based on users' interests and experiences. Specifically, when a user provides a topic about their hobbies or interests, the generative AI analyzes the information and suggests relevant themed forums. For example, if a user is interested in gardening, the generative AI will suggest a gardening forum, facilitating interaction with other gardening enthusiasts. Furthermore, the Facilitation Department can support user-to-user interaction using generative AI. The generative AI analyzes user statements and actions, providing conversation starters at appropriate times. For example, when a user joins a new forum, the generative AI might display a message such as "Please introduce yourself," encouraging interaction with other users. In addition, the Facilitation Department can learn users' interests and behavioral patterns, providing each user with optimal interaction opportunities. This allows the Facilitation Department to facilitate smoother communication among users and revitalize the online community.

[0074] The interpretation unit interprets the conversation facilitated by the facilitation unit and conveys it to other users. Using generative AI, the interpretation unit can appropriately interpret the statements of elderly users and convey them clearly to other users. Specifically, the generative AI analyzes what the elderly user says, understands the context and intent, and then reconstructs it in a way that is easy for other users to understand. For example, if an elderly user says, "I used to grow flowers in my garden a lot," the generative AI will convey this statement to other users as, "This person enjoys gardening and used to grow flowers in their garden." Furthermore, the interpretation unit can use generative AI to perform contextual understanding and semantic analysis. The generative AI considers the flow of the conversation and background information to accurately grasp the intent and emotion of the statement. This allows the interpretation unit to facilitate communication between users and prevent misunderstandings and problems. In addition, the interpretation unit can also support communication between users who speak different languages. The generative AI can translate statements in real time, enabling smooth conversations between users who speak different languages. This enables the interpreter to facilitate smooth communication within online communities with diverse user bases.

[0075] The service department provides health consultations and lifestyle advice. Using generative AI, the service department can provide appropriate advice based on the user's health condition and lifestyle. Specifically, when a user asks a health-related question, the generative AI analyzes the question and provides advice based on medical databases and expert knowledge. For example, if a user asks, "My lower back has been hurting lately, what should I do?", the generative AI will suggest appropriate stretching methods and lifestyle improvements for that symptom. The service department can also use generative AI to support the hosting of online events. The generative AI assists with event planning and operation, managing participants and scheduling. For example, when hosting online events such as health seminars or hobby workshops, the generative AI registers participants, sends reminders, and supports the event's progress. Furthermore, the service department can use generative AI to support coordination with family members and caregivers. The generative AI appropriately communicates the user's health condition and lifestyle to family members and caregivers and provides necessary support. For example, if a user's health condition deteriorates, the generative AI notifies family members and caregivers of this information and prompts appropriate action. This allows the service provider to comprehensively support users' health and lifestyle, and provide an environment where they can live with peace of mind.

[0076] The service provider includes an event provider that supports the hosting of online events. The event provider can plan and manage online events using generative AI. The event provider can host online events such as webinars, virtual meetings, and live streaming events. The event provider can provide events based on users' interests using generative AI. The event provider can support the progress of events using generative AI. In this way, the lives of the elderly can be enriched by supporting the hosting of online events. Some or all of the above-mentioned processes in the event provider may be performed using generative AI or not. For example, the event provider can input an event plan based on users' interests into the generative AI, and the generative AI can generate the details of the event.

[0077] The service unit includes a collaboration unit that supports coordination with family members and caregivers. The collaboration unit can share information with family members and caregivers using a generation AI. For example, the collaboration unit can share the user's social participation status and health status with family members and caregivers. The collaboration unit can use the generation AI to grasp the user's living situation in real time and notify family members and caregivers. The collaboration unit can use the generation AI to monitor the user's health status and notify family members and caregivers if there is an abnormality. This supports coordination with family members and caregivers, enabling the sharing of information about the elderly person's social participation status and health status. Some or all of the above-described processes in the collaboration unit may be performed using the generation AI or not. For example, the collaboration unit can input data monitoring the user's health status into the generation AI, which can then detect and notify of an abnormality.

[0078] The reception desk accepts voice and touch inputs. The reception desk can utilize speech recognition and touchscreen technologies using generative AI. For example, the reception desk can use speech recognition technology to analyze the user's voice input and perform appropriate processing. The reception desk can use touchscreen technology to accept the user's touch inputs. The reception desk can use generative AI to simplify user operation, making it easy for elderly people unfamiliar with IT to use. This makes it easy for elderly people unfamiliar with IT to use by accepting voice and touch inputs. Some or all of the above-mentioned processing in the reception desk may be performed using generative AI, or it may be performed without generative AI. For example, the reception desk can use speech recognition technology to input the user's voice input into the generative AI, and the generative AI can analyze the voice and perform appropriate processing.

[0079] The promotion unit provides themed forums based on users' interests and experiences. Using generative AI, the promotion unit can analyze users' interests and experiences and provide appropriate themed forums. For example, the promotion unit can provide a "gardening forum" for users who enjoy gardening, allowing them to interact with other gardening enthusiasts. The promotion unit can support user interaction using generative AI. Using generative AI, the promotion unit acts as a conversation facilitator, facilitating natural conversations between users. This promotes user interaction by providing themed forums based on users' interests and experiences. Some or all of the above-described processes in the promotion unit may be performed using generative AI, or they may not. For example, the promotion unit can input user interests and experiences into the generative AI, which can then provide appropriate themed forums.

[0080] The interpretation unit appropriately interprets the statements of elderly people and conveys them clearly to other users. The interpretation unit can analyze the statements of elderly people using generative AI and make appropriate interpretations. For example, if an elderly person asks, "What is the name of this flower?", the generative AI can convey that question to other users and obtain answers. The interpretation unit can accurately grasp the intent of a statement by performing contextual understanding and semantic analysis using generative AI. The interpretation unit can convey the content of a statement to other users in an easy-to-understand manner using generative AI. This enables smooth communication by appropriately interpreting the statements of elderly people and conveying them clearly to other users. Some or all of the above processing in the interpretation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the interpretation unit can input the statements of elderly people into generative AI, which can analyze the intent of the statements and convey them to other users.

[0081] The reception unit estimates the user's emotions and adjusts the timing of input requests based on the estimated emotions. The reception unit can use generative AI to analyze the user's emotions and prompt for input at the appropriate time. For example, if the user is stressed, the reception unit can prompt for input when the generative AI is able to help the user relax. If the user is excited, the reception unit can prompt for input when the generative AI has calmed down. If the user is tired, the reception unit can prompt for input after the generative AI has taken a break. By adjusting the timing of input requests based on the user's emotions, input can be prompted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into the generative AI, which can analyze the emotions and prompt for input at the appropriate time.

[0082] The reception desk analyzes the user's past input history and selects the optimal reception method. The reception desk can use generative AI to analyze the user's past input history and select an appropriate reception method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (voice, touch, etc.). The reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can suggest relevant input methods by referring to content that the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without generative AI. For example, the reception desk can input the user's past input history into the generative AI, and the generative AI can select the optimal reception method.

[0083] The reception unit filters input based on the user's current areas of interest. The reception unit can use generative AI to analyze the user's current areas of interest and perform appropriate filtering. For example, the reception unit can prioritize input related to themes the user has recently become interested in. The reception unit can analyze the user's past areas of interest and filter related input. The reception unit can filter input based on the themes of the forums the user is currently participating in. This allows the reception unit to prioritize input that is highly relevant by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input the user's current areas of interest into the generative AI, which can then perform appropriate filtering.

[0084] The reception unit estimates the user's emotions and determines the priority of inputs to be received based on the estimated emotions. The reception unit can use generative AI to analyze the user's emotions and determine the appropriate priority of inputs. For example, if the user is tense, the generative AI can prioritize inputs that promote relaxation. If the user is excited, the generative AI can prioritize inputs that promote calmness. If the user is tired, the generative AI can prioritize inputs that are easy to understand. This allows for the prioritization of more appropriate inputs by determining the priority of inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into a generative AI, which can analyze the emotions and determine the appropriate priority of inputs.

[0085] The reception unit prioritizes receiving inputs that are highly relevant based on the user's geographical location information. The reception unit can analyze the user's geographical location information using a generative AI and prioritize receiving appropriate inputs. For example, if the user is in a specific region, the reception unit can prioritize receiving inputs related to that region. If the user is traveling, the reception unit can prioritize receiving inputs related to their travel destination. If the user is at home, the reception unit can prioritize receiving inputs related to their home area. By prioritizing the reception of highly relevant inputs based on the user's geographical location information, the reception unit can receive more appropriate inputs. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant inputs.

[0086] The reception unit analyzes the user's social media activity when receiving input and accepts relevant input. The reception unit can use generative AI to analyze the user's social media activity and accept appropriate input. For example, the reception unit can prioritize accepting input related to themes that the user frequently posts about on social media. The reception unit can also prioritize accepting input related to themes that the user's social media friends are interested in. The reception unit can analyze the user's social media activity history and accept relevant input. This allows the reception unit to prioritize accepting relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input the user's social media activity into the generative AI, and the generative AI can accept relevant input.

[0087] The facilitation unit estimates the user's emotions and adjusts the conversation facilitation method based on the estimated user emotions. The facilitation unit can use generative AI to analyze the user's emotions and select an appropriate conversation facilitation method. For example, if the user is relaxed, the facilitation unit can use generative AI to facilitate the conversation at a relaxed pace. If the user is excited, the facilitation unit can use generative AI to facilitate a lively conversation. If the user is nervous, the facilitation unit can use generative AI to facilitate a calm conversation. In this way, by adjusting the conversation facilitation method based on the user's emotions, a more appropriate conversation can be facilitated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facilitation unit may be performed using generative AI or not. For example, the facilitation unit can input user emotion data into the generative AI, which can analyze the emotions and select an appropriate conversation facilitation method.

[0088] The facilitator selects the optimal facilitator method based on the user's interests and experiences when facilitating a conversation. The facilitator can use generative AI to analyze the user's interests and experiences and select an appropriate conversation facilitator method. For example, if the user is interested in gardening, the generative AI can provide topics related to gardening. If the user is interested in travel, the generative AI can provide topics related to travel. If the user is interested in cooking, the generative AI can provide topics related to cooking. By selecting the optimal facilitator method based on the user's interests and experiences, a more effective conversation can be facilitated. Some or all of the above-described processes in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's interests and experiences into the generative AI, which can then select an appropriate conversation facilitator method.

[0089] The facilitator improves the accuracy of conversation facilitation by referring to the user's past conversation history. The facilitator can use generative AI to analyze the user's past conversation history and select an appropriate conversation facilitation method. For example, the facilitator can provide relevant topics based on what the user has said in the past. The facilitator can analyze themes of interest from the user's past conversation history and provide topics. The facilitator can facilitate conversation by referring to themes of forums the user has participated in in the past. In this way, the accuracy of facilitation can be improved by referring to the user's past conversation history. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's past conversation history into the generative AI, which can then select an appropriate conversation facilitation method.

[0090] The facilitator estimates the user's emotions and determines conversation priorities based on the estimated emotions. The facilitator can use generative AI to analyze the user's emotions and determine appropriate conversation priorities. For example, if the user is nervous, the facilitator can have the generative AI prioritize providing relaxing topics. If the user is excited, the facilitator can have the generative AI prioritize providing calming topics. If the user is tired, the facilitator can have the generative AI prioritize providing simple topics. By prioritizing conversations based on the user's emotions, more appropriate conversations can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the facilitator may be performed using or without the generative AI. For example, the facilitator can input user emotion data into the generative AI, which can analyze the emotions and determine appropriate conversation priorities.

[0091] The facilitator prioritizes promoting conversations that are highly relevant based on the user's geographical location information during conversation facilitation. The facilitator can analyze the user's geographical location information using generative AI and prioritize promoting appropriate conversations. For example, if the user is in a specific region, the facilitator can offer topics related to that region. If the user is traveling, the facilitator can offer topics related to their travel destination. If the user is at home, the facilitator can offer topics related to their home area. This allows for more appropriate conversations to be provided by prioritizing and promoting highly relevant conversations based on the user's geographical location information. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's geographical location information into the generative AI, which can then prioritize promoting highly relevant conversations.

[0092] The facilitator analyzes the user's social media activity and facilitates relevant conversations during conversation facilitation. The facilitator can use generative AI to analyze the user's social media activity and facilitate appropriate conversations. For example, the facilitator can provide topics related to themes the user frequently posts about on social media. The facilitator can provide topics related to themes that the user's social media friends are interested in. The facilitator can analyze the user's social media activity history and provide relevant topics. This allows the facilitator to prioritize providing relevant conversations by analyzing the user's social media activity. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's social media activity into the generative AI, which can then facilitate relevant conversations.

[0093] The interpretation unit estimates the user's emotions and adjusts the interpretation method of the utterance based on the estimated emotions. The interpretation unit can use a generative AI to analyze the user's emotions and select an appropriate interpretation method for the utterance. For example, if the user is relaxed, the generative AI can interpret the utterance at a relaxed pace. If the user is excited, the generative AI can interpret the utterance in an energetic manner. If the user is tense, the generative AI can interpret the utterance in a calm manner. In this way, by adjusting the interpretation method of the utterance based on the user's emotions, a more appropriate interpretation can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interpretation unit may be performed using a generative AI or not. For example, the interpretation unit can input user emotion data into a generative AI, which can analyze the emotions and select an appropriate interpretation method for the utterance.

[0094] The interpretation unit improves the accuracy of its interpretations by referring to the user's past utterance history when interpreting utterances. The interpretation unit can use a generative AI to analyze the user's past utterance history and select an appropriate method for interpreting utterances. For example, the interpretation unit can interpret relevant utterances based on what the user has said in the past. The interpretation unit can analyze themes of interest from the user's past utterance history and interpret utterances. The interpretation unit can interpret utterances by referring to themes of forums the user has participated in in the past. In this way, the accuracy of interpretation can be improved by referring to the user's past utterance history. Some or all of the above processing in the interpretation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the interpretation unit can input the user's past utterance history into a generative AI, and the generative AI can select an appropriate method for interpreting utterances.

[0095] The interpretation unit interprets utterances while considering the user's attribute information. The interpretation unit can use a generative AI to analyze the user's attribute information and select an appropriate method for interpreting the utterance. For example, the interpretation unit can interpret utterances based on the user's age and gender. The interpretation unit can interpret utterances based on the user's occupation and hobbies. The interpretation unit can interpret utterances based on the user's place of residence and cultural background. This allows for more appropriate interpretations by considering the user's attribute information. Some or all of the above-described processes in the interpretation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the interpretation unit can input the user's attribute information into a generative AI, which can then select an appropriate method for interpreting the utterance.

[0096] The interpretation unit estimates the user's emotions and determines the priority of interpretations based on the estimated emotions. The interpretation unit can use generative AI to analyze the user's emotions and determine the appropriate priority of interpretations. For example, if the user is tense, the interpretation unit can have the generative AI prioritize interpreting statements that promote relaxation. If the user is excited, the interpretation unit can have the generative AI prioritize interpreting statements that promote calmness. If the user is tired, the interpretation unit can have the generative AI prioritize interpreting statements that are simple. By determining the priority of interpretations based on the user's emotions, more appropriate interpretations can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the interpretation unit may be performed using the generative AI or not. For example, the interpretation unit can input user emotion data into the generative AI, which can analyze the emotions and determine the appropriate priority of interpretations.

[0097] The interpretation unit, when interpreting utterances, prioritizes interpreting utterances that are highly relevant based on the user's geographical location information. The interpretation unit can analyze the user's geographical location information using a generative AI and prioritize the interpretation of appropriate utterances. For example, if the user is in a specific region, the interpretation unit can prioritize interpreting utterances related to that region. If the user is traveling, the interpretation unit can prioritize interpreting utterances related to the travel destination. If the user is at home, the interpretation unit can prioritize interpreting utterances related to the area around the user's home. This allows for more appropriate interpretations by prioritizing the interpretation of utterances that are highly relevant based on the user's geographical location information. Some or all of the above processing in the interpretation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the interpretation unit can input the user's geographical location information into a generative AI, which can then prioritize the interpretation of utterances that are highly relevant.

[0098] The interpretation unit analyzes the user's social media activity and interprets relevant statements when interpreting statements. The interpretation unit can use generative AI to analyze the user's social media activity and interpret appropriate statements. For example, the interpretation unit can prioritize interpreting statements related to themes that the user frequently posts about on social media. The interpretation unit can also interpret statements related to themes that the user's social media friends are interested in. The interpretation unit can analyze the user's social media activity history and interpret relevant statements. This allows the interpretation unit to prioritize interpreting relevant statements by analyzing the user's social media activity. Some or all of the above processing in the interpretation unit may be performed using generative AI or not. For example, the interpretation unit can input the user's social media activity into generative AI, which can then interpret relevant statements.

[0099] The service provider estimates the user's emotions and adjusts the content of the advice provided based on the estimated emotions. The service provider can use generative AI to analyze the user's emotions and select appropriate advice. For example, if the user is relaxed, the generative AI can provide advice at a relaxed pace. If the user is in a hurry, the generative AI can provide quick and concise advice. If the user is excited, the generative AI can provide calm advice. By adjusting the content of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without the generative AI. For example, the service provider can input user emotion data into the generative AI, which can analyze the emotions and select appropriate advice.

[0100] The service provider provides optimal advice by referring to the user's past consultation history when providing advice. The service provider can use a generative AI to analyze the user's past consultation history and select appropriate advice. For example, the service provider can provide relevant advice based on the content of past consultations the user has had. The service provider can analyze the user's areas of interest from their past consultation history and provide advice. The service provider can provide optimal advice by referring to advice the user has received in the past. In this way, the service provider can provide optimal advice by referring to the user's past consultation history. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's past consultation history into a generative AI, and the generative AI can select appropriate advice.

[0101] The service provider customizes the content of the advice based on the user's current health condition when providing advice. The service provider can use a generative AI to analyze the user's health condition and select appropriate advice. For example, if the user is tired, the generative AI can provide advice recommending rest. If the user is seeking healthy exercise, the generative AI can provide advice regarding exercise. If the user is feeling unwell, the generative AI can provide advice recommending that the user visit a medical institution. By customizing the content of the advice based on the user's current health condition, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using the generative AI, or it may be performed without using the generative AI. For example, the service provider can input the user's health condition data into the generative AI, and the generative AI can select appropriate advice.

[0102] The service provider estimates the user's emotions and determines the priority of advice based on the estimated emotions. The service provider can use generative AI to analyze the user's emotions and determine the priority of appropriate advice. For example, if the user is tense, the generative AI can prioritize providing advice that helps the user relax. If the user is excited, the generative AI can prioritize providing advice that calms the user. If the user is tired, the generative AI can prioritize providing simple advice. By prioritizing advice based on the user's emotions, more appropriate advice can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without the generative AI. For example, the service provider can input user emotion data into the generative AI, which can analyze the emotions and determine the priority of appropriate advice.

[0103] The service provider provides optimal advice based on the user's geographical location information. The service provider can use a generative AI to analyze the user's geographical location information and select appropriate advice. For example, if the user is in a specific region, the service provider can provide advice related to that region. If the user is traveling, the service provider can provide advice related to their travel destination. If the user is at home, the service provider can provide advice related to their home area. By providing optimal advice based on the user's geographical location information, the service provider can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's geographical location information into a generative AI, which can then select appropriate advice.

[0104] The service provider analyzes the user's social media activity and provides relevant advice when offering advice. The service provider can use generative AI to analyze the user's social media activity and select appropriate advice. For example, the service provider can provide advice related to themes that the user frequently posts about on social media. The service provider can provide advice related to themes that the user's social media friends are interested in. The service provider can analyze the user's social media activity history and provide relevant advice. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's social media activity into generative AI, and the generative AI can select relevant advice.

[0105] The event unit estimates the user's emotions and adjusts the event content based on the estimated emotions. The event unit can use generative AI to analyze the user's emotions and select appropriate event content. For example, if the user is relaxed, the generative AI can provide a relaxed-paced event. If the user is excited, the generative AI can provide an active event. If the user is tense, the generative AI can provide a calm event. In this way, by adjusting the event content based on the user's emotions, more appropriate events can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the event unit may be performed using generative AI or not. For example, the event unit can input user emotion data into a generative AI, which can analyze the emotions and select appropriate event content.

[0106] The Events Department provides the most suitable events when an event is being held, by referring to the user's past participation history. The Events Department can use generative AI to analyze the user's past participation history and select appropriate events. For example, the Events Department can provide relevant events based on events the user has previously participated in. The Events Department can analyze themes of interest from the user's past participation history and provide events accordingly. The Events Department can provide the most suitable events by referring to the user's evaluation of events they have previously participated in. In this way, the most suitable events can be provided by referring to the user's past participation history. Some or all of the above processing in the Events Department may be performed using generative AI, or it may be performed without generative AI. For example, the Events Department can input the user's past participation history into generative AI, and the generative AI can select appropriate events.

[0107] The event unit estimates the user's emotions and prioritizes events based on those emotions. The event unit can use generative AI to analyze the user's emotions and determine the appropriate event priorities. For example, if the user is tense, the generative AI can prioritize events that promote relaxation. If the user is excited, the generative AI can prioritize calming events. If the user is tired, the generative AI can prioritize simple events. This allows for the prioritization of more appropriate events based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the event unit may be performed using or without generative AI. For example, the event unit can input user emotion data into a generative AI, which can analyze the emotions and determine the appropriate event priorities.

[0108] The Events Unit provides the most suitable events based on the user's geographical location information when an event is being held. The Events Unit can use generative AI to analyze the user's geographical location information and select appropriate events. For example, if the user is in a specific region, the Events Unit can provide events related to that region. If the user is traveling, the Events Unit can provide events related to their travel destination. If the user is at home, the Events Unit can provide events related to their home area. By providing the most suitable events based on the user's geographical location information, the Events Unit can provide more appropriate events. Some or all of the above processing in the Events Unit may be performed using generative AI, or it may be performed without generative AI. For example, the Events Unit can input the user's geographical location information into the generative AI, which can then select appropriate events.

[0109] The collaboration unit estimates the user's emotions and adjusts the collaboration method based on the estimated emotions. The collaboration unit can use generative AI to analyze the user's emotions and select an appropriate collaboration method. For example, if the user is relaxed, the generative AI can collaborate at a relaxed pace. If the user is excited, the generative AI can collaborate actively. If the user is tense, the generative AI can collaborate calmly. By adjusting the collaboration method based on the user's emotions, more appropriate collaboration can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using the generative AI or not. For example, the collaboration unit can input user emotion data into the generative AI, which can analyze the emotions and select an appropriate collaboration method.

[0110] The integration unit provides the optimal integration method by referring to the user's past integration history during integration. The integration unit can use a generative AI to analyze the user's past integration history and select an appropriate integration method. For example, the integration unit can provide relevant integration methods based on the content the user has previously integrated with. The integration unit can analyze themes of interest from the user's past integration history and provide integration methods. The integration unit can provide the optimal integration method by referring to an evaluation of the content the user has previously integrated with. In this way, the optimal integration method can be provided by referring to the user's past integration history. Some or all of the above processing in the integration unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the integration unit can input the user's past integration history into a generative AI, and the generative AI can select an appropriate integration method.

[0111] The collaboration unit estimates the user's emotions and determines the priority of collaborations based on the estimated emotions. The collaboration unit can use generative AI to analyze the user's emotions and determine the appropriate priority of collaborations. For example, if the user is tense, the generative AI can prioritize providing relaxing collaborations. If the user is excited, the generative AI can prioritize providing calming collaborations. If the user is tired, the generative AI can prioritize providing simple collaborations. In this way, by determining the priority of collaborations based on the user's emotions, more appropriate collaborations can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using generative AI or not. For example, the collaboration unit can input user emotion data into the generative AI, which can analyze the emotions and determine the appropriate priority of collaborations.

[0112] The integration unit provides the optimal integration method based on the user's geographical location information during integration. The integration unit can analyze the user's geographical location information using a generative AI and select an appropriate integration method. For example, if the user is in a specific region, the integration unit can provide an integration method related to that region. If the user is traveling, the integration unit can provide an integration method related to the travel destination. If the user is at home, the integration unit can provide an integration method related to the area around the user's home. This allows for more appropriate integration by providing the optimal integration method based on the user's geographical location information. Some or all of the above processing in the integration unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the integration unit can input the user's geographical location information into a generative AI, which can then select an appropriate integration method.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The reception desk not only receives user input but can also provide appropriate feedback based on that input. For example, if a user enters a question, the reception desk can use generative AI to provide an immediate answer to that question. Also, if a user enters a message of gratitude, the reception desk can use generative AI to provide an appropriate response. Furthermore, if a user expresses dissatisfaction, the reception desk can use generative AI to suggest solutions to that dissatisfaction. In this way, by providing immediate feedback on user input, user satisfaction can be improved.

[0115] The service provider can not only support the hosting of online events but also collect user feedback and incorporate it into future events. For example, they can conduct a survey of users after the event and use the results to improve the content of the next event. They can also add a feature that allows users to provide real-time feedback during the event. Furthermore, they can analyze user feedback to determine whether specific themes or event formats are popular. This allows them to leverage user feedback to deliver more engaging events.

[0116] The collaboration unit not only facilitates information sharing with family members and caregivers, but can also adjust the timing of collaboration based on the user's daily routine. For example, if the user is a morning person, the collaboration unit can share information during the morning hours. Similarly, if the user is a night owl, the collaboration unit can share information during the evening hours. Furthermore, if the user's daily routine changes, the collaboration unit can automatically adjust the timing of collaboration accordingly. This allows for more effective collaboration by aligning information sharing with the user's daily rhythm.

[0117] The reception desk can accept not only voice and touch input, but also gesture input. For example, if a user waves their hand, the reception desk can recognize the gesture and provide an appropriate response. Similarly, if a user points, the reception desk can perform the action indicated. Furthermore, if a user performs a specific gesture, the reception desk can provide a customized response corresponding to that gesture. This allows for more intuitive operation by accepting gesture input in addition to voice and touch input.

[0118] The promotion team can not only provide themed forums based on users' interests and experiences, but can also suggest new themes based on users' learning history. For example, it can analyze the content of forums that users have previously participated in and suggest related new themes. Furthermore, if a user shows a high level of interest in a particular theme, the promotion team can create a new forum related to that theme. In addition, if a user develops new interests through their activities in the forums, the promotion team can suggest new themes based on those interests. This allows the promotion team to leverage users' learning history to provide a wider variety of themed forums.

[0119] The interpretation unit can not only appropriately interpret the statements of elderly users and convey them clearly to other users, but can also interpret them while considering the tone and nuances of the statements. For example, if an elderly person tells a joke, the interpretation unit can understand the tone of the joke and convey it to other users with humor. Also, if an elderly person expresses gratitude, the interpretation unit can accurately convey the nuance of that gratitude. Furthermore, if an elderly person expresses dissatisfaction, the interpretation unit can consider the tone of that dissatisfaction and make an appropriate interpretation. In this way, by interpreting statements while considering their tone and nuances, more natural communication can be achieved.

[0120] The reception desk can estimate the user's emotions and adjust the timing of input based on those emotions, as well as provide feedback tailored to the user's feelings. For example, if the user is stressed, the reception desk can offer advice to help them relax. If the user is agitated, the reception desk can offer suggestions to calm down. Furthermore, if the user is tired, the reception desk can encourage them to take a break. By providing feedback tailored to the user's emotions, more appropriate support can be provided.

[0121] The reception desk can analyze the user's past input history and select the optimal reception method, as well as learn the user's input patterns to perform predictive input. For example, if a user frequently uses a particular phrase, the reception desk can predict that phrase and complete the input. Also, if a user tends to perform a particular operation at a specific time, the reception desk can suggest an appropriate operation for that time. Furthermore, by learning the user's input patterns, the reception desk can understand the user's intent more accurately and provide an appropriate response. In this way, learning the user's input patterns and performing predictive input enables a smoother user experience.

[0122] The reception system not only filters input based on the user's current areas of interest, but can also track and adapt to changes in the user's areas of interest in real time. For example, if a user begins to show interest in a new topic, the reception system can detect this change and prioritize receiving relevant input. Furthermore, if a user loses interest in a particular topic, the reception system can filter out input related to that topic. By analyzing changes in the user's areas of interest, the reception system can provide customized feedback tailored to the user's interests. This allows for a more personalized service by adapting to changes in the user's areas of interest.

[0123] The reception desk can not only estimate the user's emotions and prioritize the inputs it receives based on those emotions, but it can also provide customized responses tailored to the user's feelings. For example, if the user is nervous, the reception desk can provide a relaxing response. If the user is excited, the reception desk can offer suggestions to calm down. Furthermore, if the user is tired, the reception desk can encourage them to take a break. By providing customized responses tailored to the user's emotions, it is possible to provide more appropriate support.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The reception desk receives user input. The reception desk can accept voice and touch input, analyze user input using generative AI, and perform appropriate processing. Step 2: The Facilitation Department facilitates conversations based on the information received by the Reception Department. The Facilitation Department can use generative AI to provide themed forums based on users' interests and experiences, supporting interaction among users. Step 3: The interpretation unit interprets the conversation facilitated by the facilitation unit and conveys it to other users. The interpretation unit can appropriately interpret the elderly person's statements using generative AI and convey them clearly to other users. It can also perform contextual understanding and semantic analysis. Step 4: The service provider offers health consultations and lifestyle advice. Using generative AI, the service provider can provide appropriate advice based on the user's health condition and lifestyle. It can also support the hosting of online events and collaboration with family members and caregivers.

[0126] 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, facilitation unit, interpretation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts voice and touch operations. The facilitation unit is implemented by the control unit 46A of the smart device 14 and provides themed plazas based on the user's interests and experiences. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and appropriately interprets the statements of elderly people using generating AI and conveys them in an easy-to-understand manner to other users. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health consultations and lifestyle advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, facilitation unit, interpretation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts voice commands. The facilitation unit is implemented by the control unit 46A of the smart glasses 214 and provides themed plazas based on the user's interests and experiences. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generating AI to appropriately interpret the statements of elderly people and convey them to other users in an easy-to-understand manner. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health consultations and lifestyle advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, facilitation unit, interpretation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts voice commands. The facilitation unit is implemented by the control unit 46A of the headset terminal 314 and provides themed forums based on the user's interests and experiences. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generating AI to appropriately interpret the statements of elderly people and convey them to other users in an easy-to-understand manner. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health consultations and lifestyle advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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.

[0178] Each of the multiple elements described above, including the reception unit, facilitation unit, interpretation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts voice commands. The facilitation unit is implemented by the control unit 46A of the robot 414 and provides themed plazas based on the user's interests and experiences. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generating AI to appropriately interpret the statements of elderly people and convey them to other users in an easy-to-understand manner. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health consultations and lifestyle advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A reception area that receives user input, A facilitation unit that facilitates conversation based on the information received by the reception unit, An interpretation unit that interprets the conversation facilitated by the aforementioned facilitation unit and conveys it to other users, It includes a service department that provides health consultations and lifestyle advice. A system characterized by the following features. (Note 2) We have an events department to support the hosting of online events. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a liaison department to support collaboration with family members and caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It accepts voice and touch controls. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned promotion unit is We provide themed forums based on users' interests and experiences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned interpretation section is: Interpreting what elderly people say appropriately and conveying it clearly to other users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned promotion unit is It estimates the user's emotions and adjusts how the conversation is facilitated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned promotion unit is When facilitating a conversation, select the most appropriate facilitation method based on the user's interests and experiences. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned promotion unit is When facilitating a conversation, referencing the user's past conversation history improves the accuracy of the facilitation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned promotion unit is It estimates the user's emotions and determines conversation priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned promotion unit is When facilitating conversations, prioritize relevant conversations based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned promotion unit is When facilitating conversations, the system analyzes the user's social media activity and promotes relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned interpretation section is: It estimates the user's emotions and adjusts how statements are interpreted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned interpretation section is: When interpreting a user's statements, the system improves the accuracy of the interpretation by referencing the user's past statement history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned interpretation section is: When interpreting a statement, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned interpretation section is: It estimates the user's emotions and determines the priority of interpretations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned interpretation section is: When interpreting user statements, the system prioritizes interpreting statements that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned interpretation section is: When interpreting statements, the system analyzes the user's social media activity and interprets relevant statements. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the advice provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, we refer to the user's past consultation history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, customize the advice based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we provide the most suitable advice based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned event section, It estimates the user's emotions and adjusts the event content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned event section, When hosting an event, we provide the most suitable event by referring to the user's past participation history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned event section, It estimates the user's emotions and determines the priority of events based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned event section, During an event, the system provides the most suitable event based on the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, When integrating, the system provides the optimal integration method by referring to the user's past integration history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned linkage unit is, When integrating, the system provides the optimal integration method based on the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0198] 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 reception area that receives user input, A facilitation unit that facilitates conversation based on the information received by the reception unit, An interpretation unit that interprets the conversation facilitated by the aforementioned facilitation unit and conveys it to other users, It includes a service department that provides health consultations and lifestyle advice. A system characterized by the following features.

2. We have an events department to support the hosting of online events. The system according to feature 1.

3. We have a liaison department to support collaboration with family members and caregivers. The system according to feature 1.

4. The aforementioned reception unit is It accepts voice and touch controls. The system according to feature 1.

5. The aforementioned promotion unit is We provide themed forums based on users' interests and experiences. The system according to feature 1.

6. The aforementioned interpretation section is: Interpreting what elderly people say appropriately and conveying it clearly to other users. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

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