system

Abstract

The system according to this embodiment aims to provide information quickly and accurately, and to perform tasks on behalf of others. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a coordination unit, a response unit, and a proxy unit. The reception unit receives questions and requests from users. The generation unit analyzes the questions and requests received by the reception unit and generates an appropriate response. The coordination unit coordinates with the platform provider's service group. The response unit provides the response generated by the generation unit in real time. The proxy unit performs tasks such as making restaurant and hotel reservations, managing schedules, and supporting online shopping.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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, the response time of the concierge service is often delayed, which may reduce the user satisfaction.

[0005] The system according to the embodiment aims to provide information quickly and accurately and to perform tasks on behalf of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a collaboration unit, a response unit, and a proxy unit. The reception unit receives user questions and requests. The generation unit analyzes the questions and requests received by the reception unit and generates an appropriate response. The collaboration unit collaborates with the platform provider's service group. The response unit provides the response generated by the generation unit in real time. The proxy unit performs tasks such as making restaurant and hotel reservations, managing schedules, and supporting online shopping. [Effects of the Invention]

[0007] The system according to this embodiment can provide information quickly and accurately, and perform tasks on behalf of others. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The chatbot-type concierge service according to an embodiment of the present invention is a system that utilizes generative AI to provide information and perform tasks quickly and accurately. This system significantly reduces response times compared to conventional credit card company concierge services and can respond to user requests in real time. Specifically, the generative AI accurately understands user questions and requests using advanced natural language processing technology and generates appropriate responses. Next, it collaborates with various services provided by the platform provider (e.g., news distribution, restaurant recommendations, event information provision, etc.) to respond to diverse user requests. Furthermore, it responds to user requests immediately, minimizing delays. In addition, it performs tasks such as restaurant and hotel reservations, schedule management, and online shopping support. It also provides customer support, technical support, and personal assistance, responding to individual user questions and consultations. As a result, it can significantly reduce response times compared to conventional credit card company concierge services and respond quickly and accurately to diverse user requests. This can improve user satisfaction. It can also be integrated with smart speakers and voice assistant devices. This allows chatbot-based concierge services to respond quickly and accurately to user questions and requests, and to perform various tasks on their behalf.

[0029] The chatbot-type concierge service according to this embodiment comprises a reception unit, a generation unit, a coordination unit, a response unit, and an agency unit. The reception unit receives user questions and requests. User questions and requests include, but are not limited to, restaurant reservations, event information, and requests for technical support. The reception unit receives, for example, text messages entered by the user into the chatbot. The reception unit can also receive voice input. For example, if a user asks a question via a smart speaker, it receives voice data. Furthermore, the reception unit can estimate the user's emotions and adjust the way questions and requests are received based on the estimated emotions. The generation unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate responses. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate answers to user questions. The generation unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is relaxed, it generates a polite and detailed response. The integration unit integrates with the platform provider's service suite. For example, the integration unit can integrate with a news distribution service to provide the latest news. It can also integrate with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, the integration unit can estimate the user's emotions and select services to integrate with based on those estimated emotions. The response unit provides responses generated by the generation unit in real time. For example, the response unit displays text messages through a chatbot interface. The response unit can also provide voice responses. For example, it can provide answers via a smart speaker. Furthermore, the response unit can estimate the user's emotions and adjust how the response is displayed based on those estimated emotions. The proxy unit performs tasks such as making restaurant and hotel reservations, managing schedules, and assisting with online shopping. For example, the proxy unit makes restaurant reservations on behalf of the user. It can also manage the user's schedule and set reminders.Furthermore, the proxy unit can also estimate the user's emotions and determine the priority of tasks to be performed based on the estimated emotions. As a result, the chatbot-type concierge service according to the embodiment can respond quickly and accurately to the user's questions and requests and perform a variety of tasks on their behalf.

[0030] The reception desk receives user questions and requests. These include, but are not limited to, requests for restaurant reservations, event information, and technical support. The reception desk can, for example, receive text messages entered by users into a chatbot. It can also accept voice input. For example, if a user asks a question via a smart speaker, it will receive voice data. Furthermore, the reception desk can estimate the user's emotions and adjust how it handles questions and requests based on those emotions. Specifically, the reception desk uses natural language processing technology to analyze the user's input and accurately understand the intent of the question or request. For example, if a user enters "Tell me about nearby Italian restaurants," the reception desk will extract the keyword "Italian restaurants" and search for nearby restaurants based on location information. In the case of voice input, it will use speech recognition technology to convert the voice data into text and perform a similar analysis. Furthermore, emotion estimation uses technology that estimates the user's emotions from the tone of voice and the expression of the text. For example, if a user is angry, the reception desk will detect that emotion and adjust its response to be more polite and calm. This allows the reception desk to accommodate diverse user input methods and provide more personalized services.

[0031] The generation unit uses a generation AI to analyze questions and requests received by the reception unit and generate appropriate responses. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate answers to user questions. The generation unit can also estimate the user's emotions and adjust the response's expression based on the estimated emotion. For example, if the user is relaxed, it will generate a polite and detailed response. Specifically, the generation AI learns from a large dataset to generate the optimal answer to the user's question. For example, if a user asks, "What's the weather like tonight?", the generation AI will generate an answer based on the latest weather information. Furthermore, the generation AI understands context and can naturally continue a conversation. In addition, emotion estimation uses technology that estimates the user's current emotional state based on user input and past conversation history. For example, if a user inputs, "I'm tired today," the generation AI considers this emotion and generates relaxing suggestions or words of encouragement. This allows the generation unit to provide quick and appropriate responses to user questions and requests, improving user satisfaction.

[0032] The integration unit connects with the platform provider's service suite. For example, it can connect with a news distribution service to provide the latest news. It can also connect with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, the integration unit can estimate the user's emotions and select services to connect with based on those estimated emotions. Specifically, the integration unit communicates with external services via APIs to obtain necessary information. For example, when connecting with a news distribution service, it obtains the latest news articles via the API and provides them to the user. When connecting with a restaurant recommendation service, it recommends the most suitable restaurant based on the user's preferences and past history. In addition, emotion estimation uses technology that estimates the user's current emotional state based on the user's input and past conversation history. For example, if a user inputs "Today is a special day, so I'm looking for a good restaurant," the integration unit takes that emotion into consideration and recommends a restaurant suitable for a special day. This allows the integration unit to respond to diverse user needs and provide more personalized services.

[0033] The response unit provides responses generated by the generation unit in real time. The response unit can, for example, display text messages through a chatbot interface. The response unit can also provide voice responses, for example, by providing answers via a smart speaker. Furthermore, the response unit can estimate the user's emotions and adjust the display method of the response based on the estimated emotions. Specifically, the response unit provides responses in the optimal format according to the user's device. For example, it displays text messages to users using smartphones and provides voice responses to users using smart speakers. In addition, emotion estimation uses technology that estimates the user's current emotional state based on the user's input and past conversation history. For example, if a user inputs "I'm tired today," the response unit takes that emotion into consideration and displays relaxing suggestions or words of encouragement. This allows the response unit to respond quickly and appropriately to diverse user requests and improve user satisfaction.

[0034] The proxy service handles tasks such as making restaurant and hotel reservations, managing schedules, and assisting with online shopping. For example, the proxy service can make restaurant reservations on behalf of users. It can also manage users' schedules and set reminders. Furthermore, the proxy service can estimate users' emotions and prioritize tasks based on those emotions. Specifically, the proxy service integrates with external reservation systems and calendar apps to automatically execute tasks according to user requests. For example, when making a restaurant reservation, the proxy service selects the most suitable restaurant based on the user's preferences and past history, and completes the reservation. In terms of schedule management, it centrally manages users' schedules and sets reminders for important events and tasks. In addition, emotion estimation uses technology that estimates the user's current emotional state based on user input and past conversation history. For example, if a user inputs "I'm busy today," the proxy service takes that emotion into consideration and prioritizes important tasks. This allows the proxy service to respond quickly and appropriately to diverse user requests, making users' lives more convenient.

[0035] The support department can provide individual support such as customer support, technical support, and personal assistance. For example, the support department can provide solutions to users' technical problems. It can also provide detailed answers to users' individual questions. Furthermore, the support department can estimate the user's emotions and adjust the way support is provided based on those emotions. This allows the support department to address diverse user needs by providing individualized support. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input a user's question into an AI, which can then generate an appropriate answer.

[0036] The integration unit can integrate with services such as news distribution, restaurant recommendations, and event information provision. For example, the integration unit can integrate with a news distribution service to provide the latest news. It can also integrate with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, it can integrate with an event information provision service to provide event information of interest to the user. In this way, by integrating with a variety of services, it can respond to the diverse needs of users. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input news data obtained from a news distribution service into an AI, which can then select news suitable for the user.

[0037] The proxy service can make restaurant and hotel reservations on behalf of users. For example, the proxy service can make restaurant reservations on behalf of users. It can also make hotel reservations on behalf of users. Furthermore, the proxy service can estimate the user's emotions and determine reservation priorities based on those emotions. This improves user convenience by providing restaurant and hotel reservation services. Some or all of the above processes in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can input the user's reservation request into the AI, which can then make an appropriate reservation.

[0038] The proxy unit can perform schedule management. For example, the proxy unit can manage the user's schedule and set reminders. It can also adjust the user's schedule and avoid overlaps. Furthermore, the proxy unit can estimate the user's emotions and determine schedule priorities based on the estimated emotions. In this way, by managing schedules, it assists the user in managing their time. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's schedule data into AI, and the AI ​​can generate an appropriate schedule.

[0039] The proxy service can provide online shopping support. For example, the proxy service can shop online on behalf of the user. It can also recommend appropriate products based on the user's purchase history. Furthermore, the proxy service can estimate the user's emotions and determine shopping priorities based on those estimated emotions. In this way, by providing online shopping support, it improves the user's shopping experience. Some or all of the above processes in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can input the user's purchase request into AI, which can then select appropriate products.

[0040] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions the content of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the content of questions the user will use at specific times based on their past question history. This improves user convenience by providing the optimal reception method based on past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's question history data into AI, which can then generate the optimal reception method.

[0041] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is in their current location, the reception desk will prioritize displaying information relevant to that location. The reception desk can also prioritize receiving relevant questions and requests based on the user's areas of interest. Furthermore, the reception desk can suggest appropriate questions and requests based on the user's current situation (e.g., time of day, weather). This allows for the provision of more relevant information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation data into the AI, which can then perform appropriate filtering.

[0042] The reception desk can prioritize receiving questions and requests that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving information related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions and requests related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving information about their home area. This allows the reception desk to provide users with highly relevant information by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then perform appropriate filtering.

[0043] The reception desk can analyze a user's social media activity when receiving questions or requests and accept relevant questions or requests. For example, the reception desk can accept relevant questions or requests based on the topics the user is discussing on social media. The reception desk can also analyze the content of a user's social media posts and provide relevant information. Furthermore, the reception desk can accept relevant questions or requests based on the activity of the user's social media followers and friends. This allows for the provision of information based on the user's interests by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into an AI, which can then perform appropriate filtering.

[0044] The generation unit can adjust the level of detail in the response based on the importance of the question or request when generating the response. For example, the generation unit can generate a detailed response for high-importance questions. It can also generate a concise response for low-importance questions. Furthermore, the generation unit can generate a response with an appropriate level of detail depending on the content of the question. This allows for an appropriate response to user requests by providing responses that match the importance of the questions or requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into the AI, and the AI ​​can generate a response with an appropriate level of detail.

[0045] The generation unit can apply different response algorithms depending on the category of the question or request when generating a response. For example, the generation unit can apply a specialized response algorithm to technical questions. It can also apply a response algorithm specialized for customer support to customer support questions. Furthermore, it can apply a response algorithm specialized for individual support to personal assistance questions. By applying a response algorithm according to the category, a more appropriate response can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into the AI, and the AI ​​can apply an appropriate response algorithm.

[0046] The generation unit can determine the priority of responses based on when the questions or requests were submitted. For example, the generation unit can generate responses with the highest priority for urgent questions. It can also generate responses with normal priority for regular questions. Furthermore, the generation unit can postpone the generation of responses for past questions. This allows for a quick response to urgent requests by setting response priorities according to the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question submission timing data into the AI, which can then generate appropriate priorities.

[0047] The generation unit can adjust the order of responses based on the relevance of the questions and requests when generating responses. For example, the generation unit can prioritize generating responses to highly relevant questions. It can also postpone generating responses to less relevant questions. Furthermore, the generation unit can generate responses in an appropriate order depending on the content of the questions. This allows for an appropriate response to user requests by setting the order of responses according to relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into the AI, and the AI ​​can generate responses in an appropriate order.

[0048] The integration unit can select the optimal integration method by referring to the service provision history during integration. For example, the integration unit can select the optimal integration method based on the history of services used in the past. The integration unit can also select an integration method that suits the user's preferences from the service provision history. Furthermore, the integration unit can analyze the service provision history and select the most efficient integration method. This improves user convenience by providing the optimal integration method based on the service provision history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input service provision history data into AI, and the AI ​​can generate an appropriate integration method.

[0049] The integration unit can apply different integration methods depending on the service category during integration. For example, for entertainment-related services, the integration unit can apply an entertainment-specific integration method. Furthermore, for business-related services, the integration unit can apply a business-specific integration method. In addition, for relaxation-related services, the integration unit can apply a relaxation-specific integration method. This allows for the provision of more appropriate services by applying the appropriate integration method according to the category. Some or all of the above processing in the integration unit may be performed using AI, or without AI. For example, the integration unit can input service category data into the AI, which can then apply the appropriate integration method.

[0050] The integration unit can determine the priority of integration based on the timing of service delivery. For example, the integration unit will prioritize urgent services. It can also integrate regular services with normal priorities. Furthermore, it can postpone integrating past services. This allows for a rapid response to urgent services by setting integration priorities according to the timing of service delivery. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input service delivery timing data into AI, which can then generate appropriate priorities.

[0051] The integration unit can adjust the order of integration based on the relevance of the services. For example, the integration unit will prioritize integration with highly relevant services. It can also postpone integration with less relevant services. Furthermore, the integration unit can integrate in an appropriate order depending on the content of the services. This allows for appropriate responses to user requests by setting the integration order according to relevance. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input service relevance data into the AI, which can then perform integration in an appropriate order.

[0052] The response unit can select the optimal display method by referring to the user's past response history when displaying a response. For example, the response unit can select the optimal display method based on the display methods the user has used in the past. The response unit can also select a display method that suits the user's preferences from the user's past response history. Furthermore, the response unit can analyze the user's past response history and select the most efficient display method. This improves user convenience by providing the optimal display method based on past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's response history data into AI, and the AI ​​can generate an appropriate display method.

[0053] The response unit can customize the display method of the response based on the user's current situation when displaying a response. For example, if the user is on the move, the response unit can provide a concise and highly visible display method. If the user is at home, the response unit can also provide a display method that includes detailed information. Furthermore, if the user is in a meeting, the response unit can provide a quiet notification method. This improves user convenience by providing a display method that is appropriate to the current situation. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's current situation data into the AI, and the AI ​​can generate an appropriate display method.

[0054] The response unit can select the optimal display method when displaying a response, taking into account the user's device information. For example, if the user is using a smartphone, the response unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the response unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the response unit can provide a concise and highly visible display method. This improves user convenience by providing a display method tailored to the device information. Some or all of the above processing in the response unit may be performed using AI, or without AI. For example, the response unit can input the user's device information into the AI, which can then generate an appropriate display method.

[0055] The response unit can analyze the user's social media activity and propose a means of displaying the response when displaying a response. For example, the response unit can prioritize displaying relevant responses based on the topics the user is discussing on social media. The response unit can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the response unit can prioritize displaying relevant responses based on the activities of the user's social media followers and friends. In this way, by analyzing social media activity, it is possible to provide a means of displaying responses based on the user's interests. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's social media data into AI, and the AI ​​can generate an appropriate means of displaying responses.

[0056] The task execution unit can select the optimal task execution method by referring to the user's past task history when executing a task. For example, the task execution unit can select the optimal task execution method based on the history of tasks previously requested by the user. The task execution unit can also select a task execution method that suits the user's preferences from their past task history. Furthermore, the task execution unit can analyze the user's past task history and select the most efficient task execution method. This improves user convenience by providing the optimal task execution method based on past task history. Some or all of the above processing in the task execution unit may be performed using AI, for example, or without AI. For example, the task execution unit can input the user's task history data into AI, which can then generate an appropriate task execution method.

[0057] The proxy unit can customize the proxy means based on the user's current situation when executing a proxy task. For example, if the user is on the move, the proxy unit can provide a proxy means that can respond quickly. Also, if the user is at home, the proxy unit can provide a proxy means that includes detailed information. Furthermore, if the user is in a meeting, the proxy unit can provide a quiet notification method. This improves user convenience by providing a proxy means that is appropriate to the current situation. Some or all of the above processing in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's current situation data into the AI, and the AI ​​can generate an appropriate proxy means.

[0058] The proxy unit can select the optimal proxy method when executing a proxy task, taking into account the user's geographical location information. For example, if the user is in a specific region, the proxy unit will prioritize proxy tasks related to that region. Furthermore, if the user is traveling, the proxy unit can prioritize proxy tasks related to the travel destination. Additionally, if the user is at home, the proxy unit can prioritize proxy tasks around their home. This allows the proxy unit to provide highly relevant proxy methods by considering geographical location information. Some or all of the above processing in the proxy unit may be performed using AI, for example, or without AI. For instance, the proxy unit can input the user's geographical location information into AI, which can then generate an appropriate proxy method.

[0059] The proxy unit can analyze the user's social media activity and suggest proxy methods when executing proxy tasks. For example, the proxy unit can prioritize executing relevant proxy tasks based on the topics the user is discussing on social media. The proxy unit can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the proxy unit can prioritize executing relevant proxy tasks based on the activities of the user's social media followers and friends. In this way, by analyzing social media activity, it can provide proxy methods based on the user's interests. Some or all of the above processing in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's social media data into AI, and the AI ​​can generate appropriate proxy methods.

[0060] The support department can select the optimal support method by referring to the user's past support history when providing support. For example, the support department can select the optimal support method based on the support methods the user has used in the past. The support department can also select a support method that suits the user's preferences from their past support history. Furthermore, the support department can analyze the user's past support history and select the most efficient support method. This improves user convenience by providing the optimal support method based on past support history. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the user's support history data into AI, and the AI ​​can generate an appropriate support method.

[0061] The support unit can select the optimal support method when providing support, taking into account the user's device information. For example, if the user is using a smartphone, the support unit can provide a support method that is adapted to the screen size. Furthermore, if the user is using a tablet, the support unit can provide a support method optimized for a larger screen. In addition, if the user is using a smartwatch, the support unit can provide a concise and highly visible support method. This improves user convenience by providing support methods tailored to device information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's device information into AI, which can then generate an appropriate support method.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk can analyze a user's past question history and select the optimal reception method. For example, it can automatically display as suggestions the types of questions the user has frequently asked in the past. It can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, it can predict and suggest questions that the user may ask during specific time periods based on their past question history. This improves user convenience by providing the optimal reception method based on past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's question history data into an AI, which can then generate the optimal reception method.

[0064] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is currently located, it will prioritize displaying information related to that location. It can also prioritize receiving relevant questions and requests based on the user's areas of interest. Furthermore, it can suggest appropriate questions and requests based on the user's current situation (e.g., time of day, weather). This allows for the provision of more relevant information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation data into the AI, which can then perform appropriate filtering.

[0065] The reception desk can prioritize receiving questions and requests that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving information related to that region. Similarly, if the user is traveling, it can prioritize receiving questions and requests related to their travel destination. Furthermore, if the user is at home, it can prioritize receiving information about their surroundings. This allows the reception desk to provide users with information that is highly relevant to them by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then perform appropriate filtering.

[0066] The generation unit can adjust the level of detail in responses based on the importance of the questions and requests when generating responses. For example, it can generate detailed responses for high-importance questions, and concise responses for low-importance questions. Furthermore, it can generate responses with appropriate levels of detail depending on the content of the question. This allows for appropriate responses to user requests by providing responses that match the importance of the questions and requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into the AI, and the AI ​​can generate responses with appropriate levels of detail.

[0067] The generation unit can apply different response algorithms depending on the category of the question or request when generating a response. For example, a specialized response algorithm can be applied to technical questions. A response algorithm specifically designed for customer support can also be applied to customer support questions. Furthermore, a response algorithm specifically designed for individual support can be applied to personal assistance questions. By applying a response algorithm according to the category, a more appropriate response can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into the AI, and the AI ​​can apply an appropriate response algorithm.

[0068] The generation unit can determine the priority of responses based on when the questions or requests were submitted. For example, it can generate responses with the highest priority for urgent questions. It can also generate responses with normal priority for regular questions. Furthermore, it can postpone the generation of responses for past questions. This allows for a quick response to urgent requests by setting response priorities according to the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question submission timing data into the AI, which can then generate appropriate priorities.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives user questions and requests. These include requests for restaurant reservations, event information, and technical support. The reception desk can receive text messages and voice input from users via a chatbot. Furthermore, the reception desk can estimate the user's emotions and adjust how questions and requests are received based on those emotions. Step 2: The generation unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate responses. The generation AI uses a text generation AI (e.g., LLM) to generate answers to the user's questions. The generation unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. Step 3: The integration unit integrates with the platform provider's service suite. For example, it can integrate with a news distribution service to provide the latest news. It can also integrate with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, the integration unit can estimate the user's emotions and select services to integrate with based on those estimated emotions. Step 4: The response unit provides the response generated by the generation unit in real time. The response unit displays the text message through the chatbot interface. It can also provide a voice response. Furthermore, the response unit can estimate the user's emotions and adjust how the response is displayed based on the estimated emotions. Step 5: The proxy service handles tasks such as making restaurant and hotel reservations, managing schedules, and assisting with online shopping. For example, it can make restaurant reservations on behalf of the user. It can also manage the user's schedule and set reminders. Furthermore, the proxy service can estimate the user's emotions and prioritize tasks based on those estimated emotions.

[0071] (Example of form 2) The chatbot-type concierge service according to an embodiment of the present invention is a system that utilizes generative AI to provide information and perform tasks quickly and accurately. This system significantly reduces response times compared to conventional credit card company concierge services and can respond to user requests in real time. Specifically, the generative AI accurately understands user questions and requests using advanced natural language processing technology and generates appropriate responses. Next, it collaborates with various services provided by the platform provider (e.g., news distribution, restaurant recommendations, event information provision, etc.) to respond to diverse user requests. Furthermore, it responds to user requests immediately, minimizing delays. In addition, it performs tasks such as restaurant and hotel reservations, schedule management, and online shopping support. It also provides customer support, technical support, and personal assistance, responding to individual user questions and consultations. As a result, it can significantly reduce response times compared to conventional credit card company concierge services and respond quickly and accurately to diverse user requests. This can improve user satisfaction. It can also be integrated with smart speakers and voice assistant devices. This allows chatbot-based concierge services to respond quickly and accurately to user questions and requests, and to perform various tasks on their behalf.

[0072] The chatbot-type concierge service according to this embodiment comprises a reception unit, a generation unit, a coordination unit, a response unit, and an agency unit. The reception unit receives user questions and requests. User questions and requests include, but are not limited to, restaurant reservations, event information, and requests for technical support. The reception unit receives, for example, text messages entered by the user into the chatbot. The reception unit can also receive voice input. For example, if a user asks a question via a smart speaker, it receives voice data. Furthermore, the reception unit can estimate the user's emotions and adjust the way questions and requests are received based on the estimated emotions. The generation unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate responses. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate answers to user questions. The generation unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is relaxed, it generates a polite and detailed response. The integration unit integrates with the platform provider's service suite. For example, the integration unit can integrate with a news distribution service to provide the latest news. It can also integrate with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, the integration unit can estimate the user's emotions and select services to integrate with based on those estimated emotions. The response unit provides responses generated by the generation unit in real time. For example, the response unit displays text messages through a chatbot interface. The response unit can also provide voice responses. For example, it can provide answers via a smart speaker. Furthermore, the response unit can estimate the user's emotions and adjust how the response is displayed based on those estimated emotions. The proxy unit performs tasks such as making restaurant and hotel reservations, managing schedules, and assisting with online shopping. For example, the proxy unit makes restaurant reservations on behalf of the user. It can also manage the user's schedule and set reminders.Furthermore, the proxy unit can also estimate the user's emotions and determine the priority of tasks to be performed based on the estimated emotions. As a result, the chatbot-type concierge service according to the embodiment can respond quickly and accurately to the user's questions and requests and perform a variety of tasks on their behalf.

[0073] The reception desk receives user questions and requests. These include, but are not limited to, requests for restaurant reservations, event information, and technical support. The reception desk can, for example, receive text messages entered by users into a chatbot. It can also accept voice input. For example, if a user asks a question via a smart speaker, it will receive voice data. Furthermore, the reception desk can estimate the user's emotions and adjust how it handles questions and requests based on those emotions. Specifically, the reception desk uses natural language processing technology to analyze the user's input and accurately understand the intent of the question or request. For example, if a user enters "Tell me about nearby Italian restaurants," the reception desk will extract the keyword "Italian restaurants" and search for nearby restaurants based on location information. In the case of voice input, it will use speech recognition technology to convert the voice data into text and perform a similar analysis. Furthermore, emotion estimation uses technology that estimates the user's emotions from the tone of voice and the expression of the text. For example, if a user is angry, the reception desk will detect that emotion and adjust its response to be more polite and calm. This allows the reception desk to accommodate diverse user input methods and provide more personalized services.

[0074] The generation unit uses a generation AI to analyze questions and requests received by the reception unit and generate appropriate responses. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate answers to user questions. The generation unit can also estimate the user's emotions and adjust the response's expression based on the estimated emotion. For example, if the user is relaxed, it will generate a polite and detailed response. Specifically, the generation AI learns from a large dataset to generate the optimal answer to the user's question. For example, if a user asks, "What's the weather like tonight?", the generation AI will generate an answer based on the latest weather information. Furthermore, the generation AI understands context and can naturally continue a conversation. In addition, emotion estimation uses technology that estimates the user's current emotional state based on user input and past conversation history. For example, if a user inputs, "I'm tired today," the generation AI considers this emotion and generates relaxing suggestions or words of encouragement. This allows the generation unit to provide quick and appropriate responses to user questions and requests, improving user satisfaction.

[0075] The integration unit connects with the platform provider's service suite. For example, it can connect with a news distribution service to provide the latest news. It can also connect with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, the integration unit can estimate the user's emotions and select services to connect with based on those estimated emotions. Specifically, the integration unit communicates with external services via APIs to obtain necessary information. For example, when connecting with a news distribution service, it obtains the latest news articles via the API and provides them to the user. When connecting with a restaurant recommendation service, it recommends the most suitable restaurant based on the user's preferences and past history. In addition, emotion estimation uses technology that estimates the user's current emotional state based on the user's input and past conversation history. For example, if a user inputs "Today is a special day, so I'm looking for a good restaurant," the integration unit takes that emotion into consideration and recommends a restaurant suitable for a special day. This allows the integration unit to respond to diverse user needs and provide more personalized services.

[0076] The response unit provides responses generated by the generation unit in real time. The response unit can, for example, display text messages through a chatbot interface. The response unit can also provide voice responses, for example, by providing answers via a smart speaker. Furthermore, the response unit can estimate the user's emotions and adjust the display method of the response based on the estimated emotions. Specifically, the response unit provides responses in the optimal format according to the user's device. For example, it displays text messages to users using smartphones and provides voice responses to users using smart speakers. In addition, emotion estimation uses technology that estimates the user's current emotional state based on the user's input and past conversation history. For example, if a user inputs "I'm tired today," the response unit takes that emotion into consideration and displays relaxing suggestions or words of encouragement. This allows the response unit to respond quickly and appropriately to diverse user requests and improve user satisfaction.

[0077] The proxy service handles tasks such as making restaurant and hotel reservations, managing schedules, and assisting with online shopping. For example, the proxy service can make restaurant reservations on behalf of users. It can also manage users' schedules and set reminders. Furthermore, the proxy service can estimate users' emotions and prioritize tasks based on those emotions. Specifically, the proxy service integrates with external reservation systems and calendar apps to automatically execute tasks according to user requests. For example, when making a restaurant reservation, the proxy service selects the most suitable restaurant based on the user's preferences and past history, and completes the reservation. In terms of schedule management, it centrally manages users' schedules and sets reminders for important events and tasks. In addition, emotion estimation uses technology that estimates the user's current emotional state based on user input and past conversation history. For example, if a user inputs "I'm busy today," the proxy service takes that emotion into consideration and prioritizes important tasks. This allows the proxy service to respond quickly and appropriately to diverse user requests, making users' lives more convenient.

[0078] The support department can provide individual support such as customer support, technical support, and personal assistance. For example, the support department can provide solutions to users' technical problems. It can also provide detailed answers to users' individual questions. Furthermore, the support department can estimate the user's emotions and adjust the way support is provided based on those emotions. This allows the support department to address diverse user needs by providing individualized support. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input a user's question into an AI, which can then generate an appropriate answer.

[0079] The integration unit can integrate with services such as news distribution, restaurant recommendations, and event information provision. For example, the integration unit can integrate with a news distribution service to provide the latest news. It can also integrate with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, it can integrate with an event information provision service to provide event information of interest to the user. In this way, by integrating with a variety of services, it can respond to the diverse needs of users. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input news data obtained from a news distribution service into an AI, which can then select news suitable for the user.

[0080] The proxy service can make restaurant and hotel reservations on behalf of users. For example, the proxy service can make restaurant reservations on behalf of users. It can also make hotel reservations on behalf of users. Furthermore, the proxy service can estimate the user's emotions and determine reservation priorities based on those emotions. This improves user convenience by providing restaurant and hotel reservation services. Some or all of the above processes in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can input the user's reservation request into the AI, which can then make an appropriate reservation.

[0081] The proxy unit can perform schedule management. For example, the proxy unit can manage the user's schedule and set reminders. It can also adjust the user's schedule and avoid overlaps. Furthermore, the proxy unit can estimate the user's emotions and determine schedule priorities based on the estimated emotions. In this way, by managing schedules, it assists the user in managing their time. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's schedule data into AI, and the AI ​​can generate an appropriate schedule.

[0082] The proxy service can provide online shopping support. For example, the proxy service can shop online on behalf of the user. It can also recommend appropriate products based on the user's purchase history. Furthermore, the proxy service can estimate the user's emotions and determine shopping priorities based on those estimated emotions. In this way, by providing online shopping support, it improves the user's shopping experience. Some or all of the above processes in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can input the user's purchase request into AI, which can then select appropriate products.

[0083] The reception desk can estimate the user's emotions and adjust how questions and requests are handled based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of questions and requests. This improves user satisfaction by providing a reception method that is tailored to 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 desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then generate an appropriate reception method.

[0084] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions the content of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the content of questions the user will use at specific times based on their past question history. This improves user convenience by providing the optimal reception method based on past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's question history data into AI, which can then generate the optimal reception method.

[0085] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is in their current location, the reception desk will prioritize displaying information relevant to that location. The reception desk can also prioritize receiving relevant questions and requests based on the user's areas of interest. Furthermore, the reception desk can suggest appropriate questions and requests based on the user's current situation (e.g., time of day, weather). This allows for the provision of more relevant information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation data into the AI, which can then perform appropriate filtering.

[0086] The reception desk can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated emotions. For example, if the user has an urgent request, the reception desk will receive that request with the highest priority. If the user is relaxed, the reception desk can also receive the request with the normal priority. Furthermore, if the user is stressed, the reception desk can prioritize high-priority requests. This allows for the rapid processing of important requests by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can then generate appropriate priorities.

[0087] The reception desk can prioritize receiving questions and requests that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving information related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions and requests related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving information about their home area. This allows the reception desk to provide users with highly relevant information by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then perform appropriate filtering.

[0088] The reception desk can analyze a user's social media activity when receiving questions or requests and accept relevant questions or requests. For example, the reception desk can accept relevant questions or requests based on the topics the user is discussing on social media. The reception desk can also analyze the content of a user's social media posts and provide relevant information. Furthermore, the reception desk can accept relevant questions or requests based on the activity of the user's social media followers and friends. This allows for the provision of information based on the user's interests by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into an AI, which can then perform appropriate filtering.

[0089] The generation unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a polite and detailed response. If the user is in a hurry, the generation unit can also generate a concise and quick response. Furthermore, if the user is stressed, the generation unit can generate a response using gentle language. This improves user satisfaction by providing responses that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can generate an appropriate response.

[0090] The generation unit can adjust the level of detail in the response based on the importance of the question or request when generating the response. For example, the generation unit can generate a detailed response for high-importance questions. It can also generate a concise response for low-importance questions. Furthermore, the generation unit can generate a response with an appropriate level of detail depending on the content of the question. This allows for an appropriate response to user requests by providing responses that match the importance of the questions or requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into the AI, and the AI ​​can generate a response with an appropriate level of detail.

[0091] The generation unit can apply different response algorithms depending on the category of the question or request when generating a response. For example, the generation unit can apply a specialized response algorithm to technical questions. It can also apply a response algorithm specialized for customer support to customer support questions. Furthermore, it can apply a response algorithm specialized for individual support to personal assistance questions. By applying a response algorithm according to the category, a more appropriate response can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into the AI, and the AI ​​can apply an appropriate response algorithm.

[0092] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can also generate a longer response that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a response with visually stimulating effects. This improves user satisfaction by providing response lengths that match the user's emotions. 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI, which can then generate an appropriate response length.

[0093] The generation unit can determine the priority of responses based on when the questions or requests were submitted. For example, the generation unit can generate responses with the highest priority for urgent questions. It can also generate responses with normal priority for regular questions. Furthermore, the generation unit can postpone the generation of responses for past questions. This allows for a quick response to urgent requests by setting response priorities according to the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question submission timing data into the AI, which can then generate appropriate priorities.

[0094] The generation unit can adjust the order of responses based on the relevance of the questions and requests when generating responses. For example, the generation unit can prioritize generating responses to highly relevant questions. It can also postpone generating responses to less relevant questions. Furthermore, the generation unit can generate responses in an appropriate order depending on the content of the questions. This allows for an appropriate response to user requests by setting the order of responses according to relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into the AI, and the AI ​​can generate responses in an appropriate order.

[0095] The integration unit can estimate the user's emotions and select services to integrate with based on the estimated emotions. For example, if the user is relaxed, the integration unit will prioritize integrating entertainment-related services. If the user is in a hurry, the integration unit can also prioritize services that can provide a quick response. Furthermore, if the user is stressed, the integration unit can prioritize integrating relaxation-related services. This improves user satisfaction by providing services that match 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 integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI, which can then select appropriate services.

[0096] The integration unit can select the optimal integration method by referring to the service provision history during integration. For example, the integration unit can select the optimal integration method based on the history of services used in the past. The integration unit can also select an integration method that suits the user's preferences from the service provision history. Furthermore, the integration unit can analyze the service provision history and select the most efficient integration method. This improves user convenience by providing the optimal integration method based on the service provision history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input service provision history data into AI, and the AI ​​can generate an appropriate integration method.

[0097] The integration unit can apply different integration methods depending on the service category during integration. For example, for entertainment-related services, the integration unit can apply an entertainment-specific integration method. Furthermore, for business-related services, the integration unit can apply a business-specific integration method. In addition, for relaxation-related services, the integration unit can apply a relaxation-specific integration method. This allows for the provision of more appropriate services by applying the appropriate integration method according to the category. Some or all of the above processing in the integration unit may be performed using AI, or without AI. For example, the integration unit can input service category data into the AI, which can then apply the appropriate integration method.

[0098] The integration unit can estimate the user's emotions and determine the priority of services to integrate based on the estimated emotions. For example, if the user has an urgent request, the integration unit will prioritize services related to that request. If the user is relaxed, the integration unit can integrate with normal priorities. Furthermore, if the user is stressed, the integration unit can prioritize services of high importance. This allows for the rapid delivery of important services by setting priorities according to 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 integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI, which can then generate appropriate priorities.

[0099] The integration unit can determine the priority of integration based on the timing of service delivery. For example, the integration unit will prioritize urgent services. It can also integrate regular services with normal priorities. Furthermore, it can postpone integrating past services. This allows for a rapid response to urgent services by setting integration priorities according to the timing of service delivery. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input service delivery timing data into AI, which can then generate appropriate priorities.

[0100] The integration unit can adjust the order of integration based on the relevance of the services. For example, the integration unit will prioritize integration with highly relevant services. It can also postpone integration with less relevant services. Furthermore, the integration unit can integrate in an appropriate order depending on the content of the services. This allows for appropriate responses to user requests by setting the integration order according to relevance. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input service relevance data into the AI, which can then perform integration in an appropriate order.

[0101] The response unit can estimate the user's emotions and adjust the display method of the response based on the estimated user emotions. For example, if the user is nervous, the response unit can provide a simple and highly visible display method. If the user is relaxed, the response unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the response unit can provide a concise display method. This improves user satisfaction by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into the generative AI, and the generative AI can generate an appropriate display method.

[0102] The response unit can select the optimal display method by referring to the user's past response history when displaying a response. For example, the response unit can select the optimal display method based on the display methods the user has used in the past. The response unit can also select a display method that suits the user's preferences from the user's past response history. Furthermore, the response unit can analyze the user's past response history and select the most efficient display method. This improves user convenience by providing the optimal display method based on past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's response history data into AI, and the AI ​​can generate an appropriate display method.

[0103] The response unit can customize the display method of the response based on the user's current situation when displaying a response. For example, if the user is on the move, the response unit can provide a concise and highly visible display method. If the user is at home, the response unit can also provide a display method that includes detailed information. Furthermore, if the user is in a meeting, the response unit can provide a quiet notification method. This improves user convenience by providing a display method that is appropriate to the current situation. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's current situation data into the AI, and the AI ​​can generate an appropriate display method.

[0104] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user has an urgent request, the response unit will display the response to that request with the highest priority. The response unit can also display responses with normal priority if the user is relaxed. Furthermore, if the user is stressed, the response unit can prioritize the display of high-priority responses. This allows for the rapid display of important responses by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 response unit may be performed using AI or not using AI. For example, the response unit can input user emotion data into a generative AI, which can then generate appropriate priorities.

[0105] The response unit can select the optimal display method when displaying a response, taking into account the user's device information. For example, if the user is using a smartphone, the response unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the response unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the response unit can provide a concise and highly visible display method. This improves user convenience by providing a display method tailored to the device information. Some or all of the above processing in the response unit may be performed using AI, or without AI. For example, the response unit can input the user's device information into the AI, which can then generate an appropriate display method.

[0106] The response unit can analyze the user's social media activity and propose a means of displaying the response when displaying a response. For example, the response unit can prioritize displaying relevant responses based on the topics the user is discussing on social media. The response unit can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the response unit can prioritize displaying relevant responses based on the activities of the user's social media followers and friends. In this way, by analyzing social media activity, it is possible to provide a means of displaying responses based on the user's interests. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's social media data into AI, and the AI ​​can generate an appropriate means of displaying responses.

[0107] The task manager can estimate the user's emotions and determine the priority of tasks to be performed based on the estimated emotions. For example, if the user requests an urgent task, the task manager will prioritize that task. If the user is relaxed, the task manager can also perform tasks with normal priority. Furthermore, if the user is stressed, the task manager can prioritize high-priority tasks. This allows for the rapid performance of important tasks by setting priorities according to 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 task manager may be performed using AI or not. For example, the task manager can input user emotion data into a generative AI, which can then generate appropriate priorities.

[0108] The task execution unit can select the optimal task execution method by referring to the user's past task history when executing a task. For example, the task execution unit can select the optimal task execution method based on the history of tasks previously requested by the user. The task execution unit can also select a task execution method that suits the user's preferences from their past task history. Furthermore, the task execution unit can analyze the user's past task history and select the most efficient task execution method. This improves user convenience by providing the optimal task execution method based on past task history. Some or all of the above processing in the task execution unit may be performed using AI, for example, or without AI. For example, the task execution unit can input the user's task history data into AI, which can then generate an appropriate task execution method.

[0109] The proxy unit can customize the proxy means based on the user's current situation when executing a proxy task. For example, if the user is on the move, the proxy unit can provide a proxy means that can respond quickly. Also, if the user is at home, the proxy unit can provide a proxy means that includes detailed information. Furthermore, if the user is in a meeting, the proxy unit can provide a quiet notification method. This improves user convenience by providing a proxy means that is appropriate to the current situation. Some or all of the above processing in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's current situation data into the AI, and the AI ​​can generate an appropriate proxy means.

[0110] The proxy unit can estimate the user's emotions and adjust the display method of the proxy task based on the estimated user emotions. For example, if the user is nervous, the proxy unit can provide a simple and highly visible display method. If the user is relaxed, the proxy unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the proxy unit can provide a concise display method. This improves user satisfaction by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input user emotion data into a generative AI, and the generative AI can generate an appropriate display method.

[0111] The proxy unit can select the optimal proxy method when executing a proxy task, taking into account the user's geographical location information. For example, if the user is in a specific region, the proxy unit will prioritize proxy tasks related to that region. Furthermore, if the user is traveling, the proxy unit can prioritize proxy tasks related to the travel destination. Additionally, if the user is at home, the proxy unit can prioritize proxy tasks around their home. This allows the proxy unit to provide highly relevant proxy methods by considering geographical location information. Some or all of the above processing in the proxy unit may be performed using AI, for example, or without AI. For instance, the proxy unit can input the user's geographical location information into AI, which can then generate an appropriate proxy method.

[0112] The proxy unit can analyze the user's social media activity and suggest proxy methods when executing proxy tasks. For example, the proxy unit can prioritize executing relevant proxy tasks based on the topics the user is discussing on social media. The proxy unit can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the proxy unit can prioritize executing relevant proxy tasks based on the activities of the user's social media followers and friends. In this way, by analyzing social media activity, it can provide proxy methods based on the user's interests. Some or all of the above processing in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's social media data into AI, and the AI ​​can generate appropriate proxy methods.

[0113] The support unit can estimate the user's emotions and adjust the method of support provided based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. If the user is relaxed, the support unit can provide support in a cheerful voice. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. This improves user satisfaction by providing support that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI, and the generative AI can generate an appropriate support method.

[0114] The support department can select the optimal support method by referring to the user's past support history when providing support. For example, the support department can select the optimal support method based on the support methods the user has used in the past. The support department can also select a support method that suits the user's preferences from their past support history. Furthermore, the support department can analyze the user's past support history and select the most efficient support method. This improves user convenience by providing the optimal support method based on past support history. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the user's support history data into AI, and the AI ​​can generate an appropriate support method.

[0115] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user needs urgent support, the support unit will provide that support with the highest priority. The support unit can also provide support with the normal priority if the user is relaxed. Furthermore, if the support unit is stressed, the support unit can prioritize high-priority support. This allows for the rapid provision of important support by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI, which can then generate appropriate priorities.

[0116] The support unit can select the optimal support method when providing support, taking into account the user's device information. For example, if the user is using a smartphone, the support unit can provide a support method that is adapted to the screen size. Furthermore, if the user is using a tablet, the support unit can provide a support method optimized for a larger screen. In addition, if the user is using a smartwatch, the support unit can provide a concise and highly visible support method. This improves user convenience by providing support methods tailored to device information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's device information into AI, which can then generate an appropriate support method.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The reception desk can estimate the user's emotions and adjust how questions and requests are handled based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of questions and requests. This improves user satisfaction by providing a reception method that is tailored to the user's emotions. 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 desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then generate an appropriate reception method.

[0119] The reception desk can analyze a user's past question history and select the optimal reception method. For example, it can automatically display as suggestions the types of questions the user has frequently asked in the past. It can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, it can predict and suggest questions that the user may ask during specific time periods based on their past question history. This improves user convenience by providing the optimal reception method based on past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's question history data into an AI, which can then generate the optimal reception method.

[0120] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is currently located, it will prioritize displaying information related to that location. It can also prioritize receiving relevant questions and requests based on the user's areas of interest. Furthermore, it can suggest appropriate questions and requests based on the user's current situation (e.g., time of day, weather). This allows for the provision of more relevant information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation data into the AI, which can then perform appropriate filtering.

[0121] The reception desk can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated emotions. For example, if the user has an urgent request, that request will be given the highest priority. If the user is relaxed, the request can be received with the normal priority. Furthermore, if the user is stressed, high-priority requests can be given priority. This allows important requests to be processed quickly by setting priorities according to the user's emotions. 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 desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then generate appropriate priorities.

[0122] The reception desk can prioritize receiving questions and requests that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving information related to that region. Similarly, if the user is traveling, it can prioritize receiving questions and requests related to their travel destination. Furthermore, if the user is at home, it can prioritize receiving information about their surroundings. This allows the reception desk to provide users with information that is highly relevant to them by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then perform appropriate filtering.

[0123] The generation unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is relaxed, it can generate a polite and detailed response. If the user is in a hurry, it can generate a concise and quick response. Furthermore, if the user is stressed, it can generate a response using gentle language. This improves user satisfaction by providing responses that match the user's emotions. 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generative AI, which can then generate an appropriate response.

[0124] The generation unit can adjust the level of detail in responses based on the importance of the questions and requests when generating responses. For example, it can generate detailed responses for high-importance questions, and concise responses for low-importance questions. Furthermore, it can generate responses with appropriate levels of detail depending on the content of the question. This allows for appropriate responses to user requests by providing responses that match the importance of the questions and requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into the AI, and the AI ​​can generate responses with appropriate levels of detail.

[0125] The generation unit can apply different response algorithms depending on the category of the question or request when generating a response. For example, a specialized response algorithm can be applied to technical questions. A response algorithm specifically designed for customer support can also be applied to customer support questions. Furthermore, a response algorithm specifically designed for individual support can be applied to personal assistance questions. By applying a response algorithm according to the category, a more appropriate response can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into the AI, and the AI ​​can apply an appropriate response algorithm.

[0126] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, it can generate a short, concise response. If the user is relaxed, it can generate a longer response that includes detailed explanations. Furthermore, if the user is excited, it can generate a response with visually stimulating effects. This improves user satisfaction by providing response lengths that match the user's emotions. 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI, which can then generate an appropriate response length.

[0127] The generation unit can determine the priority of responses based on when the questions or requests were submitted. For example, it can generate responses with the highest priority for urgent questions. It can also generate responses with normal priority for regular questions. Furthermore, it can postpone the generation of responses for past questions. This allows for a quick response to urgent requests by setting response priorities according to the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question submission timing data into the AI, which can then generate appropriate priorities.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk receives user questions and requests. These include requests for restaurant reservations, event information, and technical support. The reception desk can receive text messages and voice input from users via a chatbot. Furthermore, the reception desk can estimate the user's emotions and adjust how questions and requests are received based on those emotions. Step 2: The generation unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate responses. The generation AI uses a text generation AI (e.g., LLM) to generate answers to the user's questions. The generation unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. Step 3: The integration unit integrates with the platform provider's service suite. For example, it can integrate with a news distribution service to provide the latest news. It can also integrate with a restaurant recommendation service to recommend restaurants suitable for the user. Furthermore, the integration unit can estimate the user's emotions and select services to integrate with based on those estimated emotions. Step 4: The response unit provides the response generated by the generation unit in real time. The response unit displays the text message through the chatbot interface. It can also provide a voice response. Furthermore, the response unit can estimate the user's emotions and adjust how the response is displayed based on the estimated emotions. Step 5: The proxy service handles tasks such as making restaurant and hotel reservations, managing schedules, and assisting with online shopping. For example, it can make restaurant reservations on behalf of the user. It can also manage the user's schedule and set reminders. Furthermore, the proxy service can estimate the user's emotions and prioritize tasks based on those estimated emotions.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, response unit, proxy unit, and support unit, is implemented 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 receives text messages and voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using a generation AI. The collaboration unit collaborates with the platform provider's service group via the communication I / F 26 of the data processing unit 12. The response unit is implemented by the output device 40 of the smart device 14 and provides the generated response in real time. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks on behalf of the user. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides individual support. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

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

[0137] The 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.

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

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

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

[0141] Figure 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.

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

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

[0144] In the 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.

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

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, response unit, proxy unit, and support 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 receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using a generation AI. The collaboration unit collaborates with the platform provider's service group via the communication I / F 26 of the data processing unit 12. The response unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated response in real time. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks on behalf of the user. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides individual support. 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.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

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

[0153] The 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.

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

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, response unit, proxy unit, and support unit, is implemented in 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 receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using a generation AI. The collaboration unit collaborates with the platform provider's service group via the communication I / F 26 of the data processing unit 12. The response unit is implemented by the speaker 240 of the headset terminal 314 and provides the generated response in real time. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks on behalf of the user. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides individual support. 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.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the reception unit, generation unit, coordination unit, response unit, proxy unit, and support unit, is implemented by, for example, 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 receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using generation AI. The coordination unit coordinates with the platform provider's service group via the communication I / F 26 of the data processing unit 12. The response unit is implemented by the speaker 240 of the robot 414 and provides the generated response in real time. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks on behalf of the user. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides individual support. 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) A reception desk that handles user questions and requests, A generation unit analyzes questions and requests received by the reception unit and generates appropriate responses, The integration department, which works in conjunction with the platform provider's service suite, A response unit that provides the response generated by the generation unit in real time, It includes an outsourcing department that handles tasks such as restaurant and hotel reservations, schedule management, and online shopping support. A system characterized by the following features. (Note 2) The company has a support department that provides individual support such as customer support, technical support, and personal assistance. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned linkage unit is, It integrates with services such as news distribution, restaurant recommendations, and event information provision. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned agency unit, We provide reservation services for restaurants and hotels. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned agency unit, Manage your schedule The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned agency unit, Providing support for online shopping The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how questions and requests are received based on those 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 question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions or requests, filtering is performed based on the user's current situation and 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 questions and requests to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions or requests, the system prioritizes those that are highly relevant, taking into account 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 questions or requests, the system analyzes the user's social media activity and accepts relevant questions or requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a response, adjust the level of detail in the response based on the importance of the question or request. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a response, different response algorithms are applied depending on the category of the question or request. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating responses, the priority of responses is determined based on when the questions or requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating responses, the order of responses is adjusted based on the relevance of the questions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, It estimates the user's emotions and selects services to integrate with based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, During integration, the optimal integration method is selected by referring to the service provision history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When integrating, different integration methods are applied depending on the service category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of services to connect with based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, When integrating services, the priority of the integration is determined based on the timing of service delivery. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, During integration, the order of integrations is adjusted based on the relevance of the services. The system described in Appendix 1, characterized by the features described herein. (Note 25) The response unit is It estimates the user's emotions and adjusts how responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The response unit is When displaying a response, the system selects the optimal display method by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response unit is When displaying a response, customize the way the response is displayed based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response unit is It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response unit is When displaying a response, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The response unit is When displaying a response, the system analyzes the user's social media activity and proposes a method for displaying the response. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned agency unit, It estimates the user's emotions and determines the priority of tasks to be performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned agency unit, When executing a task on behalf of another user, the system will refer to the user's past task history to select the most suitable method of execution. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned agency unit, When executing a proxy task, the proxy method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned agency unit, It estimates the user's emotions and adjusts how the proxy tasks are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned agency unit, When executing a proxy task, the system selects the optimal proxy method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned agency unit, When performing proxy tasks, the system analyzes the user's social media activity and suggests alternative methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit is We estimate the user's emotions and adjust how support is provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned support unit is When providing support, the system will refer to the user's past support history to select the most appropriate support method. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned support unit is When providing support, the optimal support method is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0202] 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 desk that handles user questions and requests, A generation unit analyzes questions and requests received by the reception unit and generates appropriate responses, The integration department, which works in conjunction with the platform provider's service suite, A response unit that provides the response generated by the generation unit in real time, It includes an outsourcing department that handles tasks such as restaurant and hotel reservations, schedule management, and online shopping support. A system characterized by the following features.

2. The company has a support department that provides individual support such as customer support, technical support, and personal assistance. The system according to feature 1.

3. The aforementioned linkage unit is, It integrates with services such as news distribution, restaurant recommendations, and event information provision. The system according to feature 1.

4. The aforementioned agency unit, We provide reservation services for restaurants and hotels. The system according to feature 1.

5. The aforementioned agency unit, Manage your schedule The system according to feature 1.

6. The aforementioned agency unit, Providing support for online shopping The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how questions and requests are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

9. The aforementioned reception unit is When receiving questions or requests, filtering is performed based on the user's current situation and 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 questions and requests to accept based on those estimated emotions. The system according to feature 1.

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