system

Abstract

The system according to this embodiment aims to enable even users with low IT literacy to use cutting-edge web services. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a generation unit, an application unit, and a voice reception unit. The reception unit receives user information. The collection unit collects information from the user by asking questions based on the information received by the reception unit. The generation unit understands the mechanism of the web application based on the information collected by the collection unit and makes an application on behalf of the user. The application unit makes an application based on the information generated by the generation unit. The voice reception unit handles communication via voice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult for users with low IT literacy, such as the elderly, to use advanced Web services.

[0005] The system according to the embodiment aims to enable users with low IT literacy to use advanced Web services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, a generation unit, an application unit, and a voice reception unit. The reception unit receives user information. The collection unit collects information from the user by asking questions based on the information received by the reception unit. The generation unit understands the mechanism of the web application based on the information collected by the collection unit and submits an application on behalf of the user. The application unit submits an application based on the information generated by the generation unit. The voice reception unit handles communication via voice. [Effects of the Invention]

[0007] The system according to this embodiment enables even users with low IT literacy to use cutting-edge web services. [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 controls 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 service according to an embodiment of the present invention is a service that enables elderly people to access cutting-edge web services using familiar tools such as telephones and televisions. This service provides a mechanism that allows elderly people to avoid difficult operations and complete services such as Shinkansen (bullet train) reservations by voice. Specifically, first, the user fills out an application form in advance and registers it by taking a photo of it or by faxing it. Next, a generating AI uses this pre-registration information to collect the necessary information for registration by asking the user questions and then submits the application. A key feature of this service is its ability to understand the mechanism of web applications in a general way and to submit applications on behalf of the user. This allows it to handle various services that will emerge in the future. For example, when making a Shinkansen reservation, the user makes the reservation by voice using a telephone. The generating AI uses the user's pre-registration information to ask necessary questions and collect the information necessary for the reservation. The collected information is entered into the web application by the generating AI, and the reservation is completed. If this mechanism enables the secure handling of personal information, it can be expanded into a service that includes voice support or proxy services for administrative services, applications, and benefit procedures. For example, when applying for administrative services by voice, the user makes the application by voice using a telephone, and the generating AI collects the necessary information and submits the application on their behalf. Thus, the present invention provides a service that can be easily used even by elderly people with low IT literacy by enabling them to access cutting-edge web services using telephones or televisions.

[0029] The service according to this embodiment comprises a reception unit, a collection unit, a generation unit, an application unit, and a voice reception unit. The reception unit receives user information. User information includes, but is not limited to, personal information, usage history, and areas of interest. The reception unit allows, for example, users to fill out an application form in advance and register it by taking a photo of it or by fax. The collection unit collects information from the user by asking questions based on the information received by the reception unit. The collection unit collects necessary information by asking questions based on the user's prior information. The collection unit can ask, for example, multiple-choice or open-ended questions. The generation unit understands the mechanism of the web application based on the information collected by the collection unit and submits the application on behalf of the user. The generation unit understands the mechanism of the web application by, for example, analyzing APIs and user interfaces. The generation unit can, for example, fill out online forms and submit documents on behalf of the user. The application unit submits the application based on the information generated by the generation unit. The application unit creates and submits application documents based on the generated information. The application unit can, for example, make applications using digital data. The voice reception unit enables users to operate by voice using a telephone. The voice reception unit enables users to operate by voice using, for example, speech recognition technology or speech synthesis technology. The voice reception unit can, for example, enable users to make Shinkansen reservations by voice using a telephone. As a result, the service according to the embodiment can enable elderly people to access advanced web services using telephones or televisions. Some or all of the above-described processes in the reception unit, collection unit, generation unit, application unit, and voice reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input user information into the AI ​​and have the AI ​​perform the information reception. The collection unit can collect information using an AI model that asks questions to the user and collects information. The generation unit can make applications using an AI model that understands the mechanism of the web application based on the collected information and makes applications on behalf of the user. The application unit can make applications using an AI model that makes applications based on the generated information.The voice reception unit can perform voice reception using an AI model that allows users to operate the system by voice over the phone.

[0030] The reception desk receives user information. User information includes, but is not limited to, personal information, usage history, and areas of interest. For example, users can register information by filling out an application form in advance and sending it via photo or fax. Furthermore, the reception desk can also receive information through online forms or dedicated apps. This allows users to easily provide information even from home. The reception desk stores the received information in a database and uses it for subsequent processing. For example, personal information is stored encrypted to ensure security. Usage history and areas of interest information are used to provide services tailored to the user's needs. The reception desk can use AI to classify and organize information. For example, natural language processing technology can be used to analyze free-form text and extract necessary information. AI can also suggest personalized services based on user information. This allows the reception desk to efficiently and accurately receive information and build a foundation for providing optimal services to users.

[0031] The data collection unit collects information by asking users questions based on the information received by the reception unit. For example, the data collection unit asks questions based on the user's prior information and collects the necessary information. The data collection unit can ask multiple-choice or open-ended questions. Specifically, it generates questions tailored to the user's areas of interest and usage history, and presents them in a format that is easy for the user to answer. The data collection unit can use AI to optimize the content and order of questions. For example, it can use machine learning algorithms to analyze past answer data and identify the most effective question patterns. The AI ​​can also analyze the user's answers in real time and automatically generate the next necessary questions. This allows the data collection unit to collect information efficiently and effectively, supporting the provision of services that meet the user's needs. Furthermore, the data collection unit can also ask and answer questions using voice recognition technology. This makes it possible to accommodate users who have difficulty typing, such as the elderly and visually impaired. The data collection unit stores the collected information in a database, making it available to subsequent generation and application units. This allows the data collection unit to efficiently collect user information and improve the overall system performance.

[0032] The generation unit understands the web application's mechanism based on the information collected by the collection unit and submits applications on behalf of the user. For example, the generation unit analyzes APIs and user interfaces to understand the web application's mechanism. Specifically, it analyzes the web application's API documentation to identify necessary endpoints and parameters. It also analyzes the user interface to identify the location of form input fields and submission buttons. Based on this information, the generation unit can perform online form input and document submission on behalf of the user. For example, it can automatically fill in the necessary forms based on the information provided by the user and click the submit button. The generation unit can automate these processes using AI. For example, it can use natural language processing technology to analyze user information and input it into the appropriate form. It can also use machine learning algorithms to analyze past application data and identify the optimal application method. This allows the generation unit to submit applications efficiently and accurately, significantly reducing the user's workload. Furthermore, the generation unit can monitor the application's progress in real time and notify the user. This allows the user to always know the status of their application and use the service with peace of mind.

[0033] The application department submits applications based on information generated by the generation department. For example, the application department creates and submits application documents based on the generated information. Specifically, the application department automatically creates the necessary documents based on the data provided by the generation department and submits them online. Because the application department can submit applications using digital data, it eliminates the need to mail paper documents. The application department can optimize the application process using AI. For example, it can use machine learning algorithms to analyze past application data and identify the most efficient application method. The AI ​​can also automatically check the content of application documents to ensure there are no deficiencies. This allows the application department to submit applications efficiently and accurately, significantly reducing the burden on users. Furthermore, the application department can monitor the progress of applications in real time and notify users. This allows users to always know the status of their applications and use the service with peace of mind. Even after the application is completed, the application department can provide additional information as needed. For example, if the application is approved, it will notify the user and guide them through the next steps. If the application is rejected, it will explain the reason to the user and support the resubmission process. This allows the application department to provide consistent support to users and ensure the entire application process runs smoothly.

[0034] The voice reception unit enables users to operate the system by voice using their telephone. The voice reception unit utilizes technologies such as speech recognition and speech synthesis to allow users to operate the system by voice. Specifically, the voice reception unit allows users to make Shinkansen (bullet train) reservations by voice using their telephone. The voice reception unit uses AI for speech recognition and speech synthesis. For example, speech recognition technology is used to convert the user's voice instructions into text and execute the necessary operations. Speech synthesis technology is used to provide system responses in voice. This allows users to easily operate the system using their telephone. The voice reception unit uses an AI model to analyze the user's voice instructions and execute appropriate operations. For example, natural language processing technology is used to accurately understand the user's intent and execute appropriate operations. Speech synthesis technology is used to provide users with easy-to-understand voice responses. This allows the voice reception unit to provide an environment where users can intuitively operate the system using their telephone. Furthermore, the voice reception unit can collect user feedback and continuously improve the system's accuracy and usability. For example, if a user's voice instructions are not accurately recognized, the voice recognition model can be improved based on that data. Furthermore, by analyzing the user's operation history, it can suggest more intuitive operating methods. This allows the voice reception unit to provide users with a high level of convenience and satisfaction.

[0035] The data collection unit can collect necessary information by asking questions based on the user's prior information. For example, the data collection unit can ask questions based on the user's prior information. For example, the data collection unit can ask questions based on past usage history and registration information. For example, the data collection unit can ask multiple-choice or open-ended questions. This allows for efficient collection of information based on the user's prior information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information by inputting the user's prior information into an AI and using an AI model that asks questions.

[0036] The generation unit can understand the mechanism of a web application based on the collected information and submit applications on its behalf. For example, the generation unit can understand the mechanism of a web application based on the collected information and submit applications on its behalf. For example, the generation unit can analyze APIs and user interfaces to understand the mechanism of a web application. For example, the generation unit can perform tasks such as filling out online forms and submitting documents on behalf of the user. This reduces the burden on the user by understanding the mechanism of the web application and submitting applications on its behalf. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected information into a generation AI, understand the mechanism of a web application, and submit applications using a generation AI model that performs applications on its behalf.

[0037] The voice reception unit can enable users to operate it by voice using a telephone. The voice reception unit can enable users to operate it by voice using a telephone, for example. The voice reception unit can enable users to operate it by voice using, for example, speech recognition technology or speech synthesis technology. The voice reception unit can enable users to make Shinkansen reservations by voice using a telephone, for example. This simplifies operation by enabling users to operate it by voice using a telephone. Some or all of the above processing in the voice reception unit may be performed using, for example, AI, or not using AI. For example, the voice reception unit can perform voice reception using an AI model that enables users to operate it by voice using a telephone.

[0038] The application department can submit applications based on the generated information. For example, the application department can submit applications based on the generated information. For example, the application department can create and submit application documents based on the generated information. For example, the application department can submit applications using digital data. This simplifies the application process by submitting applications based on the generated information. Some or all of the above-described processes in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the generated information into an AI and submit an application using an AI model.

[0039] The generation unit can handle personal information securely. The generation unit can, for example, handle personal information securely. The generation unit can, for example, use encryption technology and access control to handle personal information securely. This ensures the security of information by handling personal information securely. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform the secure handling of personal information.

[0040] The generation unit can provide voice support or act on behalf of administrative services, applications, benefits, and other procedures. For example, the generation unit can provide voice support or act on behalf of administrative services, applications, benefits, and other procedures. For example, the generation unit can provide voice guidance or act on behalf of application procedures. This improves user convenience by providing voice support or acting on behalf of administrative services, applications, benefits, and other procedures. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can have a generation AI perform voice support or act on behalf of administrative services, applications, benefits, and other procedures.

[0041] The reception desk can analyze a user's past information reception history and select the optimal reception method. For example, the reception desk can analyze a user's past information reception history and select the optimal reception method. For example, if a user has preferred using voice input in the past, the reception desk can prioritize suggesting voice input. For example, if a user has frequently used text input in the past, the reception desk can prioritize suggesting text input. For example, if a user has received information during a specific time period in the past, the reception desk can adjust the reception time accordingly. In this way, by analyzing past information reception history, the reception desk can provide the user with the most suitable reception method. 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 a user's past information reception history into an AI and select the reception method using an AI model that selects the optimal reception method.

[0042] The reception unit can filter information upon receipt based on the user's current situation and areas of interest. For example, if the user is interested in travel, the reception unit can prioritize receiving travel-related information. If the user is interested in health, the reception unit can prioritize receiving health-related information. If the user is interested in finance, the reception unit can prioritize receiving finance-related information. By filtering information based on the user's situation and areas of interest, the reception unit can provide highly relevant 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 current situation and areas of interest into an AI and filter the information using an AI model that performs filtering.

[0043] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, when receiving information, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. For example, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, if the user is at home, the reception unit can prioritize receiving information around the user's home. In this way, highly relevant information can be provided by considering the user's geographical location. 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 an AI and receive information using an AI model that prioritizes receiving highly relevant information.

[0044] The reception unit can analyze a user's social media activity when receiving information and receive relevant information. For example, the reception unit can analyze a user's social media activity when receiving information and receive relevant information. For example, if a user frequently posts on a particular topic on social media, the reception unit can prioritize receiving information related to that topic. For example, if a user participates in a particular event on social media, the reception unit can prioritize receiving information related to that event. For example, if a user belongs to a particular group on social media, the reception unit can prioritize receiving information related to that group. In this way, by analyzing social media activity, relevant information can be provided to the user. 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 receive information using an AI model that inputs the user's social media activity and receives relevant information.

[0045] The data collection unit can adjust the level of detail of questions based on the user's importance when collecting information. For example, the data collection unit can adjust the level of detail of questions based on the user's importance when collecting information. For example, the data collection unit can ask detailed questions if the user provides important information. For example, the data collection unit can ask concise questions if the user provides general information. For example, the data collection unit can ask questions to quickly gather necessary information if the user provides urgent information. This allows for efficient information collection by adjusting the level of detail of questions based on the user's importance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's importance into the AI ​​and use an AI model to adjust the level of detail of questions to ask questions.

[0046] The data collection unit can apply different question algorithms depending on the user's category when collecting information. For example, the data collection unit can apply different question algorithms depending on the user's category when collecting information. For example, if the user is elderly, the data collection unit can ask simple and easy-to-understand questions. For example, if the user is a business person, the data collection unit can ask specialized questions. For example, if the user is a student, the data collection unit can ask education-related questions. By applying a question algorithm according to the user's category, appropriate questions can be asked. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's category into the AI ​​and use an AI model that applies a question algorithm to ask questions.

[0047] The data collection unit can determine the priority of questions based on the timing of user submissions when collecting information. For example, the data collection unit can prioritize questions based on the timing of user submissions when collecting information. For example, the data collection unit can prioritize questions if the user is providing urgent information. For example, the data collection unit can ask questions with normal priority if the user is providing general information. For example, the data collection unit can postpone questions if the user is providing future information. This allows for efficient information collection by prioritizing questions based on the timing of user submissions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the timing of user submissions into an AI and use an AI model to determine the priority of questions to ask questions.

[0048] The data collection unit can adjust the order of questions based on the user's relevance when collecting information. For example, the data collection unit can adjust the order of questions based on the user's relevance when collecting information. For example, the data collection unit can ask questions first if the user is providing important information. For example, the data collection unit can ask questions in the normal order if the user is providing general information. For example, the data collection unit can ask questions with the highest priority if the user is providing urgent information. This allows for efficient information collection by adjusting the order of questions based on the user's relevance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's relevance into an AI and ask questions using an AI model that adjusts the order of questions.

[0049] The generation unit can adjust the level of detail of the generated information based on the user's importance when generating information. For example, the generation unit can adjust the level of detail of the generated information based on the user's importance when generating information. For example, if the user provides important information, the generation unit can generate detailed information. For example, if the user provides general information, the generation unit can generate concise information. For example, if the user provides urgent information, the generation unit can quickly generate the necessary information. In this way, appropriate information can be provided by adjusting the level of detail of the generated information based on the user's importance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's importance into the generation AI and generate information using a generation AI model that adjusts the level of detail of the generated information.

[0050] The generation unit can apply different generation algorithms depending on the user's category when generating information. For example, the generation unit can apply different generation algorithms depending on the user's category when generating information. For example, if the user is elderly, the generation unit can generate simple and easy-to-understand information. For example, if the user is a business person, the generation unit can generate specialized information. For example, if the user is a student, the generation unit can generate education-related information. By applying a generation algorithm according to the user's category, appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's category into a generation AI and generate information using a generation AI model that applies a generation algorithm.

[0051] The generation unit can determine the generation priority based on the user's submission timing when generating information. For example, the generation unit can prioritize generating information when the user provides urgent information. For example, the generation unit can generate information with normal priority when the user provides general information. For example, the generation unit can postpone generating information when the user provides future information. This allows for efficient information provision by determining the generation priority based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's submission timing into a generation AI and generate information using a generation AI model that determines the generation priority.

[0052] The generation unit can adjust the order of information generation based on the user's relevance. For example, the generation unit can adjust the order of information generation based on the user's relevance. For example, if the user provides important information, the generation unit can generate the information first. For example, if the user provides general information, the generation unit can generate the information in the normal order. For example, if the user provides urgent information, the generation unit can generate the information with the highest priority. This allows for efficient information delivery by adjusting the order of generation based on the user's relevance. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's relevance into a generation AI and generate information using a generation AI model that adjusts the order of generation.

[0053] The application unit can analyze the user's past application history and select the optimal application method at the time of application. For example, the application unit can analyze the user's past application history and select the optimal application method at the time of application. For example, the application unit can prioritize suggesting application methods that the user has used in the past. For example, the application unit can select the optimal method based on the user's past successful application methods. For example, the application unit can suggest the optimal application method for a specific time period based on the user's past application history. In this way, by analyzing past application history, the application unit can provide the user with the optimal application method. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's past application history into an AI and select an application method using an AI model that selects the optimal application method.

[0054] The application unit can customize the application process based on the user's current situation at the time of application. For example, the application unit can customize the application process based on the user's current situation at the time of application. For example, if the user is at home, the application unit can suggest an online application method. For example, if the user is out, the application unit can suggest an application method using a mobile device. For example, if the user is in the office, the application unit can suggest an application method using a desktop computer. This allows the application unit to provide an appropriate application method by customizing the application process based on the user's current situation. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's current situation into the AI ​​and use an AI model to customize the application process before making an application.

[0055] The application unit can select the optimal application method when an application is submitted, taking into account the user's geographical location information. For example, the application unit can select the optimal application method when an application is submitted, taking into account the user's geographical location information. For example, if the user is in a specific region, the application unit can suggest an application method relevant to that region. For example, if the user is traveling, the application unit can suggest an application method relevant to the travel destination. For example, if the user is at home, the application unit can suggest an application method around the user's home. In this way, an appropriate application method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's geographical location information into an AI and use an AI model to select the optimal application method to submit an application.

[0056] The application unit can analyze the user's social media activity at the time of application and propose an application method. For example, if the user frequently posts on a particular topic on social media, the application unit can propose an application method related to that topic. For example, if the user participates in a particular event on social media, the application unit can propose an application method related to that event. For example, if the user belongs to a particular group on social media, the application unit can propose an application method related to that group. By analyzing social media activity, the application unit can provide the user with the most suitable application method. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's social media activity into an AI and use an AI model that proposes an application method to make an application.

[0057] The voice reception unit can select the optimal reception method by referring to the user's past voice operation history when receiving a voice call. For example, the voice reception unit can select the optimal reception method by referring to the user's past voice operation history when receiving a voice call. For example, if the user has preferred to use voice input in the past, the voice reception unit can prioritize suggesting voice input. For example, if the user has frequently used a particular phrase in the past, the voice reception unit can prioritize using that phrase. For example, the voice reception unit can suggest the optimal voice reception method for a specific time period based on the user's past voice operation history. In this way, the voice reception unit can provide the user with the optimal voice reception method by referring to past voice operation history. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can perform voice reception using an AI model that inputs the user's past voice operation history into the AI ​​and selects the optimal reception method.

[0058] The voice reception unit can customize the voice reception method based on the user's current situation when a voice call is received. For example, the voice reception unit can customize the voice reception method based on the user's current situation when a voice call is received. For example, if the user is at home, the voice reception unit can suggest voice reception in a quiet environment. For example, if the user is out, the voice reception unit can suggest voice reception using a noise-canceling function. For example, if the user is in the office, the voice reception unit can suggest voice reception using a desktop computer. By customizing the voice reception method based on the user's current situation, an appropriate voice reception method can be provided. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can perform voice reception using an AI model that inputs the user's current situation into the AI ​​and customizes the voice reception method.

[0059] The voice reception unit can select the optimal voice reception method when receiving a voice call, taking into account the user's geographical location information. For example, the voice reception unit can select the optimal voice reception method when receiving a voice call, taking into account the user's geographical location information. For example, if the user is in a specific region, the voice reception unit can suggest a voice reception method related to that region. For example, if the user is traveling, the voice reception unit can suggest a voice reception method related to the travel destination. For example, if the user is at home, the voice reception unit can suggest a voice reception method around the user's home. In this way, an appropriate voice reception method can be provided by taking the user's geographical location information into an AI and selecting the optimal voice reception method.

[0060] The voice reception unit can analyze the user's social media activity and propose a method for voice reception when a voice call is received. For example, if the user frequently posts about a particular topic on social media, the voice reception unit can propose a voice reception method related to that topic. For example, if the user participates in a particular event on social media, the voice reception unit can propose a voice reception method related to that event. For example, if the user belongs to a particular group on social media, the voice reception unit can propose a voice reception method related to that group. In this way, by analyzing social media activity, the voice reception unit can provide the user with the most suitable voice reception method. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's social media activity into AI and perform voice reception using an AI model that proposes a method for voice reception.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze a user's past usage history and select the most appropriate questions. For example, if a user has frequently used a particular service in the past, it can prioritize asking questions related to that service. Also, if a user has used a service during a specific time period in the past, it can ask questions tailored to that time period. Furthermore, if a user has provided information in a specific way in the past, it can prioritize suggesting that method. This allows for efficient information collection by leveraging past usage history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past usage history into an AI and use an AI model that selects the most appropriate questions to ask questions.

[0063] The reception department can analyze a user's past information reception history and select the most suitable reception method. For example, if a user has preferred using voice input in the past, voice input can be suggested preferentially. If a user has frequently used text input in the past, text input can be suggested preferentially. If a user has previously received information during a specific time period, the reception can be scheduled to coincide with that time period. In this way, by analyzing past information reception history, the reception department can provide the user with the most suitable reception method. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's past information reception history into an AI and select the most suitable reception method using an AI model.

[0064] The data collection unit can adjust the level of detail of questions based on the user's importance during information gathering. For example, if the user provides important information, detailed questions can be asked. If the user provides general information, concise questions can be asked. If the user provides urgent information, questions can be asked to quickly gather the necessary information. This allows for efficient information gathering by adjusting the level of detail of questions based on the user's importance. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's importance into the AI ​​and use an AI model to adjust the level of detail of the questions before asking them.

[0065] The generation unit can apply different generation algorithms depending on the user's category when generating information. For example, if the user is elderly, it can generate simple and easy-to-understand information. If the user is a business person, it can generate specialized information. If the user is a student, it can generate education-related information. By applying a generation algorithm according to the user's category, appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's category into a generation AI and generate information using a generation AI model that applies a generation algorithm.

[0066] The application department can analyze the user's past application history to select the optimal application method at the time of application. For example, it can prioritize suggesting application methods the user has used in the past. It can select the optimal method based on the user's past successful application methods. It can suggest the optimal application method for a specific time period based on the user's past application history. In this way, by analyzing past application history, the application department can provide the user with the most suitable application method. Some or all of the above processes in the application department may be performed using AI or not. For example, the application department can input the user's past application history into an AI and select an application method using an AI model that selects the optimal application method.

[0067] The voice reception unit can select the optimal reception method by referring to the user's past voice operation history when receiving a voice call. For example, if the user has preferred using voice input in the past, voice input can be suggested preferentially. If the user has frequently used a particular phrase in the past, that phrase can be used preferentially. Based on the user's past voice operation history, the optimal voice reception method can be suggested for a specific time period. In this way, the optimal voice reception method can be provided to the user by referring to past voice operation history. Some or all of the above processing in the voice reception unit may be performed using AI or not. For example, the voice reception unit can input the user's past voice operation history into an AI and perform voice reception using an AI model that selects the optimal reception method.

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

[0069] Step 1: The reception desk receives user information. User information includes, for example, personal information, usage history, and areas of interest. The reception desk can register users who have filled out an application form in advance and sent it by photo or fax. Step 2: The data collection unit collects information by asking questions to the user, based on the information received by the reception unit. The data collection unit asks questions based on the user's prior information and collects the necessary information. The data collection unit can ask multiple-choice or open-ended questions. Step 3: The generation unit understands the web application's mechanism based on the information collected by the collection unit and submits the application on behalf of the user. The generation unit analyzes the API and user interface to understand the web application's mechanism. The generation unit can also fill out online forms and submit documents on behalf of the user. Step 4: The application department submits the application based on the information generated by the generation department. The application department creates and submits the application documents based on the generated information. The application department can submit the application using digital data. Step 5: The voice reception system will enable users to operate it by voice using their phones. The voice reception system will use speech recognition and speech synthesis technologies to enable users to operate it by voice. The voice reception system will allow users to make Shinkansen reservations by voice using their phones.

[0070] (Example of form 2) The service according to an embodiment of the present invention is a service that enables elderly people to access cutting-edge web services using familiar tools such as telephones and televisions. This service provides a mechanism that allows elderly people to avoid difficult operations and complete services such as Shinkansen (bullet train) reservations by voice. Specifically, first, the user fills out an application form in advance and registers it by taking a photo of it or by faxing it. Next, a generating AI uses this pre-registration information to collect the necessary information for registration by asking the user questions and then submits the application. A key feature of this service is its ability to understand the mechanism of web applications in a general way and to submit applications on behalf of the user. This allows it to handle various services that will emerge in the future. For example, when making a Shinkansen reservation, the user makes the reservation by voice using a telephone. The generating AI uses the user's pre-registration information to ask necessary questions and collect the information necessary for the reservation. The collected information is entered into the web application by the generating AI, and the reservation is completed. If this mechanism enables the secure handling of personal information, it can be expanded into a service that includes voice support or proxy services for administrative services, applications, and benefit procedures. For example, when applying for administrative services by voice, the user makes the application by voice using a telephone, and the generating AI collects the necessary information and submits the application on their behalf. Thus, the present invention provides a service that can be easily used even by elderly people with low IT literacy by enabling them to access cutting-edge web services using telephones or televisions.

[0071] The service according to this embodiment comprises a reception unit, a collection unit, a generation unit, an application unit, and a voice reception unit. The reception unit receives user information. User information includes, but is not limited to, personal information, usage history, and areas of interest. The reception unit allows, for example, users to fill out an application form in advance and register it by taking a photo of it or by fax. The collection unit collects information from the user by asking questions based on the information received by the reception unit. The collection unit collects necessary information by asking questions based on the user's prior information. The collection unit can ask, for example, multiple-choice or open-ended questions. The generation unit understands the mechanism of the web application based on the information collected by the collection unit and submits the application on behalf of the user. The generation unit understands the mechanism of the web application by, for example, analyzing APIs and user interfaces. The generation unit can, for example, fill out online forms and submit documents on behalf of the user. The application unit submits the application based on the information generated by the generation unit. The application unit creates and submits application documents based on the generated information. The application unit can, for example, make applications using digital data. The voice reception unit enables users to operate by voice using a telephone. The voice reception unit enables users to operate by voice using, for example, speech recognition technology or speech synthesis technology. The voice reception unit can, for example, enable users to make Shinkansen reservations by voice using a telephone. As a result, the service according to the embodiment can enable elderly people to access advanced web services using telephones or televisions. Some or all of the above-described processes in the reception unit, collection unit, generation unit, application unit, and voice reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input user information into the AI ​​and have the AI ​​perform the information reception. The collection unit can collect information using an AI model that asks questions to the user and collects information. The generation unit can make applications using an AI model that understands the mechanism of the web application based on the collected information and makes applications on behalf of the user. The application unit can make applications using an AI model that makes applications based on the generated information.The voice reception unit can perform voice reception using an AI model that allows users to operate the system by voice over the phone.

[0072] The reception desk receives user information. User information includes, but is not limited to, personal information, usage history, and areas of interest. For example, users can register information by filling out an application form in advance and sending it via photo or fax. Furthermore, the reception desk can also receive information through online forms or dedicated apps. This allows users to easily provide information even from home. The reception desk stores the received information in a database and uses it for subsequent processing. For example, personal information is stored encrypted to ensure security. Usage history and areas of interest information are used to provide services tailored to the user's needs. The reception desk can use AI to classify and organize information. For example, natural language processing technology can be used to analyze free-form text and extract necessary information. AI can also suggest personalized services based on user information. This allows the reception desk to efficiently and accurately receive information and build a foundation for providing optimal services to users.

[0073] The data collection unit collects information by asking users questions based on the information received by the reception unit. For example, the data collection unit asks questions based on the user's prior information and collects the necessary information. The data collection unit can ask multiple-choice or open-ended questions. Specifically, it generates questions tailored to the user's areas of interest and usage history, and presents them in a format that is easy for the user to answer. The data collection unit can use AI to optimize the content and order of questions. For example, it can use machine learning algorithms to analyze past answer data and identify the most effective question patterns. The AI ​​can also analyze the user's answers in real time and automatically generate the next necessary questions. This allows the data collection unit to collect information efficiently and effectively, supporting the provision of services that meet the user's needs. Furthermore, the data collection unit can also ask and answer questions using voice recognition technology. This makes it possible to accommodate users who have difficulty typing, such as the elderly and visually impaired. The data collection unit stores the collected information in a database, making it available to subsequent generation and application units. This allows the data collection unit to efficiently collect user information and improve the overall system performance.

[0074] The generation unit understands the web application's mechanism based on the information collected by the collection unit and submits applications on behalf of the user. For example, the generation unit analyzes APIs and user interfaces to understand the web application's mechanism. Specifically, it analyzes the web application's API documentation to identify necessary endpoints and parameters. It also analyzes the user interface to identify the location of form input fields and submission buttons. Based on this information, the generation unit can perform online form input and document submission on behalf of the user. For example, it can automatically fill in the necessary forms based on the information provided by the user and click the submit button. The generation unit can automate these processes using AI. For example, it can use natural language processing technology to analyze user information and input it into the appropriate form. It can also use machine learning algorithms to analyze past application data and identify the optimal application method. This allows the generation unit to submit applications efficiently and accurately, significantly reducing the user's workload. Furthermore, the generation unit can monitor the application's progress in real time and notify the user. This allows the user to always know the status of their application and use the service with peace of mind.

[0075] The application department submits applications based on information generated by the generation department. For example, the application department creates and submits application documents based on the generated information. Specifically, the application department automatically creates the necessary documents based on the data provided by the generation department and submits them online. Because the application department can submit applications using digital data, it eliminates the need to mail paper documents. The application department can optimize the application process using AI. For example, it can use machine learning algorithms to analyze past application data and identify the most efficient application method. The AI ​​can also automatically check the content of application documents to ensure there are no deficiencies. This allows the application department to submit applications efficiently and accurately, significantly reducing the burden on users. Furthermore, the application department can monitor the progress of applications in real time and notify users. This allows users to always know the status of their applications and use the service with peace of mind. Even after the application is completed, the application department can provide additional information as needed. For example, if the application is approved, it will notify the user and guide them through the next steps. If the application is rejected, it will explain the reason to the user and support the resubmission process. This allows the application department to provide consistent support to users and ensure the entire application process runs smoothly.

[0076] The voice reception unit enables users to operate the system by voice using their telephone. The voice reception unit utilizes technologies such as speech recognition and speech synthesis to allow users to operate the system by voice. Specifically, the voice reception unit allows users to make Shinkansen (bullet train) reservations by voice using their telephone. The voice reception unit uses AI for speech recognition and speech synthesis. For example, speech recognition technology is used to convert the user's voice instructions into text and execute the necessary operations. Speech synthesis technology is used to provide system responses in voice. This allows users to easily operate the system using their telephone. The voice reception unit uses an AI model to analyze the user's voice instructions and execute appropriate operations. For example, natural language processing technology is used to accurately understand the user's intent and execute appropriate operations. Speech synthesis technology is used to provide users with easy-to-understand voice responses. This allows the voice reception unit to provide an environment where users can intuitively operate the system using their telephone. Furthermore, the voice reception unit can collect user feedback and continuously improve the system's accuracy and usability. For example, if a user's voice instructions are not accurately recognized, the voice recognition model can be improved based on that data. Furthermore, by analyzing the user's operation history, it can suggest more intuitive operating methods. This allows the voice reception unit to provide users with a high level of convenience and satisfaction.

[0077] The data collection unit can collect necessary information by asking questions based on the user's prior information. For example, the data collection unit can ask questions based on the user's prior information. For example, the data collection unit can ask questions based on past usage history and registration information. For example, the data collection unit can ask multiple-choice or open-ended questions. This allows for efficient collection of information based on the user's prior information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information by inputting the user's prior information into an AI and using an AI model that asks questions.

[0078] The generation unit can understand the mechanism of a web application based on the collected information and submit applications on its behalf. For example, the generation unit can understand the mechanism of a web application based on the collected information and submit applications on its behalf. For example, the generation unit can analyze APIs and user interfaces to understand the mechanism of a web application. For example, the generation unit can perform tasks such as filling out online forms and submitting documents on behalf of the user. This reduces the burden on the user by understanding the mechanism of the web application and submitting applications on its behalf. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected information into a generation AI, understand the mechanism of a web application, and submit applications using a generation AI model that performs applications on its behalf.

[0079] The voice reception unit can enable users to operate it by voice using a telephone. The voice reception unit can enable users to operate it by voice using a telephone, for example. The voice reception unit can enable users to operate it by voice using, for example, speech recognition technology or speech synthesis technology. The voice reception unit can enable users to make Shinkansen reservations by voice using a telephone, for example. This simplifies operation by enabling users to operate it by voice using a telephone. Some or all of the above processing in the voice reception unit may be performed using, for example, AI, or not using AI. For example, the voice reception unit can perform voice reception using an AI model that enables users to operate it by voice using a telephone.

[0080] The application department can submit applications based on the generated information. For example, the application department can submit applications based on the generated information. For example, the application department can create and submit application documents based on the generated information. For example, the application department can submit applications using digital data. This simplifies the application process by submitting applications based on the generated information. Some or all of the above-described processes in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the generated information into an AI and submit an application using an AI model.

[0081] The generation unit can handle personal information securely. The generation unit can, for example, handle personal information securely. The generation unit can, for example, use encryption technology and access control to handle personal information securely. This ensures the security of information by handling personal information securely. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform the secure handling of personal information.

[0082] The generation unit can provide voice support or act on behalf of administrative services, applications, benefits, and other procedures. For example, the generation unit can provide voice support or act on behalf of administrative services, applications, benefits, and other procedures. For example, the generation unit can provide voice guidance or act on behalf of application procedures. This improves user convenience by providing voice support or acting on behalf of administrative services, applications, benefits, and other procedures. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can have a generation AI perform voice support or act on behalf of administrative services, applications, benefits, and other procedures.

[0083] The reception unit can estimate the user's emotions and adjust the timing of information reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the reception timing to provide time for relaxation. If the user is in a hurry, the reception unit can speed up the timing to receive information quickly. If the user is relaxed, the reception unit can receive information at the normal timing. This reduces user stress by adjusting the timing of information reception 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 AI and have the AI ​​perform emotion estimation.

[0084] The reception desk can analyze a user's past information reception history and select the optimal reception method. For example, the reception desk can analyze a user's past information reception history and select the optimal reception method. For example, if a user has preferred using voice input in the past, the reception desk can prioritize suggesting voice input. For example, if a user has frequently used text input in the past, the reception desk can prioritize suggesting text input. For example, if a user has received information during a specific time period in the past, the reception desk can adjust the reception time accordingly. In this way, by analyzing past information reception history, the reception desk can provide the user with the most suitable reception method. 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 a user's past information reception history into an AI and select the reception method using an AI model that selects the optimal reception method.

[0085] The reception unit can filter information upon receipt based on the user's current situation and areas of interest. For example, if the user is interested in travel, the reception unit can prioritize receiving travel-related information. If the user is interested in health, the reception unit can prioritize receiving health-related information. If the user is interested in finance, the reception unit can prioritize receiving finance-related information. By filtering information based on the user's situation and areas of interest, the reception unit can provide highly relevant 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 current situation and areas of interest into an AI and filter the information using an AI model that performs filtering.

[0086] The reception unit can estimate the user's emotions and determine the priority of the information to be received based on the estimated emotions. For example, if the user is stressed, the reception unit can postpone receiving less important information. If the user is relaxed, the reception unit can prioritize receiving more important information. If the user is in a hurry, the reception unit can prioritize receiving urgent information. In this way, by prioritizing information according to the user's emotions, important information can be received preferentially. 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 an AI and have the AI ​​perform emotion estimation.

[0087] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, when receiving information, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. For example, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, if the user is at home, the reception unit can prioritize receiving information around the user's home. In this way, highly relevant information can be provided by considering the user's geographical location. 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 an AI and receive information using an AI model that prioritizes receiving highly relevant information.

[0088] The reception unit can analyze a user's social media activity when receiving information and receive relevant information. For example, the reception unit can analyze a user's social media activity when receiving information and receive relevant information. For example, if a user frequently posts on a particular topic on social media, the reception unit can prioritize receiving information related to that topic. For example, if a user participates in a particular event on social media, the reception unit can prioritize receiving information related to that event. For example, if a user belongs to a particular group on social media, the reception unit can prioritize receiving information related to that group. In this way, by analyzing social media activity, relevant information can be provided to the user. 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 receive information using an AI model that inputs the user's social media activity and receives relevant information.

[0089] The data collection unit can estimate the user's emotions and adjust the wording of questions based on the estimated emotions. For example, if the user is nervous, the data collection unit can ask questions in a gentle tone. If the user is relaxed, the data collection unit can ask questions in a friendly tone. If the user is in a hurry, the data collection unit can ask concise and quick questions. This reduces user stress by adjusting 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0090] The data collection unit can adjust the level of detail of questions based on the user's importance when collecting information. For example, the data collection unit can adjust the level of detail of questions based on the user's importance when collecting information. For example, the data collection unit can ask detailed questions if the user provides important information. For example, the data collection unit can ask concise questions if the user provides general information. For example, the data collection unit can ask questions to quickly gather necessary information if the user provides urgent information. This allows for efficient information collection by adjusting the level of detail of questions based on the user's importance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's importance into the AI ​​and use an AI model to adjust the level of detail of questions to ask questions.

[0091] The data collection unit can apply different question algorithms depending on the user's category when collecting information. For example, the data collection unit can apply different question algorithms depending on the user's category when collecting information. For example, if the user is elderly, the data collection unit can ask simple and easy-to-understand questions. For example, if the user is a business person, the data collection unit can ask specialized questions. For example, if the user is a student, the data collection unit can ask education-related questions. By applying a question algorithm according to the user's category, appropriate questions can be asked. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's category into the AI ​​and use an AI model that applies a question algorithm to ask questions.

[0092] The data collection unit can estimate the user's emotions and adjust the length of questions based on the estimated emotions. For example, the data collection unit can ask short, concise questions if the user is nervous. For example, the data collection unit can ask detailed questions if the user is relaxed. For example, the data collection unit can ask short questions that can be answered quickly if the user is in a hurry. This allows for efficient information collection by adjusting the length of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0093] The data collection unit can determine the priority of questions based on the timing of user submissions when collecting information. For example, the data collection unit can prioritize questions based on the timing of user submissions when collecting information. For example, the data collection unit can prioritize questions if the user is providing urgent information. For example, the data collection unit can ask questions with normal priority if the user is providing general information. For example, the data collection unit can postpone questions if the user is providing future information. This allows for efficient information collection by prioritizing questions based on the timing of user submissions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the timing of user submissions into an AI and use an AI model to determine the priority of questions to ask questions.

[0094] The data collection unit can adjust the order of questions based on the user's relevance when collecting information. For example, the data collection unit can adjust the order of questions based on the user's relevance when collecting information. For example, the data collection unit can ask questions first if the user is providing important information. For example, the data collection unit can ask questions in the normal order if the user is providing general information. For example, the data collection unit can ask questions with the highest priority if the user is providing urgent information. This allows for efficient information collection by adjusting the order of questions based on the user's relevance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's relevance into an AI and ask questions using an AI model that adjusts the order of questions.

[0095] The generation unit can estimate the user's emotions and adjust the way the generated information is presented based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the way the generated information is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate information that proceeds at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate concise and quick information. For example, if the user is excited, the generation unit can generate information with visually stimulating effects. In this way, by adjusting the way the information is presented according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI and generate information using a generative AI model that adjusts the way the information is presented.

[0096] The generation unit can adjust the level of detail of the generated information based on the user's importance when generating information. For example, the generation unit can adjust the level of detail of the generated information based on the user's importance when generating information. For example, if the user provides important information, the generation unit can generate detailed information. For example, if the user provides general information, the generation unit can generate concise information. For example, if the user provides urgent information, the generation unit can quickly generate the necessary information. In this way, appropriate information can be provided by adjusting the level of detail of the generated information based on the user's importance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's importance into the generation AI and generate information using a generation AI model that adjusts the level of detail of the generated information.

[0097] The generation unit can apply different generation algorithms depending on the user's category when generating information. For example, the generation unit can apply different generation algorithms depending on the user's category when generating information. For example, if the user is elderly, the generation unit can generate simple and easy-to-understand information. For example, if the user is a business person, the generation unit can generate specialized information. For example, if the user is a student, the generation unit can generate education-related information. By applying a generation algorithm according to the user's category, appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's category into a generation AI and generate information using a generation AI model that applies a generation algorithm.

[0098] The generation unit can estimate the user's emotions and adjust the length of the information it generates based on the estimated emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the information it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise information. For example, if the user is relaxed, the generation unit can generate longer information that includes detailed explanations. For example, if the user is excited, the generation unit can generate information with visually stimulating effects. This allows for the provision of appropriate information by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and generate information using a generative AI model that adjusts the length of the information.

[0099] The generation unit can determine the generation priority based on the user's submission timing when generating information. For example, the generation unit can prioritize generating information when the user provides urgent information. For example, the generation unit can generate information with normal priority when the user provides general information. For example, the generation unit can postpone generating information when the user provides future information. This allows for efficient information provision by determining the generation priority based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's submission timing into a generation AI and generate information using a generation AI model that determines the generation priority.

[0100] The generation unit can adjust the order of information generation based on the user's relevance. For example, the generation unit can adjust the order of information generation based on the user's relevance. For example, if the user provides important information, the generation unit can generate the information first. For example, if the user provides general information, the generation unit can generate the information in the normal order. For example, if the user provides urgent information, the generation unit can generate the information with the highest priority. This allows for efficient information delivery by adjusting the order of generation based on the user's relevance. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's relevance into a generation AI and generate information using a generation AI model that adjusts the order of generation.

[0101] The application unit can estimate the user's emotions and adjust the application method based on the estimated emotions. For example, the application unit can estimate the user's emotions and adjust the application method based on the estimated emotions. For example, if the user is nervous, the application unit can provide a simple and easy-to-understand application method. For example, if the user is relaxed, the application unit can provide a detailed application method. For example, if the user is in a hurry, the application unit can provide a method that allows for quick application. By adjusting the application method according to the user's emotions, an application method that is easy for the user to understand can be provided. 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 application unit may be performed using AI, for example, or without AI. For example, the application unit can input user emotion data into an AI and make an application using an AI model that adjusts the application method.

[0102] The application unit can analyze the user's past application history and select the optimal application method at the time of application. For example, the application unit can analyze the user's past application history and select the optimal application method at the time of application. For example, the application unit can prioritize suggesting application methods that the user has used in the past. For example, the application unit can select the optimal method based on the user's past successful application methods. For example, the application unit can suggest the optimal application method for a specific time period based on the user's past application history. In this way, by analyzing past application history, the application unit can provide the user with the optimal application method. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's past application history into an AI and select an application method using an AI model that selects the optimal application method.

[0103] The application unit can customize the application process based on the user's current situation at the time of application. For example, the application unit can customize the application process based on the user's current situation at the time of application. For example, if the user is at home, the application unit can suggest an online application method. For example, if the user is out, the application unit can suggest an application method using a mobile device. For example, if the user is in the office, the application unit can suggest an application method using a desktop computer. This allows the application unit to provide an appropriate application method by customizing the application process based on the user's current situation. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's current situation into the AI ​​and use an AI model to customize the application process before making an application.

[0104] The application unit can estimate the user's emotions and determine the priority of applications based on the estimated emotions. For example, if the user is stressed, the application unit can postpone less important applications. If the user is relaxed, the application unit can prioritize processing more important applications. If the user is in a hurry, the application unit can prioritize processing urgent applications. This allows important applications to be processed preferentially by determining the priority of applications 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 application unit may be performed using AI or not. For example, the application unit can input user emotion data into an AI and use an AI model to determine the priority of applications to make an application.

[0105] The application unit can select the optimal application method when an application is submitted, taking into account the user's geographical location information. For example, the application unit can select the optimal application method when an application is submitted, taking into account the user's geographical location information. For example, if the user is in a specific region, the application unit can suggest an application method relevant to that region. For example, if the user is traveling, the application unit can suggest an application method relevant to the travel destination. For example, if the user is at home, the application unit can suggest an application method around the user's home. In this way, an appropriate application method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's geographical location information into an AI and use an AI model to select the optimal application method to submit an application.

[0106] The application unit can analyze the user's social media activity at the time of application and propose an application method. For example, if the user frequently posts on a particular topic on social media, the application unit can propose an application method related to that topic. For example, if the user participates in a particular event on social media, the application unit can propose an application method related to that event. For example, if the user belongs to a particular group on social media, the application unit can propose an application method related to that group. By analyzing social media activity, the application unit can provide the user with the most suitable application method. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the user's social media activity into an AI and use an AI model that proposes an application method to make an application.

[0107] The voice reception unit can estimate the user's emotions and adjust the voice reception method based on the estimated emotions. For example, the voice reception unit can estimate the user's emotions and adjust the voice reception method based on the estimated emotions. For example, if the user is nervous, the voice reception unit can perform voice reception in a gentle tone. For example, if the user is relaxed, the voice reception unit can perform voice reception in a friendly tone. For example, if the user is in a hurry, the voice reception unit can perform voice reception in a concise and quick manner. By adjusting the voice reception method according to the user's emotions, a voice reception method that is easy for the user to understand can be provided. 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 voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input user emotion data into the AI ​​and use an AI model to adjust the voice reception method to perform voice reception.

[0108] The voice reception unit can select the optimal reception method by referring to the user's past voice operation history when receiving a voice call. For example, the voice reception unit can select the optimal reception method by referring to the user's past voice operation history when receiving a voice call. For example, if the user has preferred to use voice input in the past, the voice reception unit can prioritize suggesting voice input. For example, if the user has frequently used a particular phrase in the past, the voice reception unit can prioritize using that phrase. For example, the voice reception unit can suggest the optimal voice reception method for a specific time period based on the user's past voice operation history. In this way, the voice reception unit can provide the user with the optimal voice reception method by referring to past voice operation history. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can perform voice reception using an AI model that inputs the user's past voice operation history into the AI ​​and selects the optimal reception method.

[0109] The voice reception unit can customize the voice reception method based on the user's current situation when a voice call is received. For example, the voice reception unit can customize the voice reception method based on the user's current situation when a voice call is received. For example, if the user is at home, the voice reception unit can suggest voice reception in a quiet environment. For example, if the user is out, the voice reception unit can suggest voice reception using a noise-canceling function. For example, if the user is in the office, the voice reception unit can suggest voice reception using a desktop computer. By customizing the voice reception method based on the user's current situation, an appropriate voice reception method can be provided. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can perform voice reception using an AI model that inputs the user's current situation into the AI ​​and customizes the voice reception method.

[0110] The voice reception unit can estimate the user's emotions and determine the priority of voice reception based on the estimated emotions. For example, if the user is nervous, the voice reception unit can postpone less important voice receptions. For example, if the user is relaxed, the voice reception unit can prioritize processing more important voice receptions. For example, if the user is in a hurry, the voice reception unit can prioritize processing more urgent voice receptions. In this way, important voice receptions can be processed preferentially by determining the priority of voice receptions 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 voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input user emotion data into the AI ​​and use an AI model to determine the priority of voice reception to perform voice reception.

[0111] The voice reception unit can select the optimal voice reception method when receiving a voice call, taking into account the user's geographical location information. For example, the voice reception unit can select the optimal voice reception method when receiving a voice call, taking into account the user's geographical location information. For example, if the user is in a specific region, the voice reception unit can suggest a voice reception method related to that region. For example, if the user is traveling, the voice reception unit can suggest a voice reception method related to the travel destination. For example, if the user is at home, the voice reception unit can suggest a voice reception method around the user's home. In this way, an appropriate voice reception method can be provided by taking the user's geographical location information into an AI and selecting the optimal voice reception method.

[0112] The voice reception unit can analyze the user's social media activity and propose a method for voice reception when a voice call is received. For example, if the user frequently posts about a particular topic on social media, the voice reception unit can propose a voice reception method related to that topic. For example, if the user participates in a particular event on social media, the voice reception unit can propose a voice reception method related to that event. For example, if the user belongs to a particular group on social media, the voice reception unit can propose a voice reception method related to that group. In this way, by analyzing social media activity, the voice reception unit can provide the user with the most suitable voice reception method. Some or all of the above processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input the user's social media activity into AI and perform voice reception using an AI model that proposes a method for voice reception.

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

[0114] The reception desk can estimate the user's emotions and adjust its reception approach based on those estimates. For example, if the user is stressed, the reception desk can respond in a gentle tone; if the user is relaxed, it can respond in a friendly tone. If the user is in a hurry, the reception desk can ask concise questions to ensure a quick response. This allows for flexible responses tailored to the user's emotions, thereby improving user satisfaction. Emotion estimation is achieved using emotion engines or generative AI. For example, the reception desk can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0115] The data collection unit can analyze a user's past usage history and select the most appropriate questions. For example, if a user has frequently used a particular service in the past, it can prioritize asking questions related to that service. Also, if a user has used a service during a specific time period in the past, it can ask questions tailored to that time period. Furthermore, if a user has provided information in a specific way in the past, it can prioritize suggesting that method. This allows for efficient information collection by leveraging past usage history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past usage history into an AI and use an AI model that selects the most appropriate questions to ask questions.

[0116] The generation unit can estimate the user's emotions and adjust the way the generated information is presented based on those emotions. For example, if the user is relaxed, it can generate information that proceeds at a leisurely pace. If the user is in a hurry, it can generate concise and quick information. If the user is excited, it can generate information with visually stimulating effects. In this way, by adjusting the way information is presented according to the user's emotions, it is possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI. For example, the generation unit can input the user's emotion data into the generative AI and generate information using a generative AI model that adjusts the way the information is presented.

[0117] The voice reception unit can estimate the user's emotions and adjust the voice reception method based on the estimated emotions. For example, if the user is nervous, the voice reception can be conducted in a gentle tone. If the user is relaxed, the voice reception can be conducted in a friendly tone. If the user is in a hurry, the voice reception can be conducted in a concise and quick manner. In this way, by adjusting the voice reception method according to the user's emotions, a voice reception method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion engine or generative AI. For example, the voice reception unit can input user emotion data into the AI ​​and use an AI model that adjusts the voice reception method to conduct voice reception.

[0118] The application unit can estimate the user's emotions and adjust the application method based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand application method. If the user is relaxed, it can provide a detailed application method. If the user is in a hurry, it can provide a method that allows for quick application. In this way, by adjusting the application method according to the user's emotions, an application method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion engine or generative AI. For example, the application unit can input the user's emotion data into the AI ​​and use an AI model that adjusts the application method to make an application.

[0119] The reception department can analyze a user's past information reception history and select the most suitable reception method. For example, if a user has preferred using voice input in the past, voice input can be suggested preferentially. If a user has frequently used text input in the past, text input can be suggested preferentially. If a user has previously received information during a specific time period, the reception can be scheduled to coincide with that time period. In this way, by analyzing past information reception history, the reception department can provide the user with the most suitable reception method. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's past information reception history into an AI and select the most suitable reception method using an AI model.

[0120] The data collection unit can adjust the level of detail of questions based on the user's importance during information gathering. For example, if the user provides important information, detailed questions can be asked. If the user provides general information, concise questions can be asked. If the user provides urgent information, questions can be asked to quickly gather the necessary information. This allows for efficient information gathering by adjusting the level of detail of questions based on the user's importance. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's importance into the AI ​​and use an AI model to adjust the level of detail of the questions before asking them.

[0121] The generation unit can apply different generation algorithms depending on the user's category when generating information. For example, if the user is elderly, it can generate simple and easy-to-understand information. If the user is a business person, it can generate specialized information. If the user is a student, it can generate education-related information. By applying a generation algorithm according to the user's category, appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's category into a generation AI and generate information using a generation AI model that applies a generation algorithm.

[0122] The application department can analyze the user's past application history to select the optimal application method at the time of application. For example, it can prioritize suggesting application methods the user has used in the past. It can select the optimal method based on the user's past successful application methods. It can suggest the optimal application method for a specific time period based on the user's past application history. In this way, by analyzing past application history, the application department can provide the user with the most suitable application method. Some or all of the above processes in the application department may be performed using AI or not. For example, the application department can input the user's past application history into an AI and select an application method using an AI model that selects the optimal application method.

[0123] The voice reception unit can select the optimal reception method by referring to the user's past voice operation history when receiving a voice call. For example, if the user has preferred using voice input in the past, voice input can be suggested preferentially. If the user has frequently used a particular phrase in the past, that phrase can be used preferentially. Based on the user's past voice operation history, the optimal voice reception method can be suggested for a specific time period. In this way, the optimal voice reception method can be provided to the user by referring to past voice operation history. Some or all of the above processing in the voice reception unit may be performed using AI or not. For example, the voice reception unit can input the user's past voice operation history into an AI and perform voice reception using an AI model that selects the optimal reception method.

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

[0125] Step 1: The reception desk receives user information. User information includes, for example, personal information, usage history, and areas of interest. The reception desk can register users who have filled out an application form in advance and sent it by photo or fax. Step 2: The data collection unit collects information by asking questions to the user, based on the information received by the reception unit. The data collection unit asks questions based on the user's prior information and collects the necessary information. The data collection unit can ask multiple-choice or open-ended questions. Step 3: The generation unit understands the web application's mechanism based on the information collected by the collection unit and submits the application on behalf of the user. The generation unit analyzes the API and user interface to understand the web application's mechanism. The generation unit can also fill out online forms and submit documents on behalf of the user. Step 4: The application department submits the application based on the information generated by the generation department. The application department creates and submits the application documents based on the generated information. The application department can submit the application using digital data. Step 5: The voice reception system will enable users to operate it by voice using their phones. The voice reception system will use speech recognition and speech synthesis technologies to enable users to operate it by voice. The voice reception system will allow users to make Shinkansen reservations by voice using their phones.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, application unit, and voice reception unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user information. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects information by asking the user questions. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and understands the mechanism of the web application based on the collected information and submits an application on behalf of the user. The application unit is implemented by the specific processing unit 290 of the data processing device 12 and submits an application based on the generated information. The voice reception unit is implemented by the control unit 46A of the smart device 14 and allows the user to operate it by voice using a telephone. The reception unit estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, application unit, and voice reception 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 receives user information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information by asking the user questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the mechanism of the web application based on the collected information and submits an application on behalf of the user. The application unit is implemented by the specific processing unit 290 of the data processing unit 12 and submits an application based on the generated information. The voice reception unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to operate it by voice using a telephone. The reception unit estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. 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.

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

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, application unit, and voice reception unit, is implemented by, for example, 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 receives user information. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information by asking the user questions. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and understands the mechanism of the web application based on the collected information and submits an application on behalf of the user. The application unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and submits an application based on the generated information. The voice reception unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows the user to operate by voice using a telephone. The reception unit estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. 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.

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

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, application unit, and voice reception unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user information. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information by asking the user questions. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and understands the mechanism of the web application based on the collected information and submits an application on behalf of the user. The application unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and submits an application based on the generated information. The voice reception unit is implemented by, for example, the control unit 46A of the robot 414 and allows the user to operate by voice using a telephone. The reception unit estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. 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.

[0179] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0189] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) A reception desk that receives user information, Based on the information received by the aforementioned reception unit, the collection unit collects information by asking questions to the user, Based on the information collected by the aforementioned collection unit, the generation unit understands the mechanism of the web application and submits the application on behalf of the user. An application unit that submits an application based on the information generated by the generation unit, It includes a voice reception unit for voice communication. A system characterized by the following features. (Note 2) The aforementioned collection unit is Based on the user's prior information, we ask questions and collect the necessary information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the collected information, we understand the mechanism of the web application and submit the application on your behalf. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned voice reception unit is Allows users to control the system using their phone via voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned application department, The application is submitted based on the generated information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We handle personal information securely. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Provides voice support or assistance for administrative services, applications, benefits, and other procedures. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past information request history and select the optimal request method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, 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 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit 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 15) The aforementioned collection unit is When gathering information, adjust the level of detail of questions based on the user's importance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When gathering information, different question algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit 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 18) The aforementioned collection unit is When gathering information, prioritize questions based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is When gathering information, the order of questions is adjusted based on the relevance of the user. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts how the information generated is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating information, adjust the level of detail based on the user's importance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating information, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the information generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating information, the generation priority is determined based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating information, the order of generation is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned application department, The system estimates the user's emotions and adjusts the application process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned application department, When an application is submitted, the system analyzes the user's past application history to select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned application department, When applying, customize the application process based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned application department, The system estimates the user's emotions and determines the priority of applications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned application department, When applying, the most suitable application method will be selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned application department, When you apply, we will analyze your social media activity and suggest a method for submitting your application. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned voice reception unit is It estimates the user's emotions and adjusts the voice reception method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned voice reception unit is When a voice call is received, the system selects the most suitable method of receiving the call by referring to the user's past voice operation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned voice reception unit is When a voice call is received, the voice call method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned voice reception unit is The system estimates the user's emotions and determines the priority of voice requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned voice reception unit is When receiving a voice call, the system selects the optimal voice call method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned voice reception unit is During voice reception, the system analyzes the user's social media activity and suggests methods for voice reception. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives user information, Based on the information received by the aforementioned reception unit, the collection unit collects information by asking questions to the user, Based on the information collected by the aforementioned collection unit, the generation unit understands the mechanism of the web application and submits the application on behalf of the user. An application unit that submits an application based on the information generated by the generation unit, It includes a voice reception unit for voice communication. A system characterized by the following features.

2. The aforementioned collection unit is Based on the user's prior information, we ask questions and collect the necessary information. The system according to feature 1.

3. The generating unit is Based on the collected information, we understand the mechanism of the web application and submit the application on your behalf. The system according to feature 1.

4. The aforementioned voice reception unit is Allows users to control the system using their phone via voice commands. The system according to feature 1.

5. The aforementioned application department, The application is submitted based on the generated information. The system according to feature 1.

6. The generating unit is We handle personal information securely. The system according to feature 1.

7. The generating unit is Provides voice support or assistance for administrative services, applications, benefits, and other procedures. The system according to feature 1.

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

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