system

Abstract

The system according to this embodiment aims to understand the user's interests and provide a customized plan. [Solution] The system according to the embodiment comprises a reception unit, a questioning unit, a plan creation unit, a setup unit, and a notification unit. The reception unit receives input from the user. The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. The plan creation unit creates a customized plan based on the answers obtained by the questioning unit. The setup unit sets up each service based on the plan created by the plan creation unit. The notification unit notifies the user of the progress of the services set up by the setup unit.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there is a lack of customized support for maximizing the services provided to users.

[0005] The system according to the embodiment aims to understand the interests of users and provide customized plans.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a questioning unit, a plan creation unit, a setup unit, and a notification unit. The reception unit receives input from the user. The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. The plan creation unit creates a customized plan based on the answers obtained by the questioning unit. The setup unit sets up each service based on the plan created by the plan creation unit. The notification unit notifies the user of the progress of the services set up by the setup unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the user's interests and provide a customized plan. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 system according to an embodiment of the present invention is a mechanism that leverages an AI agent to promote understanding and use of cashless app services, thereby maximizing the value of cashless apps to customers. This system begins when a user activates the cashless app assistant mode and selects the option "I want help using cashless apps as effectively as possible." Next, the AI ​​agent asks a series of questions to understand the user's interests and provides a list of cashless app services that the user might be interested in. The AI ​​agent then proposes a customized approach, and if the user answers "yes," the AI ​​agent sets up each service. Once the process is complete, the AI ​​agent provides a summary of what has been done. For example, a user activates the cashless app assistant mode by long-pressing any smart icon. Next, the option "I want help using cashless apps as effectively as possible" is displayed. When this option is tapped, the AI ​​agent asks a series of questions to understand the user's interests. For example, it asks if the user wants to learn about different types of insurance, coupons, investments, financial hacks, etc. Based on the answers to the questions, the AI ​​agent provides a list of cashless app services that the user might be interested in. This list displays smart apps that meet the user's needs and clearly and concisely explains how they can be helpful. Next, the AI ​​agent asks the user if they would like a customized approach to using cashless app services like a pro. If the user answers "yes," the AI ​​agent creates a custom plan aimed at making all services work together most effectively to benefit the user. For example, the AI ​​agent might say: "Great style! It looks like you want to save money, make sure every yen works, and have insurance to cover various situations. I'll help you understand how to use all the services in the cashless app as effectively as possible, and I'll make it very easy for you.""Okay, let's get started!" The user is presented with a plan and a clear explanation of the value it will provide. From here, the user is asked if they would like help setting up each service, and if they answer "yes," the AI ​​agent works to set up each service one by one. The progress of each task is displayed through animations in which the assistant struggles on screen, often with humor. Various smart icons are displayed on the screen to help the user understand which services are being set up. If the user's cooperation is needed (for example, entering personal information), the AI ​​agent will let the user know, making the process clear, easy, and enjoyable. The user should feel that the assistant is taking on all the hard work and making it easier for them to save money and get the most out of their cashless app tools. Once the process is complete, the AI ​​agent provides a summary of what has been done. For example: "Great! You are now a power user of the cashless app! (The cashless app assistant cheers enthusiastically) You've been registered to save the most money on your purchases, travel insurance is available soon, and you're ready to start investing right away! Here are some tips on how to use all of these services together each week to get the most out of your cashless app! This allows the system to create a customized plan based on your input, set up each service, and notify you of your progress."

[0029] The system according to this embodiment comprises a reception unit, a questioning unit, a plan creation unit, a setup unit, and a notification unit. The reception unit receives input from the user. For example, the reception unit can receive input when the user long-presses a smart icon. The reception unit can also receive information from the user through voice input or text input. For example, the reception unit can receive information when the user says by voice, "I want to use a cashless app effectively." The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. For example, the questioning unit asks questions to understand whether the user wants to learn about various types of insurance, coupons, investments, financial hacks, etc. The questioning unit can dynamically adjust subsequent questions based on the user's answers. For example, if the questioning unit answers that the user is interested in insurance, it can ask detailed questions about insurance. The plan creation unit creates a customized plan based on the answers obtained by the questioning unit. For example, the plan creation unit provides a list of cashless app services that the user might be interested in. The plan creation unit can propose the most suitable service to meet the user's needs. For example, if the plan creation unit responds that the user is interested in investing, it will list cashless app services related to investing. The setup unit will set up each service based on the plan created by the plan creation unit. The setup unit will, for example, ask the user if they would like help setting up each service, and if the user answers "yes," it will set up each service one by one. The setup unit can notify the user when their cooperation is needed. For example, the setup unit will notify the user if they need to enter personal information. The notification unit will notify the user of the progress of the services set up by the setup unit. The notification unit will, for example, display the progress of each task through animation. The notification unit can visually show the user what stage the process is at. For example, the notification unit will display an animation on the screen of an assistant struggling with fun jokes.As a result, the system according to the embodiment can create a customized plan based on user input, set up each service, and notify the user of its progress.

[0030] The reception unit receives input from users. For example, the reception unit can receive input when a user long-presses a smart icon. The reception unit can also receive information from users through voice input and text input. Specifically, by long-pressing a smart icon, users can directly interact with the system. In the case of voice input, users simply speak into the microphone, and the system recognizes the voice, converts it to text, and processes it. For example, if a user voice-inputs "I want to use the cashless app effectively," that voice is converted to text using speech recognition technology and taken into the system. In the case of text input, users can directly input text using a keyboard or touchscreen. This allows the reception unit to support diverse input methods and improve user convenience. Furthermore, the reception unit analyzes the user's input and performs pre-processing to ensure appropriate processing. For example, in the case of voice input, noise reduction and speech normalization are performed, and in the case of text input, spell checking and grammatical analysis are performed to improve the quality of the input data. This allows the reception unit to receive user input accurately and efficiently.

[0031] The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. For example, the questioning unit asks questions to understand whether the user wants to learn about various types of insurance, coupons, investments, financial hacks, etc. Specifically, the questioning unit analyzes the user's input and generates questions to identify the user's interests and concerns. For example, if the user answers "I'm interested in insurance," the questioning unit can ask more detailed questions such as "What types of insurance are you interested in?" or "Do you have any specific questions about insurance?" The questioning unit can dynamically adjust the next questions based on the user's answers. For example, if the user answers "I'm interested in life insurance," the questioning unit can ask questions such as "What aspects of life insurance are you interested in?" or "Are you looking for specific information about life insurance?" This allows the questioning unit to ask appropriate questions tailored to the user's interests and concerns, accurately understanding the user's needs. Furthermore, the questioning unit can use AI to analyze the user's answers and optimize the next questions. For example, by using natural language processing technology to analyze user responses and generate relevant questions, it is possible to ask questions tailored to the user's interests and concerns. This allows the questioning unit to accurately understand the user's needs and collect information necessary to create a customized plan.

[0032] The planning department creates customized plans based on the answers obtained by the questioning department. For example, the planning department provides a list of cashless payment app services that the user might be interested in. Specifically, the planning department analyzes the user's responses and proposes the most suitable services based on the user's needs and interests. For example, if the user answers that they are interested in investing, the planning department will list cashless payment app services related to investing and explain the features and benefits of each service. The planning department can also propose combinations of services and usage methods to suit the user's needs. For example, if the user is interested in both insurance and investing, the planning department will create a plan that covers both insurance and investing and propose how to integrate and use each service. Furthermore, the planning department can use AI to predict user needs and create optimal plans. For example, by analyzing past user data using machine learning algorithms and predicting user behavior patterns and interests, it can create more accurate plans. This allows the planning department to provide customized plans tailored to user needs and improve user satisfaction.

[0033] The setup unit sets up each service based on the plan created by the plan creation unit. For example, the setup unit will ask the user if they need help setting up each service, and if the user answers "yes," it will set up each service one by one. Specifically, the setup unit will configure each service according to the user's instructions and input the necessary information. For example, if a user uses an investment service, the setup unit will input the user's personal information and investment information to complete the service setup. The setup unit can also notify the user if their cooperation is needed. For example, if the setup unit needs to input personal information, it will notify the user and explain how to input it and what information is required. Furthermore, after the setup of each service is complete, the setup unit will ask the user for confirmation and make corrections or additional settings as needed. This allows the setup unit to configure services to meet the user's needs and support a smooth start to using the services. In addition, the setup unit can optimize the setup process using AI to efficiently configure services. For example, by analyzing past setup data and proposing the optimal setup procedure, the setup process can be performed quickly and accurately. This allows the setup unit to reduce the burden on the user and support a smooth start to using the services.

[0034] The notification unit notifies users of the progress of services set up by the setup unit. For example, the notification unit displays the progress of each task through animation. Specifically, it uses progress bars and animations to visually indicate the stage of each service's setup. For instance, the notification unit might display an animation of an assistant humorously struggling on screen to inform the user of the process's progress. The notification unit can also send notifications to the user upon completion of each task, prompting them to proceed to the next step. For example, when service setup is complete, the notification unit might display a message such as, "Setup complete. Click here to proceed." Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, if a user provides feedback such as "It's easy to understand" or "I want more detailed information," the notification unit can adjust the notification based on that feedback to provide information tailored to the user's needs. This allows the notification unit to clearly communicate progress to the user and smoothly support service usage. Additionally, the notification unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably inform users of progress and support their use of the service.

[0035] The questioning unit can ask questions to understand whether the user wants to learn about various types of insurance, coupons, investments, financial hacks, etc. For example, the questioning unit might ask the user specific questions such as, "Do you want to learn about different types of insurance?", "Do you want to know how to use coupons?", "Are you interested in investing?", or "Do you want to learn about financial hacks?". Based on the user's answers, the questioning unit can dynamically adjust subsequent questions. For example, if the user answers that they are interested in insurance, the questioning unit can ask more detailed questions about insurance. This allows the questioning unit to ask specific questions to understand the user's interests. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit could input the user's answers into a generating AI and have the generating AI execute questions to understand the user's interests.

[0036] The plan creation unit can provide a list of cashless app services that the user might be interested in, based on the user's responses. For example, if the user responds that they are interested in insurance, the plan creation unit will list cashless app services related to insurance. The plan creation unit can also propose the most suitable service to meet the user's needs. For example, if the user responds that they are interested in investing, the plan creation unit will list cashless app services related to investing. Furthermore, if the user responds that they are interested in coupons, the plan creation unit can also list cashless app services related to coupons. This allows the plan creation unit to provide a list of appropriate services based on the user's interests. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's responses into a generating AI and have the AI ​​generate a list of services that the user might be interested in.

[0037] The setup unit can ask the user if they need help setting up each service, and if the user answers "yes," it can set up each service one by one. For example, the setup unit can ask the user, "Do you need help setting up this service?" and if the user answers "yes," it will start setting up that service. The setup unit can notify the user when their cooperation is needed. For example, the setup unit will notify the user if they need to enter personal information. The setup unit can also visually display the setup procedure for each service. For example, the setup unit can display an animation on the screen in which an assistant struggles with humor and jokes. This allows the setup unit to set up each service according to the user's requests. Some or all of the above processes in the setup unit may be performed using AI, for example, or not using AI. For example, the setup unit can input user input into a generating AI and have the generating AI perform the setup of each service.

[0038] The notification unit can display the progress of each task through animation. For example, the notification unit can use progress bars or icon movements to visually indicate the progress of each task. The notification unit can visually show the user what stage the process is in. For example, the notification unit can display an animation on the screen in which the assistant struggles with humorous jokes. The notification unit can also display the completion rate of each task. For example, the notification unit can use a progress bar to indicate the completion rate of a task. In this way, the notification unit can notify the user in an easy-to-understand manner by visually displaying the progress of each task. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the progress of each task into a generating AI and have the generating AI perform the display of the progress.

[0039] The notification unit can inform the user when their cooperation is needed, making the process clear, simple, and enjoyable. For example, the notification unit can display specific instructions to the user, such as "Please enter this information." The notification unit can provide guided steps when the user enters the necessary information. For example, the notification unit can use interactive elements to make the information entry process enjoyable for the user. The notification unit can also use animations or audio guidance to clarify the process when the user enters information. This allows the notification unit to clearly and simply inform the user when their cooperation is needed. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user input into a generating AI and have the generating AI guide the process.

[0040] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can automatically complete relevant input fields by referring to the content the user has entered in the past. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception desk to select the optimal reception method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying relevant input items based on the user's current situation (e.g., traveling). The reception unit can filter relevant input items based on the user's areas of interest (e.g., investing). The reception unit can also suggest the most suitable input items by combining the user's current situation and areas of interest. This allows the reception unit to filter input items based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize accepting inputs related to that region. If the user is traveling, the reception unit can prioritize accepting inputs related to the travel destination. Also, if the user is at home, the reception unit can prioritize accepting inputs related to home. In this way, the reception unit can prioritize accepting inputs that are highly relevant based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant inputs.

[0043] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on information the user has shared on social media. The reception unit can analyze the user's social media activity history and suggest relevant inputs. The reception unit can also accept relevant inputs based on information about accounts the user follows on social media. In this way, the reception unit can accept relevant inputs based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI perform the acceptance of relevant inputs.

[0044] The questioning unit can adjust the level of detail of questions based on the user's level of interest. For example, if the user shows a strong interest in a particular topic, the questioning unit can ask detailed questions. If the user shows interest in a broad range of topics, the questioning unit can ask general questions. Furthermore, if the user has little interest in a particular topic, the questioning unit can ask concise questions. In this way, the questioning unit can adjust the level of detail of questions according to the user's level of interest. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input user interest data into a generating AI and have the generating AI adjust the level of detail of the questions.

[0045] The questioning unit can apply different questioning algorithms depending on the user's category when a question is asked. For example, if the user belongs to the business category, the questioning unit can apply a business-related questioning algorithm. If the user belongs to the entertainment category, the questioning unit can apply an entertainment-related questioning algorithm. Furthermore, if the user belongs to the education category, the questioning unit can apply an education-related questioning algorithm. In this way, the questioning unit can apply an appropriate questioning algorithm according to the user's category. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input user category data into a generating AI and have the generating AI perform the application of the questioning algorithm.

[0046] The questioning unit can determine the priority of questions based on the user's answer history when a question is asked. For example, the questioning unit can prioritize relevant questions based on the user's past answer history. The questioning unit can prioritize questions on specific topics from the user's answer history. Furthermore, the questioning unit can analyze the user's answer history and prioritize the most relevant questions. In this way, the questioning unit can determine the priority of questions based on the user's answer history. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input the user's answer history data into a generating AI and have the generating AI perform the determination of question priorities.

[0047] The questioning unit can adjust the order of questions based on the user's relevance. For example, if the user shows a strong interest in a particular topic, the questioning unit will ask questions related to that topic first. If the user shows interest in a wide range of topics, the questioning unit can ask related questions in a sequential manner. Furthermore, the questioning unit can dynamically adjust the order of questions according to the degree of the user's interest. In this way, the questioning unit can adjust the order of questions based on the user's relevance. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the question order.

[0048] The plan creation unit can analyze the user's past behavior history to select the optimal plan when creating a plan. For example, the plan creation unit can propose the optimal plan based on the services the user has used in the past. The plan creation unit can select the most effective plan from the user's past behavior history. Furthermore, the plan creation unit can analyze the user's past behavior history and propose the most efficient plan. In this way, the plan creation unit can select the optimal plan based on the user's past behavior history. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's past behavior history data into a generating AI and have the generating AI perform the selection of the optimal plan.

[0049] The plan creation unit can customize the means of the plan based on the user's current situation when creating a plan. For example, the plan creation unit can customize a relevant plan based on the user's current situation (e.g., traveling). The plan creation unit can propose the optimal plan by combining the user's current situation and areas of interest. Furthermore, the plan creation unit can dynamically adjust the means of the plan according to the user's current situation. This allows the plan creation unit to customize the means of the plan based on the user's current situation. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of the plan.

[0050] The plan creation unit can select the optimal plan by considering the user's geographical location information when creating a plan. For example, if the user is in a specific region, the plan creation unit can prioritize suggesting plans related to that region. If the user is traveling, the plan creation unit can prioritize suggesting plans related to the travel destination. Also, if the user is at home, the plan creation unit can prioritize suggesting plans related to home. In this way, the plan creation unit can select the optimal plan based on the user's geographical location information. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's geographical location information into a generation AI and have the generation AI perform the selection of the optimal plan.

[0051] The plan creation unit can analyze the user's social media activity and propose plan methods when creating a plan. For example, the plan creation unit can propose relevant plans based on information the user has shared on social media. The plan creation unit can analyze the user's social media activity history and propose relevant plans. Furthermore, the plan creation unit can propose relevant plans based on information about accounts the user follows on social media. In this way, the plan creation unit can propose plan methods based on the user's social media activity. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input data on the user's social media activity into a generating AI and have the generating AI execute the proposal of plan methods.

[0052] The setup unit can analyze the user's past configuration history during setup to select the optimal setup method. For example, the setup unit can propose the optimal setup method based on the settings the user has used in the past. The setup unit can select the most effective setup method from the user's past configuration history. Furthermore, the setup unit can analyze the user's past configuration history and propose the most efficient setup method. As a result, the setup unit can select the optimal setup method based on the user's past configuration history. Some or all of the above-described processes in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input the user's past configuration history data into a generating AI and have the generating AI perform the selection of the optimal setup method.

[0053] The setup unit can customize the setup process based on the user's current situation during setup. For example, the setup unit can customize the relevant setup method based on the user's current situation (e.g., traveling). The setup unit can combine the user's current situation and areas of interest to suggest the optimal setup method. Furthermore, the setup unit can dynamically adjust the setup process according to the user's current situation. This allows the setup unit to customize the setup process based on the user's current situation. Some or all of the above-described processes in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the setup process.

[0054] The setup unit can select the optimal setup method during setup, taking into account the user's geographical location information. For example, if the user is in a specific region, the setup unit can prioritize suggesting a setup method related to that region. If the user is traveling, the setup unit can prioritize suggesting a setup method related to the travel destination. Furthermore, if the user is at home, the setup unit can prioritize suggesting a setup method related to home. In this way, the setup unit can select the optimal setup method based on the user's geographical location information. Some or all of the above processing in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal setup method.

[0055] The setup unit can analyze the user's social media activity during setup and propose setup methods. For example, the setup unit can propose relevant setup methods based on information shared by the user on social media. The setup unit can analyze the user's social media activity history and propose relevant setup methods. Furthermore, the setup unit can propose relevant setup methods based on information about accounts the user follows on social media. In this way, the setup unit can propose setup methods based on the user's social media activity. Some or all of the above processing in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input data on the user's social media activity into a generating AI and have the generating AI execute the proposal of setup methods.

[0056] The notification unit can analyze the user's past notification history to select the optimal notification method when a notification is sent. For example, the notification unit can suggest the optimal notification method based on the notification methods the user has preferred to receive in the past. The notification unit can select the most effective notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and suggest the most efficient notification method. In this way, the notification unit can select the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0057] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, the notification unit can customize the relevant notification method based on the user's current situation (e.g., being in a meeting). The notification unit can combine the user's current situation and areas of interest to suggest the optimal notification method. Furthermore, the notification unit can dynamically adjust the notification method according to the user's current situation. This allows the notification unit to customize the notification method based on the user's current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the notification method.

[0058] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user is in a specific region, the notification unit can prioritize suggesting a notification method related to that region. If the user is traveling, the notification unit can prioritize suggesting a notification method related to the travel destination. Also, if the user is at home, the notification unit can prioritize suggesting a notification method related to home. In this way, the notification unit can select the optimal notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal notification method.

[0059] The notification unit can analyze the user's social media activity and suggest notification methods when sending a notification. For example, the notification unit can suggest relevant notification methods based on information the user has shared on social media. The notification unit can analyze the user's social media activity history and suggest relevant notification methods. Furthermore, the notification unit can suggest relevant notification methods based on information about accounts the user follows on social media. In this way, the notification unit can suggest notification methods based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the user's social media activity into a generating AI and have the generating AI perform the task of suggesting notification methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze the user's past behavior history when receiving user input and suggest the most suitable input method. For example, if a user has frequently used voice input in the past, it will prioritize suggesting voice input. Also, if a user has previously entered data during a specific time period, it can prompt them to enter data during that time period. Furthermore, the reception desk can automatically complete relevant input fields by referring to the user's past input content. In this way, the reception desk can suggest the most suitable input method based on the user's past behavior history and improve input efficiency.

[0062] The planning unit can customize the planning process based on the user's current situation. For example, if the user is traveling, it can prioritize suggesting services related to their travel destination. If the user is at home, it can suggest services they can use at home. It can also combine the user's current situation and areas of interest to suggest the most suitable plan. This allows the planning unit to provide the optimal plan based on the user's current circumstances.

[0063] The reception desk can prioritize receiving input that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize receiving input related to that region. If the user is traveling, it will prioritize receiving input related to their travel destination. Also, if the user is at home, it will prioritize receiving input related to their home. In this way, the reception desk can prioritize receiving input that is highly relevant based on the user's geographical location.

[0064] The questioning function can prioritize questions based on the user's answer history. For example, it can prioritize related questions based on the user's past answers. It can also prioritize questions on specific topics based on the user's answer history. Furthermore, it can analyze the user's answer history and prioritize the most relevant questions. In this way, the questioning function can determine question priorities based on the user's answer history.

[0065] The planning department can analyze users' social media activity and propose plan strategies. For example, it can propose relevant plans based on information users have shared on social media. It can also analyze users' social media activity history and propose relevant plans. Furthermore, it can propose relevant plans based on information about accounts users follow on social media. In this way, the planning department can propose plan strategies based on users' social media activity.

[0066] The setup unit can analyze the user's past configuration history during setup to select the optimal setup method. For example, it can suggest the optimal setup method based on the settings the user has used in the past. It can select the most effective setup method from the user's past configuration history. It can also analyze the user's past configuration history and suggest the most efficient setup method. As a result, the setup unit can select the optimal setup method based on the user's past configuration history.

[0067] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, the system can customize the relevant notification method based on the user's current situation (e.g., being in a meeting). It can suggest the optimal notification method by combining the user's current situation and areas of interest. Furthermore, it can dynamically adjust the notification method according to the user's current situation. This allows the notification unit to customize the notification method based on the user's current situation.

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

[0069] Step 1: The reception desk receives input from the user. The reception desk can accept input, for example, when the user long-presses a smart icon. The reception desk can also receive information from the user through voice input or text input. Step 2: The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. For example, the questioning unit asks questions to understand if the user wants to learn about different types of insurance, coupons, investments, financial hacks, etc. Based on the user's answers, the questioning unit can dynamically adjust the next questions. Step 3: The planning team creates a customized plan based on the answers obtained from the questioning team. For example, the planning team can provide a list of services that the user might be interested in and suggest the most suitable services to meet the user's needs. Step 4: The setup unit sets up each service based on the plan created by the plan creation unit. For example, the setup unit asks the user if they need help setting up each service, and if the user answers "yes," it sets up each service one by one. The setup unit can notify the user if their assistance is needed. Step 5: The notification unit notifies the user of the progress of the services set up by the setup unit. The notification unit can, for example, display the progress of each task through animation, visually showing the user which stage the process is in.

[0070] (Example of form 2) The system according to an embodiment of the present invention is a mechanism that leverages an AI agent to promote understanding and use of cashless app services, thereby maximizing the value of cashless apps to customers. This system begins when a user activates the cashless app assistant mode and selects the option "I want help using cashless apps as effectively as possible." Next, the AI ​​agent asks a series of questions to understand the user's interests and provides a list of cashless app services that the user might be interested in. The AI ​​agent then proposes a customized approach, and if the user answers "yes," the AI ​​agent sets up each service. Once the process is complete, the AI ​​agent provides a summary of what has been done. For example, a user activates the cashless app assistant mode by long-pressing any smart icon. Next, the option "I want help using cashless apps as effectively as possible" is displayed. When this option is tapped, the AI ​​agent asks a series of questions to understand the user's interests. For example, it asks if the user wants to learn about different types of insurance, coupons, investments, financial hacks, etc. Based on the answers to the questions, the AI ​​agent provides a list of cashless app services that the user might be interested in. This list displays smart apps that meet the user's needs and clearly and concisely explains how they can be helpful. Next, the AI ​​agent asks the user if they would like a customized approach to using cashless app services like a pro. If the user answers "yes," the AI ​​agent creates a custom plan aimed at making all services work together most effectively to benefit the user. For example, the AI ​​agent might say: "Great style! It looks like you want to save money, make sure every yen works, and have insurance to cover various situations. I'll help you understand how to use all the services in the cashless app as effectively as possible, and I'll make it very easy for you.""Okay, let's get started!" The user is presented with a plan and a clear explanation of the value it will provide. From here, the user is asked if they would like help setting up each service, and if they answer "yes," the AI ​​agent works to set up each service one by one. The progress of each task is displayed through animations in which the assistant struggles on screen, often with humor. Various smart icons are displayed on the screen to help the user understand which services are being set up. If the user's cooperation is needed (for example, entering personal information), the AI ​​agent will let the user know, making the process clear, easy, and enjoyable. The user should feel that the assistant is taking on all the hard work and making it easier for them to save money and get the most out of their cashless app tools. Once the process is complete, the AI ​​agent provides a summary of what has been done. For example: "Great! You are now a power user of the cashless app! (The cashless app assistant cheers enthusiastically) You've been registered to save the most money on your purchases, travel insurance is available soon, and you're ready to start investing right away! Here are some tips on how to use all of these services together each week to get the most out of your cashless app! This allows the system to create a customized plan based on your input, set up each service, and notify you of your progress."

[0071] The system according to this embodiment comprises a reception unit, a questioning unit, a plan creation unit, a setup unit, and a notification unit. The reception unit receives input from the user. For example, the reception unit can receive input when the user long-presses a smart icon. The reception unit can also receive information from the user through voice input or text input. For example, the reception unit can receive information when the user says by voice, "I want to use a cashless app effectively." The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. For example, the questioning unit asks questions to understand whether the user wants to learn about various types of insurance, coupons, investments, financial hacks, etc. The questioning unit can dynamically adjust subsequent questions based on the user's answers. For example, if the questioning unit answers that the user is interested in insurance, it can ask detailed questions about insurance. The plan creation unit creates a customized plan based on the answers obtained by the questioning unit. For example, the plan creation unit provides a list of cashless app services that the user might be interested in. The plan creation unit can propose the most suitable service to meet the user's needs. For example, if the plan creation unit responds that the user is interested in investing, it will list cashless app services related to investing. The setup unit will set up each service based on the plan created by the plan creation unit. The setup unit will, for example, ask the user if they would like help setting up each service, and if the user answers "yes," it will set up each service one by one. The setup unit can notify the user when their cooperation is needed. For example, the setup unit will notify the user if they need to enter personal information. The notification unit will notify the user of the progress of the services set up by the setup unit. The notification unit will, for example, display the progress of each task through animation. The notification unit can visually show the user what stage the process is at. For example, the notification unit will display an animation on the screen of an assistant struggling with fun jokes.As a result, the system according to the embodiment can create a customized plan based on user input, set up each service, and notify the user of its progress.

[0072] The reception unit receives input from users. For example, the reception unit can receive input when a user long-presses a smart icon. The reception unit can also receive information from users through voice input and text input. Specifically, by long-pressing a smart icon, users can directly interact with the system. In the case of voice input, users simply speak into the microphone, and the system recognizes the voice, converts it to text, and processes it. For example, if a user voice-inputs "I want to use the cashless app effectively," that voice is converted to text using speech recognition technology and taken into the system. In the case of text input, users can directly input text using a keyboard or touchscreen. This allows the reception unit to support diverse input methods and improve user convenience. Furthermore, the reception unit analyzes the user's input and performs pre-processing to ensure appropriate processing. For example, in the case of voice input, noise reduction and speech normalization are performed, and in the case of text input, spell checking and grammatical analysis are performed to improve the quality of the input data. This allows the reception unit to receive user input accurately and efficiently.

[0073] The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. For example, the questioning unit asks questions to understand whether the user wants to learn about various types of insurance, coupons, investments, financial hacks, etc. Specifically, the questioning unit analyzes the user's input and generates questions to identify the user's interests and concerns. For example, if the user answers "I'm interested in insurance," the questioning unit can ask more detailed questions such as "What types of insurance are you interested in?" or "Do you have any specific questions about insurance?" The questioning unit can dynamically adjust the next questions based on the user's answers. For example, if the user answers "I'm interested in life insurance," the questioning unit can ask questions such as "What aspects of life insurance are you interested in?" or "Are you looking for specific information about life insurance?" This allows the questioning unit to ask appropriate questions tailored to the user's interests and concerns, accurately understanding the user's needs. Furthermore, the questioning unit can use AI to analyze the user's answers and optimize the next questions. For example, by using natural language processing technology to analyze user responses and generate relevant questions, it is possible to ask questions tailored to the user's interests and concerns. This allows the questioning unit to accurately understand the user's needs and collect information necessary to create a customized plan.

[0074] The planning department creates customized plans based on the answers obtained by the questioning department. For example, the planning department provides a list of cashless payment app services that the user might be interested in. Specifically, the planning department analyzes the user's responses and proposes the most suitable services based on the user's needs and interests. For example, if the user answers that they are interested in investing, the planning department will list cashless payment app services related to investing and explain the features and benefits of each service. The planning department can also propose combinations of services and usage methods to suit the user's needs. For example, if the user is interested in both insurance and investing, the planning department will create a plan that covers both insurance and investing and propose how to integrate and use each service. Furthermore, the planning department can use AI to predict user needs and create optimal plans. For example, by analyzing past user data using machine learning algorithms and predicting user behavior patterns and interests, it can create more accurate plans. This allows the planning department to provide customized plans tailored to user needs and improve user satisfaction.

[0075] The setup unit sets up each service based on the plan created by the plan creation unit. For example, the setup unit will ask the user if they need help setting up each service, and if the user answers "yes," it will set up each service one by one. Specifically, the setup unit will configure each service according to the user's instructions and input the necessary information. For example, if a user uses an investment service, the setup unit will input the user's personal information and investment information to complete the service setup. The setup unit can also notify the user if their cooperation is needed. For example, if the setup unit needs to input personal information, it will notify the user and explain how to input it and what information is required. Furthermore, after the setup of each service is complete, the setup unit will ask the user for confirmation and make corrections or additional settings as needed. This allows the setup unit to configure services to meet the user's needs and support a smooth start to using the services. In addition, the setup unit can optimize the setup process using AI to efficiently configure services. For example, by analyzing past setup data and proposing the optimal setup procedure, the setup process can be performed quickly and accurately. This allows the setup unit to reduce the burden on the user and support a smooth start to using the services.

[0076] The notification unit notifies users of the progress of services set up by the setup unit. For example, the notification unit displays the progress of each task through animation. Specifically, it uses progress bars and animations to visually indicate the stage of each service's setup. For instance, the notification unit might display an animation of an assistant humorously struggling on screen to inform the user of the process's progress. The notification unit can also send notifications to the user upon completion of each task, prompting them to proceed to the next step. For example, when service setup is complete, the notification unit might display a message such as, "Setup complete. Click here to proceed." Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, if a user provides feedback such as "It's easy to understand" or "I want more detailed information," the notification unit can adjust the notification based on that feedback to provide information tailored to the user's needs. This allows the notification unit to clearly communicate progress to the user and smoothly support service usage. Additionally, the notification unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably inform users of progress and support their use of the service.

[0077] The questioning unit can ask questions to understand whether the user wants to learn about various types of insurance, coupons, investments, financial hacks, etc. For example, the questioning unit might ask the user specific questions such as, "Do you want to learn about different types of insurance?", "Do you want to know how to use coupons?", "Are you interested in investing?", or "Do you want to learn about financial hacks?". Based on the user's answers, the questioning unit can dynamically adjust subsequent questions. For example, if the user answers that they are interested in insurance, the questioning unit can ask more detailed questions about insurance. This allows the questioning unit to ask specific questions to understand the user's interests. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit could input the user's answers into a generating AI and have the generating AI execute questions to understand the user's interests.

[0078] The plan creation unit can provide a list of cashless app services that the user might be interested in, based on the user's responses. For example, if the user responds that they are interested in insurance, the plan creation unit will list cashless app services related to insurance. The plan creation unit can also propose the most suitable service to meet the user's needs. For example, if the user responds that they are interested in investing, the plan creation unit will list cashless app services related to investing. Furthermore, if the user responds that they are interested in coupons, the plan creation unit can also list cashless app services related to coupons. This allows the plan creation unit to provide a list of appropriate services based on the user's interests. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's responses into a generating AI and have the AI ​​generate a list of services that the user might be interested in.

[0079] The setup unit can ask the user if they need help setting up each service, and if the user answers "yes," it can set up each service one by one. For example, the setup unit can ask the user, "Do you need help setting up this service?" and if the user answers "yes," it will start setting up that service. The setup unit can notify the user when their cooperation is needed. For example, the setup unit will notify the user if they need to enter personal information. The setup unit can also visually display the setup procedure for each service. For example, the setup unit can display an animation on the screen in which an assistant struggles with humor and jokes. This allows the setup unit to set up each service according to the user's requests. Some or all of the above processes in the setup unit may be performed using AI, for example, or not using AI. For example, the setup unit can input user input into a generating AI and have the generating AI perform the setup of each service.

[0080] The notification unit can display the progress of each task through animation. For example, the notification unit can use progress bars or icon movements to visually indicate the progress of each task. The notification unit can visually show the user what stage the process is in. For example, the notification unit can display an animation on the screen in which the assistant struggles with humorous jokes. The notification unit can also display the completion rate of each task. For example, the notification unit can use a progress bar to indicate the completion rate of a task. In this way, the notification unit can notify the user in an easy-to-understand manner by visually displaying the progress of each task. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the progress of each task into a generating AI and have the generating AI perform the display of the progress.

[0081] The notification unit can inform the user when their cooperation is needed, making the process clear, simple, and enjoyable. For example, the notification unit can display specific instructions to the user, such as "Please enter this information." The notification unit can provide guided steps when the user enters the necessary information. For example, the notification unit can use interactive elements to make the information entry process enjoyable for the user. The notification unit can also use animations or audio guidance to clarify the process when the user enters information. This allows the notification unit to clearly and simply inform the user when their cooperation is needed. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user input into a generating AI and have the generating AI guide the process.

[0082] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of input acceptance to give the user time to relax. If the user is excited, the reception unit can quickly accept input to maintain the user's excitement. Also, if the user is tired, the reception unit can adjust the timing of input acceptance to allow the user to take a break. In this way, the reception unit can adjust the timing of input acceptance 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 unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into the generative AI and have the generative AI adjust the timing of input acceptance.

[0083] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can automatically complete relevant input fields by referring to the content the user has entered in the past. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception desk to select the optimal reception method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI select the optimal reception method.

[0084] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying relevant input items based on the user's current situation (e.g., traveling). The reception unit can filter relevant input items based on the user's areas of interest (e.g., investing). The reception unit can also suggest the most suitable input items by combining the user's current situation and areas of interest. This allows the reception unit to filter input items based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0085] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is stressed, the reception unit will postpone less important inputs and prioritize more important ones. If the user is relaxed, the reception unit can process all inputs equally. Also, if the user is in a hurry, the reception unit can prioritize inputs that require quick processing. In this way, the reception unit can determine the priority of inputs 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 unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0086] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize accepting inputs related to that region. If the user is traveling, the reception unit can prioritize accepting inputs related to the travel destination. Also, if the user is at home, the reception unit can prioritize accepting inputs related to home. In this way, the reception unit can prioritize accepting inputs that are highly relevant based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant inputs.

[0087] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on information the user has shared on social media. The reception unit can analyze the user's social media activity history and suggest relevant inputs. The reception unit can also accept relevant inputs based on information about accounts the user follows on social media. In this way, the reception unit can accept relevant inputs based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI perform the acceptance of relevant inputs.

[0088] The questioning unit can estimate the user's emotions and adjust the wording of the questions based on the estimated emotions. For example, if the user is nervous, the questioning unit can ask questions in gentle language. If the user is relaxed, the questioning unit can ask questions in casual language. Also, if the user is in a hurry, the questioning unit can ask concise and quick questions. In this way, the questioning unit can adjust the wording of questions 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input user emotion data into the generative AI and have the generative AI adjust the wording of the questions.

[0089] The questioning unit can adjust the level of detail of questions based on the user's level of interest. For example, if the user shows a strong interest in a particular topic, the questioning unit can ask detailed questions. If the user shows interest in a broad range of topics, the questioning unit can ask general questions. Furthermore, if the user has little interest in a particular topic, the questioning unit can ask concise questions. In this way, the questioning unit can adjust the level of detail of questions according to the user's level of interest. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input user interest data into a generating AI and have the generating AI adjust the level of detail of the questions.

[0090] The questioning unit can apply different questioning algorithms depending on the user's category when a question is asked. For example, if the user belongs to the business category, the questioning unit can apply a business-related questioning algorithm. If the user belongs to the entertainment category, the questioning unit can apply an entertainment-related questioning algorithm. Furthermore, if the user belongs to the education category, the questioning unit can apply an education-related questioning algorithm. In this way, the questioning unit can apply an appropriate questioning algorithm according to the user's category. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input user category data into a generating AI and have the generating AI perform the application of the questioning algorithm.

[0091] The questioning unit can estimate the user's emotions and adjust the length of the questions based on the estimated emotions. For example, if the user is in a hurry, the questioning unit can ask short, to the point. If the user is relaxed, the questioning unit can ask longer questions that include detailed explanations. If the user is excited, the questioning unit can ask questions with visually stimulating effects. In this way, the questioning unit can adjust the length of the questions 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 questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input user emotion data into a generative AI and have the generative AI adjust the length of the questions.

[0092] The questioning unit can determine the priority of questions based on the user's answer history when a question is asked. For example, the questioning unit can prioritize relevant questions based on the user's past answer history. The questioning unit can prioritize questions on specific topics from the user's answer history. Furthermore, the questioning unit can analyze the user's answer history and prioritize the most relevant questions. In this way, the questioning unit can determine the priority of questions based on the user's answer history. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input the user's answer history data into a generating AI and have the generating AI perform the determination of question priorities.

[0093] The questioning unit can adjust the order of questions based on the user's relevance. For example, if the user shows a strong interest in a particular topic, the questioning unit will ask questions related to that topic first. If the user shows interest in a wide range of topics, the questioning unit can ask related questions in a sequential manner. Furthermore, the questioning unit can dynamically adjust the order of questions according to the degree of the user's interest. In this way, the questioning unit can adjust the order of questions based on the user's relevance. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the question order.

[0094] The planning unit can estimate the user's emotions and adjust the plan creation method based on the estimated emotions. For example, if the user is relaxed, the planning unit can create a plan that proceeds at a leisurely pace. If the user is in a hurry, the planning unit can create a plan that can be executed quickly. If the user is excited, the planning unit can create a plan with visually stimulating effects. In this way, the planning unit can adjust the plan creation method 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 planning unit may be performed using AI or not using AI. For example, the planning unit can input user emotion data into the generative AI and have the generative AI adjust the plan creation method.

[0095] The plan creation unit can analyze the user's past behavior history to select the optimal plan when creating a plan. For example, the plan creation unit can propose the optimal plan based on the services the user has used in the past. The plan creation unit can select the most effective plan from the user's past behavior history. Furthermore, the plan creation unit can analyze the user's past behavior history and propose the most efficient plan. In this way, the plan creation unit can select the optimal plan based on the user's past behavior history. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's past behavior history data into a generating AI and have the generating AI perform the selection of the optimal plan.

[0096] The plan creation unit can customize the means of the plan based on the user's current situation when creating a plan. For example, the plan creation unit can customize a relevant plan based on the user's current situation (e.g., traveling). The plan creation unit can propose the optimal plan by combining the user's current situation and areas of interest. Furthermore, the plan creation unit can dynamically adjust the means of the plan according to the user's current situation. This allows the plan creation unit to customize the means of the plan based on the user's current situation. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of the plan.

[0097] The planning unit can estimate the user's emotions and determine the priority of plans based on those emotions. For example, if the user is stressed, the planning unit will postpone less important plans and prioritize more important ones. If the user is relaxed, the planning unit can process all plans equally. Also, if the user is in a hurry, the planning unit can prioritize plans that require quick processing. In this way, the planning unit can determine the priority of plans 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 planning unit may be performed using AI or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI determine the priority of plans.

[0098] The plan creation unit can select the optimal plan by considering the user's geographical location information when creating a plan. For example, if the user is in a specific region, the plan creation unit can prioritize suggesting plans related to that region. If the user is traveling, the plan creation unit can prioritize suggesting plans related to the travel destination. Also, if the user is at home, the plan creation unit can prioritize suggesting plans related to home. In this way, the plan creation unit can select the optimal plan based on the user's geographical location information. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the user's geographical location information into a generation AI and have the generation AI perform the selection of the optimal plan.

[0099] The plan creation unit can analyze the user's social media activity and propose plan methods when creating a plan. For example, the plan creation unit can propose relevant plans based on information the user has shared on social media. The plan creation unit can analyze the user's social media activity history and propose relevant plans. Furthermore, the plan creation unit can propose relevant plans based on information about accounts the user follows on social media. In this way, the plan creation unit can propose plan methods based on the user's social media activity. Some or all of the above processing in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input data on the user's social media activity into a generating AI and have the generating AI execute the proposal of plan methods.

[0100] The setup unit can estimate the user's emotions and adjust the setup method based on the estimated emotions. For example, if the user is nervous, the setup unit can provide a simple and easy-to-understand setup method. If the user is relaxed, the setup unit can provide a setup method that includes detailed explanations. Also, if the user is in a hurry, the setup unit can provide a method that allows for quick setup completion. In this way, the setup unit can adjust the setup method 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 setup unit may be performed using AI, for example, or not using AI. For example, the setup unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the setup method.

[0101] The setup unit can analyze the user's past configuration history during setup to select the optimal setup method. For example, the setup unit can propose the optimal setup method based on the settings the user has used in the past. The setup unit can select the most effective setup method from the user's past configuration history. Furthermore, the setup unit can analyze the user's past configuration history and propose the most efficient setup method. As a result, the setup unit can select the optimal setup method based on the user's past configuration history. Some or all of the above-described processes in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input the user's past configuration history data into a generating AI and have the generating AI perform the selection of the optimal setup method.

[0102] The setup unit can customize the setup process based on the user's current situation during setup. For example, the setup unit can customize the relevant setup method based on the user's current situation (e.g., traveling). The setup unit can combine the user's current situation and areas of interest to suggest the optimal setup method. Furthermore, the setup unit can dynamically adjust the setup process according to the user's current situation. This allows the setup unit to customize the setup process based on the user's current situation. Some or all of the above-described processes in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the setup process.

[0103] The setup unit can estimate the user's emotions and determine the priority of the setup based on the estimated emotions. For example, if the user is stressed, the setup unit will postpone less important setups and prioritize more important ones. If the user is relaxed, the setup unit can process all setups equally. Also, if the user is in a hurry, the setup unit can prioritize setups that require quick processing. In this way, the setup unit can determine the priority of the setup 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 setup unit may be performed using AI, or not using AI. For example, the setup unit can input user emotion data into a generative AI and have the generative AI determine the priority of the setup.

[0104] The setup unit can select the optimal setup method during setup, taking into account the user's geographical location information. For example, if the user is in a specific region, the setup unit can prioritize suggesting a setup method related to that region. If the user is traveling, the setup unit can prioritize suggesting a setup method related to the travel destination. Furthermore, if the user is at home, the setup unit can prioritize suggesting a setup method related to home. In this way, the setup unit can select the optimal setup method based on the user's geographical location information. Some or all of the above processing in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal setup method.

[0105] The setup unit can analyze the user's social media activity during setup and propose setup methods. For example, the setup unit can propose relevant setup methods based on information shared by the user on social media. The setup unit can analyze the user's social media activity history and propose relevant setup methods. Furthermore, the setup unit can propose relevant setup methods based on information about accounts the user follows on social media. In this way, the setup unit can propose setup methods based on the user's social media activity. Some or all of the above processing in the setup unit may be performed using AI, for example, or without AI. For example, the setup unit can input data on the user's social media activity into a generating AI and have the generating AI execute the proposal of setup methods.

[0106] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is tense, the notification unit can send a notification in a calm tone. If the user is relaxed, the notification unit can send a notification in a casual tone. Also, if the user is in a hurry, the notification unit can send a quick and concise notification. In this way, the notification unit can adjust the notification method according to 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 notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI adjust the notification method.

[0107] The notification unit can analyze the user's past notification history to select the optimal notification method when a notification is sent. For example, the notification unit can suggest the optimal notification method based on the notification methods the user has preferred to receive in the past. The notification unit can select the most effective notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and suggest the most efficient notification method. In this way, the notification unit can select the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0108] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, the notification unit can customize the relevant notification method based on the user's current situation (e.g., being in a meeting). The notification unit can combine the user's current situation and areas of interest to suggest the optimal notification method. Furthermore, the notification unit can dynamically adjust the notification method according to the user's current situation. This allows the notification unit to customize the notification method based on the user's current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the notification method.

[0109] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will postpone less important notifications and prioritize more important ones. If the user is relaxed, the notification unit can process all notifications equally. Also, if the user is in a hurry, the notification unit can prioritize notifications that require immediate attention. In this way, the notification unit can determine the priority of notifications 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0110] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user is in a specific region, the notification unit can prioritize suggesting a notification method related to that region. If the user is traveling, the notification unit can prioritize suggesting a notification method related to the travel destination. Also, if the user is at home, the notification unit can prioritize suggesting a notification method related to home. In this way, the notification unit can select the optimal notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal notification method.

[0111] The notification unit can analyze the user's social media activity and suggest notification methods when sending a notification. For example, the notification unit can suggest relevant notification methods based on information the user has shared on social media. The notification unit can analyze the user's social media activity history and suggest relevant notification methods. Furthermore, the notification unit can suggest relevant notification methods based on information about accounts the user follows on social media. In this way, the notification unit can suggest notification methods based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the user's social media activity into a generating AI and have the generating AI perform the task of suggesting notification methods.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can analyze the user's past behavior history when receiving user input and suggest the most suitable input method. For example, if a user has frequently used voice input in the past, it will prioritize suggesting voice input. Also, if a user has previously entered data during a specific time period, it can prompt them to enter data during that time period. Furthermore, the reception desk can automatically complete relevant input fields by referring to the user's past input content. In this way, the reception desk can suggest the most suitable input method based on the user's past behavior history and improve input efficiency.

[0114] The questioning unit can estimate the user's emotions and adjust the wording of the questions based on those estimates. For example, if the user is nervous, the questions can be phrased gently. If the user is relaxed, the questions can be phrased casually. Also, if the user is in a hurry, the questions can be concise and quick. In this way, the questioning unit can adjust the wording of questions according to the user's emotions, making it easier for the user to answer questions comfortably.

[0115] The planning unit can customize the planning process based on the user's current situation. For example, if the user is traveling, it can prioritize suggesting services related to their travel destination. If the user is at home, it can suggest services they can use at home. It can also combine the user's current situation and areas of interest to suggest the most suitable plan. This allows the planning unit to provide the optimal plan based on the user's current circumstances.

[0116] The setup unit can estimate the user's emotions and adjust the setup method based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand setup method. If the user is relaxed, it can provide a setup method that includes detailed explanations. Furthermore, if the user is in a hurry, it can provide a method that allows for quick setup completion. In this way, the setup unit can adjust the setup method according to the user's emotions.

[0117] The notification unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, the notification can be delivered in a calm tone. If the user is relaxed, the notification can be delivered in a casual tone. If the user is in a hurry, the notification can be delivered quickly and concisely. In this way, the notification unit can adjust the notification method according to the user's emotions, making it easier for the user to receive notifications.

[0118] The reception desk can prioritize receiving input that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize receiving input related to that region. If the user is traveling, it will prioritize receiving input related to their travel destination. Also, if the user is at home, it will prioritize receiving input related to their home. In this way, the reception desk can prioritize receiving input that is highly relevant based on the user's geographical location.

[0119] The questioning function can prioritize questions based on the user's answer history. For example, it can prioritize related questions based on the user's past answers. It can also prioritize questions on specific topics based on the user's answer history. Furthermore, it can analyze the user's answer history and prioritize the most relevant questions. In this way, the questioning function can determine question priorities based on the user's answer history.

[0120] The planning department can analyze users' social media activity and propose plan strategies. For example, it can propose relevant plans based on information users have shared on social media. It can also analyze users' social media activity history and propose relevant plans. Furthermore, it can propose relevant plans based on information about accounts users follow on social media. In this way, the planning department can propose plan strategies based on users' social media activity.

[0121] The setup unit can analyze the user's past configuration history during setup to select the optimal setup method. For example, it can suggest the optimal setup method based on the settings the user has used in the past. It can select the most effective setup method from the user's past configuration history. It can also analyze the user's past configuration history and suggest the most efficient setup method. As a result, the setup unit can select the optimal setup method based on the user's past configuration history.

[0122] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, the system can customize the relevant notification method based on the user's current situation (e.g., being in a meeting). It can suggest the optimal notification method by combining the user's current situation and areas of interest. Furthermore, it can dynamically adjust the notification method according to the user's current situation. This allows the notification unit to customize the notification method based on the user's current situation.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives input from the user. The reception desk can accept input, for example, when the user long-presses a smart icon. The reception desk can also receive information from the user through voice input or text input. Step 2: The questioning unit asks questions to understand the user's interests based on the information received by the reception unit. For example, the questioning unit asks questions to understand if the user wants to learn about different types of insurance, coupons, investments, financial hacks, etc. Based on the user's answers, the questioning unit can dynamically adjust the next questions. Step 3: The planning team creates a customized plan based on the answers obtained from the questioning team. For example, the planning team can provide a list of services that the user might be interested in and suggest the most suitable services to meet the user's needs. Step 4: The setup unit sets up each service based on the plan created by the plan creation unit. For example, the setup unit asks the user if they need help setting up each service, and if the user answers "yes," it sets up each service one by one. The setup unit can notify the user if their assistance is needed. Step 5: The notification unit notifies the user of the progress of the services set up by the setup unit. The notification unit can, for example, display the progress of each task through animation, visually showing the user which stage the process is in.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, questioning unit, plan creation unit, setup unit, and notification 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 control unit 46A of the smart device 14 and accepts input when the user long-presses a smart icon. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12 and asks questions to understand the user's interests. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a customized plan. The setup unit is implemented by the control unit 46A of the smart device 14 and sets up each service. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the progress. 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.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, questioning unit, plan creation unit, setup unit, and notification 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 control unit 46A of the smart glasses 214, and accepts input when the user long-presses a smart icon. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12, and asks questions to understand the user's interests. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12, and creates a customized plan. The setup unit is implemented by the control unit 46A of the smart glasses 214, and sets up each service. The notification unit is implemented by the control unit 46A of the smart glasses 214, and notifies the user of the progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, questioning unit, plan creation unit, setup unit, and notification unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts input when the user long-presses a smart icon. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12 and asks questions to understand the user's interests. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a customized plan. The setup unit is implemented by the control unit 46A of the headset terminal 314 and sets up each service. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the progress. 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, questioning unit, plan creation unit, setup unit, and notification unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts input when the user long-presses a smart icon. The questioning unit is implemented by the specific processing unit 290 of the data processing unit 12 and asks questions to understand the user's interests. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a customized plan. The setup unit is implemented by the control unit 46A of the robot 414 and sets up each service. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the progress status. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reception area that receives input from users, Based on the information received by the reception unit, the questioning unit asks questions to understand the user's interests, A plan creation unit creates a customized plan based on the answers obtained by the aforementioned questioning unit, A setup unit sets up each service based on the plan created by the aforementioned plan creation unit, The system includes a notification unit that notifies the progress of the service set up by the setup unit. A system characterized by the following features. (Note 2) The aforementioned question section is, Ask questions to understand if the user wants to learn about different types of insurance, coupons, investments, financial hacks, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned plan creation unit, Based on user responses, we provide a list of cashless payment app services that users might be interested in. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned setup unit is The system asks the user if they need help setting up each service, and if the user answers "yes," it sets up each service one by one. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, The progress of each task is displayed through animation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Notify users when their cooperation is needed, and make the process clear, simple, and enjoyable. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current 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 input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, 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 input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question section is, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question section is, When asking questions, adjust the level of detail based on the user's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question section is, When asking a question, apply a different question algorithm depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question section is, The system estimates the user's emotions and adjusts the length of the questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question section is, When a question is asked, the system prioritizes the question based on the user's answer history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question section is, When asking questions, adjust the order of questions based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned plan creation unit, We estimate user sentiment and adjust the plan creation process based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned plan creation unit, When creating a plan, we analyze the user's past behavior history to select the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned plan creation unit, When creating a plan, customize the plan's methods based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned plan creation unit, It estimates user sentiment and determines plan priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned plan creation unit, When creating a plan, the optimal plan is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned plan creation unit, When creating a plan, we analyze users' social media activity and propose methods for implementing the plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned setup unit is It estimates the user's emotions and adjusts the setup method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned setup unit is During setup, the system analyzes the user's past configuration history to select the optimal setup method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned setup unit is During setup, customize the setup process based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned setup unit is It estimates the user's emotions and determines setup priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned setup unit is During setup, the optimal setup method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned setup unit is During setup, we analyze the user's social media activity and suggest setup methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system analyzes the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, Based on the information received by the reception unit, the questioning unit asks questions to understand the user's interests, A plan creation unit creates a customized plan based on the answers obtained by the aforementioned questioning unit, A setup unit sets up each service based on the plan created by the aforementioned plan creation unit, The system includes a notification unit that notifies the progress of the service set up by the setup unit. A system characterized by the following features.

2. The aforementioned question section is, Ask questions to understand if the user wants to learn about different types of insurance, coupons, investments, financial hacks, etc. The system according to feature 1.

3. The aforementioned setup unit is The system asks the user if they need help setting up each service, and if the user answers yes, it sets up each service one by one. The system according to feature 1.

4. The aforementioned notification unit, The progress of each task is displayed through animation. The system according to feature 1.

5. The aforementioned notification unit, Notify users when their cooperation is needed, and make the process clear, simple, and enjoyable. The system according to feature 1.

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

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

Images