system

Abstract

The system according to this embodiment aims to automate procedures based on personal information and eliminate the complexity of those procedures. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a selection unit, a procedure unit, and a notification unit. The reception unit receives personal information. The analysis unit analyzes the information entered by the reception unit. The selection unit selects an appropriate procedure based on the information analyzed by the analysis unit. The procedure unit proceeds with the procedure selected by the selection unit. The notification unit notifies the user of the progress of the procedure carried out by the procedure unit.

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, and includes 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 is a problem that procedures in administrative and private companies are complicated and the procedures may be delayed.

[0005] The system according to the embodiment aims to automate procedures based on personal information and eliminate the complexity of the procedures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, a procedure unit, and a notification unit. The reception unit receives personal information. The analysis unit analyzes the information entered by the reception unit. The selection unit selects an appropriate procedure based on the information analyzed by the analysis unit. The procedure unit proceeds with the procedure selected by the selection unit. The notification unit notifies the user of the progress of the procedure carried out by the procedure unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate procedures based on personal information, thereby eliminating the complexity of the process. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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) An embodiment of the present invention provides a procedure automation system that automates text-based procedures using a generative AI. This system works as follows: the user inputs personal information, the generative AI analyzes the input, selects the appropriate procedure, and proceeds with the process. If there are any unclear points, the system contacts the user via social media and notifies the user of the procedure's progress. This mechanism frees users from cumbersome procedures and allows them to track the procedure's progress in real time. For example, the user inputs personal information, such as their name, address, and contact details. Next, the generative AI analyzes the input information. Based on the input information, the generative AI selects the appropriate procedure. For example, it automatically selects the procedure the user needs, such as filing a tax return or canceling a subscription service. Based on the selected procedure, the generative AI proceeds with the process. For example, in the case of filing a tax return, the generative AI automatically creates and submits the necessary documents. If there are any unclear points, the system contacts the user via social media. For example, if necessary information is missing, the generative AI contacts the user via social media to complete the information. Finally, the system notifies the user of the procedure's progress. This allows the user to track the procedure's progress in real time. This system frees users from cumbersome procedures and allows them to track the progress of their procedures in real time. As a result, the automated procedure system streamlines user procedures and allows for real-time monitoring of their progress.

[0029] The automated procedure system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, a procedure unit, and a notification unit. The reception unit receives personal information from the user. When the user enters personal information, they enter basic information such as their name, address, and contact information. For example, the reception unit provides an interface for the user to enter basic information such as their name, address, and contact information. The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the entered information using, for example, a generation AI, and extracts data for selecting an appropriate procedure. For example, the analysis unit uses a generation AI to extract data for selecting an appropriate procedure based on the entered information. The selection unit selects an appropriate procedure based on the information analyzed by the analysis unit. For example, the selection unit uses a generation AI to select an appropriate procedure based on the analyzed information. For example, the selection unit uses a generation AI to automatically select the procedure the user needs, such as filing a tax return or canceling a subscription service, based on the analyzed information. The procedure unit proceeds with the procedure selected by the selection unit. The procedure unit uses, for example, a generation AI, to automatically create and submit the necessary documents based on the selected procedure. For example, the procedure unit uses a generation AI to automatically create and submit the necessary documents based on the selected procedure. The notification unit notifies the user of the progress of the procedure carried out by the procedure unit. The notification unit notifies the user of the progress of the procedure using a generation AI, for example. The notification unit notifies the user of the progress of the procedure using a generation AI, for example. As a result, the procedure automation system according to the embodiment can streamline the user's procedures and allow them to grasp the progress of the procedures in real time.

[0030] The reception desk is where users enter their personal information. When users enter personal information, they enter basic information such as their name, address, and contact information. For example, the reception desk provides an interface for users to enter basic information such as their name, address, and contact information. Specifically, the reception desk designs an intuitive and user-friendly interface so that users can easily enter information. The interface includes input elements such as text boxes, drop-down menus, and radio buttons, allowing users to enter the necessary information quickly and accurately. In addition, the entered information is securely transmitted using encryption technology and stored in a database. Furthermore, the reception desk has validation functions to check the integrity of the entered information and to ensure that there is no missing or incorrect information. For example, a character limit is set for the name input field, and a function is added to check the format of the postal code in the address input field. In this way, the reception desk supports users in entering accurate and complete information, and ensures that subsequent processing proceeds smoothly.

[0031] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit uses generative AI to analyze the entered information and extract data to select the appropriate procedure. Specifically, the generative AI uses natural language processing technology to analyze the entered text data and identify the user's intent and the necessary procedures. For example, it determines whether a specific procedure for a particular region is required based on the address information entered by the user. It also selects the most suitable contact method for the user based on contact information. The generative AI has an algorithm that learns from past data and cases to suggest the most appropriate procedure based on the entered information. Furthermore, the analysis unit checks the consistency and accuracy of the entered information and can request additional information from the user as needed. For example, if the address information is incomplete, the analysis unit sends a notification to the user prompting them to enter supplementary information. In this way, the analysis unit accurately analyzes the information entered by the user and supports the smooth progress of subsequent procedures.

[0032] The selection unit selects the appropriate procedure based on the information analyzed by the analysis unit. For example, the selection unit uses a generation AI to select the appropriate procedure based on the analyzed information. Specifically, the generation AI automatically selects the procedure the user needs based on the data provided by the analysis unit. For example, if a user needs to file a tax return, the generation AI analyzes the user's income and address information and selects the appropriate tax return documents. Also, if a user needs to cancel a subscription service, the generation AI analyzes the user's contract information and proposes the best way to proceed with the cancellation. The selection unit uses the generation AI's algorithm to select the most appropriate option from multiple procedure options and proposes it to the user. Furthermore, the selection unit can consider the user's past procedure history and individual needs to provide customized procedure suggestions. This allows the selection unit to quickly and accurately select the procedure the user needs, thereby improving the efficiency of the procedure.

[0033] The Procedures Department carries out the procedures selected by the Selection Department. For example, the Procedures Department uses a Generative AI to automatically create and submit the necessary documents based on the selected procedures. Specifically, the Generative AI automatically generates the necessary documents based on user input information and data provided by the Analysis Department. For example, in the case of tax returns, the Generative AI creates appropriate tax return documents based on the user's income and expense information. In the case of subscription service cancellation procedures, the Generative AI automatically carries out the necessary documents and procedures for cancellation. The Procedures Department electronically submits the documents created by the Generative AI and coordinates with relevant organizations as needed. Furthermore, the Procedures Department monitors the progress of the procedures in real time and can request additional information from the user if necessary. This allows the Procedures Department to quickly and accurately carry out the procedures required by the user, achieving increased efficiency and accuracy in procedures.

[0034] The notification unit notifies users of the progress of procedures carried out by the procedure unit. The notification unit notifies users of the progress of procedures, for example, using a generative AI. Specifically, the generative AI tracks the progress of each step of the procedure in real time and sends timely notifications to the user. For example, when a procedure is completed, the generative AI sends a notification to the user that the procedure is complete. Also, if additional information is needed during the procedure, the generative AI sends a notification to the user prompting them to provide the necessary information. The notification unit can provide multiple notification methods, such as email, SMS, and in-app notifications, according to the user's preference. Furthermore, the notification unit can collect feedback from users and use it to improve the progress of procedures and the content of notifications. This allows the notification unit to enable users to understand the progress of procedures in real time and improve the transparency and reliability of the procedures.

[0035] The reception unit can input basic information such as the user's name, address, and contact information. The reception unit can, for example, provide an interface for the user to input basic information such as their name, address, and contact information. The reception unit can also save the information entered by the user and send it to the analysis unit. This improves the accuracy of the procedure by accurately entering the user's basic information. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input the information entered by the user into a generation AI, which can analyze the information and extract data to select the appropriate procedure.

[0036] The analysis unit can select an appropriate procedure based on the input information. For example, the analysis unit can use a generation AI to analyze the input information and extract data for selecting an appropriate procedure. The analysis unit can also send the analysis results to the selection unit, which can then provide data for selecting an appropriate procedure. This improves the efficiency of the procedure by selecting an appropriate procedure based on the input information. Some or all of the above-described processing in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can input the input information into a generation AI, which will analyze the information and extract data for selecting an appropriate procedure.

[0037] The selection unit can automatically select the procedures required by the user, such as filing tax returns or canceling subscription services. For example, the selection unit can use a generative AI to select the appropriate procedure based on the analyzed information. The selection unit can also transmit the selected procedures to the procedure unit, providing the procedure unit with the necessary data to proceed with the procedures. This improves the efficiency of the procedures by automatically selecting the procedures required by the user. Some or all of the above-described processes in the selection unit may be performed using a generative AI, or they may not. For example, the selection unit can input the analyzed information into a generative AI, which can then select the appropriate procedure.

[0038] The procedure unit can automatically create and submit the necessary documents based on the selected procedure. For example, the procedure unit can use a generation AI to automatically create and submit the necessary documents based on the selected procedure. The procedure unit can also transmit the progress of the procedure to the notification unit, which can then provide data to notify the user of the procedure's progress. This improves the efficiency of the procedure by automatically creating and submitting the necessary documents. Some or all of the above-described processes in the procedure unit may be performed using a generation AI, or not. For example, the procedure unit can input the selected procedure into a generation AI, which can then automatically create and submit the necessary documents.

[0039] The notification unit can notify the user of the progress of the procedure. The notification unit notifies the user of the progress of the procedure, for example, using a generation AI. The notification unit can also notify the user of the progress of the procedure in real time. This allows the user to understand the progress of the procedure in real time by notifying the user of the progress of the procedure. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the progress of the procedure into a generation AI, and the generation AI can notify the user of the progress of the procedure.

[0040] The procedural department can contact the individual via social media if there are any unclear points. The procedural department can, for example, use a generative AI to contact the individual via social media if there are any unclear points. The procedural department can also supplement necessary information by contacting the individual via social media if there are any unclear points. This improves the accuracy of the procedure by allowing confirmation via social media when there are unclear points. Some or all of the above processing in the procedural department may be performed using a generative AI, or it may be performed without a generative AI. For example, if there are unclear points, the procedural department can input a message to the generative AI to contact the individual via social media, and the generative AI can create and send the message.

[0041] The reception desk can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception desk can use a generative AI to analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception desk can use a generative AI to analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. The reception desk can also automatically complete frequently entered information based on the user's past input history. For example, it can automatically display names and addresses that the user has frequently entered in the past as suggestions. It can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can predict and suggest information that will be used at specific times based on the user's past input history. In this way, the effort required for input can be reduced by analyzing past input history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without using a generative AI. For example, the reception desk can input the user's past input history into a generative AI, and the generative AI can provide an auto-completion function to reduce the effort required for input.

[0042] The reception desk can customize input fields based on the user's current situation and environment during input. For example, the reception desk can use generative AI to customize input fields based on the user's current situation and environment during input. Furthermore, the reception desk can improve input efficiency by changing input fields according to the user's situation and environment. For example, if the user is out, simplified input fields can be provided to allow for quick input. If the user is at home, detailed input fields can be provided to allow for accurate information entry. If the user is participating in a specific event, input fields related to that event can be prioritized. This improves input efficiency by customizing input fields according to the user's situation and environment. Some or all of the above processing in the reception desk may be performed using generative AI, or without it. For example, the reception desk can input data about the user's current situation and environment into the generative AI, which can then customize the input fields.

[0043] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, the reception desk can use a generation AI to prioritize inputting highly relevant information by considering the user's geographical location during data entry. The reception desk can also improve the efficiency of data entry by changing input fields based on the user's geographical location. For example, if the user is in a specific region, information related to that region will be prioritized. If the user is traveling, information related to the travel destination will be prioritized. If the user is at home, information related to home will be prioritized. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk can input the user's geographical location information into a generation AI, which will then prioritize inputting highly relevant information.

[0044] The reception unit can analyze the user's social media activity during input and automatically input relevant information. For example, the reception unit can use generative AI to analyze the user's social media activity and automatically input relevant information during input. Furthermore, the reception unit can improve input efficiency by modifying input fields based on the user's social media activity. For example, it can automatically complete input fields based on information shared by the user on social media. It can automatically complete input fields based on phrases and keywords frequently used by the user on social media. It can automatically input relevant information based on the user's social media activity history. This allows for the automatic input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using generative AI, or without it. For example, the reception unit can input the user's social media activity into a generative AI, which can then automatically input relevant information.

[0045] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit can use a generation AI to improve the accuracy of its analysis by referring to past analysis data during the analysis process. Furthermore, the analysis unit can improve the accuracy of its analysis when analyzing similar procedures based on past analysis data. For example, it can improve the accuracy of its analysis when analyzing similar procedures based on past analysis data. It can extract specific patterns from past analysis data to improve the accuracy of its analysis. It can improve the reliability of its analysis results by referring to past analysis data. Thus, by referring to past analysis data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input past analysis data into a generation AI, which can then improve the accuracy of its analysis.

[0046] The analysis unit can customize the analysis method based on the user's attribute information during analysis. For example, the analysis unit can use a generative AI to customize the analysis method based on the user's attribute information during analysis. For example, the analysis unit can use a generative AI to customize the analysis method based on the user's attribute information during analysis. Furthermore, the analysis unit can improve the accuracy of the analysis by changing the analysis method based on the user's attribute information. For example, it can select an appropriate analysis method based on the user's age and gender. It can customize the analysis method based on the user's occupation and lifestyle. It can adjust the analysis method based on the user's past behavioral history. In this way, the accuracy of the analysis is improved by customizing the analysis method based on the user's attribute information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the user's attribute information into a generative AI, and the generative AI can customize the analysis method.

[0047] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can use a generation AI to perform analysis while considering the user's geographical location information. For example, the analysis unit can use a generation AI to perform analysis while considering the user's geographical location information. Furthermore, the analysis unit can improve the accuracy of the analysis by changing the analysis items based on the user's geographical location information. For example, if the user is in a specific region, information related to that region will be prioritized in the analysis. If the user is traveling, information related to the travel destination will be prioritized in the analysis. If the user is at home, information related to home will be prioritized in the analysis. In this way, by considering the user's geographical location information, highly relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize the analysis of highly relevant information.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during analysis. For example, the analysis unit can use a generative AI to improve the accuracy of its analysis by referring to the user's social media activity during analysis. For example, the analysis unit can use a generative AI to improve the accuracy of its analysis by referring to the user's social media activity during analysis. The analysis unit can also improve the accuracy of its analysis by changing the analysis items based on the user's social media activity. For example, it can improve the accuracy of its analysis based on information shared by the user on social media. It can improve the accuracy of its analysis based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of its analysis based on the user's social media activity history. In this way, the accuracy of the analysis is improved by referring to social media activity. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI, and the generative AI can improve the accuracy of the analysis.

[0049] The selection unit can improve the accuracy of its selection by referring to past selection data during the selection process. For example, the selection unit can use a generative AI to improve the accuracy of its selection by referring to past selection data during the selection process. The selection unit can also improve the accuracy of selecting similar procedures based on past selection data. For example, it can improve the accuracy of selecting similar procedures based on past selection data. It can extract specific patterns from past selection data to improve selection accuracy. It can improve the reliability of the selection result by referring to past selection data. As a result, the accuracy of the selection is improved by referring to past selection data. Some or all of the above processing in the selection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the selection unit can input past selection data into a generative AI, and the generative AI can improve the accuracy of the selection.

[0050] The selection unit can customize the selection method based on the user's attribute information during the selection process. For example, the selection unit can use a generative AI to customize the selection method based on the user's attribute information during the selection process. For example, the selection unit can use a generative AI to customize the selection method based on the user's attribute information during the selection process. Furthermore, the selection unit can improve the accuracy of selections by changing the selection method based on the user's attribute information. For example, it can select an appropriate selection method based on the user's age and gender. It can customize the selection method based on the user's occupation and lifestyle. It can adjust the selection method based on the user's past behavioral history. As a result, the accuracy of selections is improved by customizing the selection method based on the user's attribute information. Some or all of the above-described processes in the selection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the selection unit can input the user's attribute information into a generative AI, which can then customize the selection method.

[0051] The selection unit can make selections while considering the user's geographical location information. The selection unit can, for example, use a generative AI to make selections while considering the user's geographical location information. For example, the selection unit can, for example, use a generative AI to make selections while considering the user's geographical location information. Furthermore, the selection unit can improve the accuracy of selections by changing the selection items based on the user's geographical location information. For example, if the user is in a specific region, procedures related to that region will be selected preferentially. If the user is traveling, procedures related to the travel destination will be selected preferentially. If the user is at home, procedures related to home will be selected preferentially. This allows for highly relevant selections by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the selection unit can input the user's geographical location information into a generative AI, which can then preferentially select highly relevant information.

[0052] The selection unit can improve the accuracy of its selections by referring to the user's social media activity during the selection process. For example, the selection unit can use a generative AI to improve the accuracy of its selections by referring to the user's social media activity during the selection process. For example, the selection unit can use a generative AI to improve the accuracy of its selections by referring to the user's social media activity during the selection process. The selection unit can also improve the accuracy of its selections by changing the selection items based on the user's social media activity. For example, it can improve the accuracy of its selections based on information shared by the user on social media. It can improve the accuracy of its selections based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of its selections based on the user's social media activity history. In this way, the accuracy of the selections is improved by referring to social media activity. Some or all of the above processing in the selection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the selection unit can input the user's social media activity into a generative AI, which can then improve the accuracy of its selections.

[0053] The procedure unit can improve the accuracy of procedures by referring to past procedure data during the procedure. For example, the procedure unit can use a generation AI to improve the accuracy of procedures by referring to past procedure data during the procedure. For example, the procedure unit can use a generation AI to improve the accuracy of procedures by referring to past procedure data during the procedure. The procedure unit can also improve the accuracy when performing similar procedures based on past procedure data. For example, it can improve the accuracy when performing similar procedures based on past procedure data. It can extract specific patterns from past procedure data to improve the accuracy of procedures. It can improve the reliability of procedure results by referring to past procedure data. As a result, the accuracy of procedures is improved by referring to past procedure data. Some or all of the above processing in the procedure unit may be performed using a generation AI or not. For example, the procedure unit can input past procedure data into a generation AI, and the generation AI can improve the accuracy of procedures.

[0054] The procedure unit can customize the procedure method based on the user's attribute information during the procedure. For example, the procedure unit can use a generative AI to customize the procedure method based on the user's attribute information during the procedure. For example, the procedure unit can use a generative AI to customize the procedure method based on the user's attribute information during the procedure. Furthermore, the procedure unit can improve the accuracy of the procedure by changing the procedure method based on the user's attribute information. For example, it can select an appropriate procedure method based on the user's age and gender. It can customize the procedure method based on the user's occupation and lifestyle. It can adjust the procedure method based on the user's past behavioral history. In this way, the accuracy of the procedure is improved by customizing the procedure method based on the user's attribute information. Some or all of the above processing in the procedure unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the procedure unit can input the user's attribute information into a generative AI, and the generative AI can customize the procedure method.

[0055] The procedure unit can perform procedures while considering the user's geographical location information. For example, the procedure unit can use a generation AI to perform procedures while considering the user's geographical location information. For example, the procedure unit can use a generation AI to perform procedures while considering the user's geographical location information. Furthermore, the procedure unit can improve the accuracy of procedures by changing procedure items based on the user's geographical location information. For example, if the user is in a specific region, procedures related to that region will be prioritized. If the user is traveling, procedures related to the travel destination will be prioritized. If the user is at home, procedures related to home will be prioritized. In this way, by considering the user's geographical location information, highly relevant procedures can be performed. Some or all of the above processing in the procedure unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the procedure unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize processing information that is highly relevant.

[0056] The procedure unit can improve the accuracy of procedures by referring to the user's social media activity during the procedure. For example, the procedure unit can use a generative AI to improve the accuracy of procedures by referring to the user's social media activity during the procedure. For example, the procedure unit can use a generative AI to improve the accuracy of procedures by referring to the user's social media activity during the procedure. The procedure unit can also improve the accuracy of procedures by changing procedure items based on the user's social media activity. For example, it can improve the accuracy of procedures based on information shared by the user on social media. It can improve the accuracy of procedures based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of procedures based on the user's social media activity history. In this way, the accuracy of procedures is improved by referring to social media activity. Some or all of the above processing in the procedure unit may be performed using a generative AI or not. For example, the procedure unit can input the user's social media activity into a generative AI, and the generative AI can improve the accuracy of the procedure.

[0057] The notification unit can improve the accuracy of notifications by referring to past notification data when issuing notifications. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to past notification data when issuing notifications. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to past notification data when issuing notifications. The notification unit can also improve the accuracy when notifying similar procedures based on past notification data. For example, it can improve the accuracy when notifying similar procedures based on past notification data. It can extract specific patterns from past notification data to improve the accuracy of notifications. It can improve the reliability of notification results by referring to past notification data. As a result, the accuracy of notifications is improved by referring to past notification data. Some or all of the above processing in the notification unit may be performed using a generation AI or not. For example, the notification unit can input past notification data into a generation AI, and the generation AI can improve the accuracy of notifications.

[0058] The notification unit can customize the notification method based on the user's attribute information when sending a notification. For example, the notification unit can use a generative AI to customize the notification method based on the user's attribute information when sending a notification. For example, the notification unit can use a generative AI to customize the notification method based on the user's attribute information when sending a notification. The notification unit can also improve the accuracy of notifications by changing the notification method based on the user's attribute information. For example, it can select an appropriate notification method based on the user's age and gender. It can customize the notification method based on the user's occupation and lifestyle. It can adjust the notification method based on the user's past behavior history. In this way, the accuracy of notifications is improved by customizing the notification method based on the user's attribute information. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the notification unit can input the user's attribute information into a generative AI, and the generative AI can customize the notification method.

[0059] The notification unit can send notifications while considering the user's geographical location. For example, the notification unit can use a generation AI to send notifications while considering the user's geographical location. The notification unit can also improve the accuracy of notifications by changing the notification content based on the user's geographical location. For example, if the user is in a specific region, notifications related to that region will be displayed preferentially. If the user is traveling, notifications related to the travel destination will be displayed preferentially. If the user is at home, notifications related to home will be displayed preferentially. In this way, by considering the user's geographical location, highly relevant notifications can be sent. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize sending highly relevant information.

[0060] The notification unit can improve the accuracy of notifications by referring to the user's social media activity at the time of notification. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to the user's social media activity at the time of notification. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to the user's social media activity at the time of notification. The notification unit can also improve the accuracy of notifications by changing the notification content based on the user's social media activity. For example, it can improve the accuracy of notifications based on information shared by the user on social media. It can improve the accuracy of notifications based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of notifications based on the user's activity history on social media. In this way, the accuracy of notifications is improved by referring to social media activity. Some or all of the above processing in the notification unit may be performed using a generation AI or not. For example, the notification unit can input the user's social media activity into a generation AI, and the generation AI can improve the accuracy of notifications.

[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 reception system can dynamically change the input order based on the user's input. For example, if a user interrupts their input, the system adjusts the order so that they can resume from where they left off the next time they input. Furthermore, if a user wants to prioritize entering specific information, the system can change the order so that this information is entered first. It is also possible to optimize the input order according to the user's input speed and accuracy. This improves the user's input experience and increases the efficiency of the procedure.

[0063] The analysis unit checks for consistency in user input and can display warnings if inconsistencies are found. For example, if a user enters different addresses in different places, the analysis unit will detect the inconsistency and prompt the user for confirmation. Furthermore, if the entered information is incomplete, it can provide guidance to complete the necessary information. The analysis unit can also check whether the input meets legal requirements and notify the user if any information is missing. This improves the accuracy of the procedure and reduces the effort required from the user.

[0064] The selection function can refer to the user's past procedure history and prioritize suggesting similar procedures. For example, if a user has filed a tax return in the past, that procedure will be prioritized when filing the next tax return. It can also select the most suitable procedure based on the results of procedures the user has performed in the past. Furthermore, based on the user's procedure history, it can predict the progress of the procedure and prepare necessary documents and information in advance. This improves the efficiency of the procedure and reduces the burden on the user.

[0065] The procedure unit can customize how the procedure proceeds based on the user's input. For example, if the user is in a hurry, it can provide options to expedite the procedure. If the user requests detailed explanations, it can provide detailed guidance for each step of the procedure. Furthermore, it can optimize the order of the procedure and efficiently collect necessary information based on the user's input. This results in a smoother procedure and improved user satisfaction.

[0066] The notification unit can adjust the frequency of notifications according to the user's progress in the process. For example, if the process is progressing smoothly, the frequency of notifications can be reduced to lessen the user's burden. Conversely, if the process is behind schedule, the frequency of notifications can be increased to allow the user to check the progress. Furthermore, it is possible to customize the content of notifications based on the user's progress in the process and provide necessary information. This allows the user to properly understand the progress of the process and take necessary actions quickly.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk allows the user to enter personal information. When the user enters personal information, they will enter basic information such as their name, address, and contact information. For example, the reception desk provides an interface for the user to enter basic information such as their name, address, and contact information. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the entered information using, for example, a generation AI, and extracts data to select the appropriate procedure. Step 3: The selection unit selects the appropriate procedure based on the information analyzed by the analysis unit. For example, the selection unit uses a generation AI to automatically select the procedure the user needs, such as filing a tax return or canceling a subscription service, based on the analyzed information. Step 4: The Procedure Unit proceeds with the procedure selected by the Selection Unit. The Procedure Unit, for example, uses a Generative AI to automatically create and submit the necessary documents based on the selected procedure. Step 5: The notification unit notifies the user of the progress of the procedure carried out by the procedure unit. The notification unit notifies the user of the progress of the procedure, for example, using a generative AI.

[0069] (Example of form 2) An embodiment of the present invention provides a procedure automation system that automates text-based procedures using a generative AI. This system works as follows: the user inputs personal information, the generative AI analyzes the input, selects the appropriate procedure, and proceeds with the process. If there are any unclear points, the system contacts the user via social media and notifies the user of the procedure's progress. This mechanism frees users from cumbersome procedures and allows them to track the procedure's progress in real time. For example, the user inputs personal information, such as their name, address, and contact details. Next, the generative AI analyzes the input information. Based on the input information, the generative AI selects the appropriate procedure. For example, it automatically selects the procedure the user needs, such as filing a tax return or canceling a subscription service. Based on the selected procedure, the generative AI proceeds with the process. For example, in the case of filing a tax return, the generative AI automatically creates and submits the necessary documents. If there are any unclear points, the system contacts the user via social media. For example, if necessary information is missing, the generative AI contacts the user via social media to complete the information. Finally, the system notifies the user of the procedure's progress. This allows the user to track the procedure's progress in real time. This system frees users from cumbersome procedures and allows them to track the progress of their procedures in real time. As a result, the automated procedure system streamlines user procedures and allows for real-time monitoring of their progress.

[0070] The automated procedure system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, a procedure unit, and a notification unit. The reception unit receives personal information from the user. When the user enters personal information, they enter basic information such as their name, address, and contact information. For example, the reception unit provides an interface for the user to enter basic information such as their name, address, and contact information. The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the entered information using, for example, a generation AI, and extracts data for selecting an appropriate procedure. For example, the analysis unit uses a generation AI to extract data for selecting an appropriate procedure based on the entered information. The selection unit selects an appropriate procedure based on the information analyzed by the analysis unit. For example, the selection unit uses a generation AI to select an appropriate procedure based on the analyzed information. For example, the selection unit uses a generation AI to automatically select the procedure the user needs, such as filing a tax return or canceling a subscription service, based on the analyzed information. The procedure unit proceeds with the procedure selected by the selection unit. The procedure unit uses, for example, a generation AI, to automatically create and submit the necessary documents based on the selected procedure. For example, the procedure unit uses a generation AI to automatically create and submit the necessary documents based on the selected procedure. The notification unit notifies the user of the progress of the procedure carried out by the procedure unit. The notification unit notifies the user of the progress of the procedure using a generation AI, for example. The notification unit notifies the user of the progress of the procedure using a generation AI, for example. As a result, the procedure automation system according to the embodiment can streamline the user's procedures and allow them to grasp the progress of the procedures in real time.

[0071] The reception desk is where users enter their personal information. When users enter personal information, they enter basic information such as their name, address, and contact information. For example, the reception desk provides an interface for users to enter basic information such as their name, address, and contact information. Specifically, the reception desk designs an intuitive and user-friendly interface so that users can easily enter information. The interface includes input elements such as text boxes, drop-down menus, and radio buttons, allowing users to enter the necessary information quickly and accurately. In addition, the entered information is securely transmitted using encryption technology and stored in a database. Furthermore, the reception desk has validation functions to check the integrity of the entered information and to ensure that there is no missing or incorrect information. For example, a character limit is set for the name input field, and a function is added to check the format of the postal code in the address input field. In this way, the reception desk supports users in entering accurate and complete information, and ensures that subsequent processing proceeds smoothly.

[0072] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit uses generative AI to analyze the entered information and extract data to select the appropriate procedure. Specifically, the generative AI uses natural language processing technology to analyze the entered text data and identify the user's intent and the necessary procedures. For example, it determines whether a specific procedure for a particular region is required based on the address information entered by the user. It also selects the most suitable contact method for the user based on contact information. The generative AI has an algorithm that learns from past data and cases to suggest the most appropriate procedure based on the entered information. Furthermore, the analysis unit checks the consistency and accuracy of the entered information and can request additional information from the user as needed. For example, if the address information is incomplete, the analysis unit sends a notification to the user prompting them to enter supplementary information. In this way, the analysis unit accurately analyzes the information entered by the user and supports the smooth progress of subsequent procedures.

[0073] The selection unit selects the appropriate procedure based on the information analyzed by the analysis unit. For example, the selection unit uses a generation AI to select the appropriate procedure based on the analyzed information. Specifically, the generation AI automatically selects the procedure the user needs based on the data provided by the analysis unit. For example, if a user needs to file a tax return, the generation AI analyzes the user's income and address information and selects the appropriate tax return documents. Also, if a user needs to cancel a subscription service, the generation AI analyzes the user's contract information and proposes the best way to proceed with the cancellation. The selection unit uses the generation AI's algorithm to select the most appropriate option from multiple procedure options and proposes it to the user. Furthermore, the selection unit can consider the user's past procedure history and individual needs to provide customized procedure suggestions. This allows the selection unit to quickly and accurately select the procedure the user needs, thereby improving the efficiency of the procedure.

[0074] The Procedures Department carries out the procedures selected by the Selection Department. For example, the Procedures Department uses a Generative AI to automatically create and submit the necessary documents based on the selected procedures. Specifically, the Generative AI automatically generates the necessary documents based on user input information and data provided by the Analysis Department. For example, in the case of tax returns, the Generative AI creates appropriate tax return documents based on the user's income and expense information. In the case of subscription service cancellation procedures, the Generative AI automatically carries out the necessary documents and procedures for cancellation. The Procedures Department electronically submits the documents created by the Generative AI and coordinates with relevant organizations as needed. Furthermore, the Procedures Department monitors the progress of the procedures in real time and can request additional information from the user if necessary. This allows the Procedures Department to quickly and accurately carry out the procedures required by the user, achieving increased efficiency and accuracy in procedures.

[0075] The notification unit notifies users of the progress of procedures carried out by the procedure unit. The notification unit notifies users of the progress of procedures, for example, using a generative AI. Specifically, the generative AI tracks the progress of each step of the procedure in real time and sends timely notifications to the user. For example, when a procedure is completed, the generative AI sends a notification to the user that the procedure is complete. Also, if additional information is needed during the procedure, the generative AI sends a notification to the user prompting them to provide the necessary information. The notification unit can provide multiple notification methods, such as email, SMS, and in-app notifications, according to the user's preference. Furthermore, the notification unit can collect feedback from users and use it to improve the progress of procedures and the content of notifications. This allows the notification unit to enable users to understand the progress of procedures in real time and improve the transparency and reliability of the procedures.

[0076] The reception unit can input basic information such as the user's name, address, and contact information. The reception unit can, for example, provide an interface for the user to input basic information such as their name, address, and contact information. The reception unit can also save the information entered by the user and send it to the analysis unit. This improves the accuracy of the procedure by accurately entering the user's basic information. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input the information entered by the user into a generation AI, which can analyze the information and extract data to select the appropriate procedure.

[0077] The analysis unit can select an appropriate procedure based on the input information. For example, the analysis unit can use a generation AI to analyze the input information and extract data for selecting an appropriate procedure. The analysis unit can also send the analysis results to the selection unit, which can then provide data for selecting an appropriate procedure. This improves the efficiency of the procedure by selecting an appropriate procedure based on the input information. Some or all of the above-described processing in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can input the input information into a generation AI, which will analyze the information and extract data for selecting an appropriate procedure.

[0078] The selection unit can automatically select the procedures required by the user, such as filing tax returns or canceling subscription services. For example, the selection unit can use a generative AI to select the appropriate procedure based on the analyzed information. The selection unit can also transmit the selected procedures to the procedure unit, providing the procedure unit with the necessary data to proceed with the procedures. This improves the efficiency of the procedures by automatically selecting the procedures required by the user. Some or all of the above-described processes in the selection unit may be performed using a generative AI, or they may not. For example, the selection unit can input the analyzed information into a generative AI, which can then select the appropriate procedure.

[0079] The procedure unit can automatically create and submit the necessary documents based on the selected procedure. For example, the procedure unit can use a generation AI to automatically create and submit the necessary documents based on the selected procedure. The procedure unit can also transmit the progress of the procedure to the notification unit, which can then provide data to notify the user of the procedure's progress. This improves the efficiency of the procedure by automatically creating and submitting the necessary documents. Some or all of the above-described processes in the procedure unit may be performed using a generation AI, or not. For example, the procedure unit can input the selected procedure into a generation AI, which can then automatically create and submit the necessary documents.

[0080] The notification unit can notify the user of the progress of the procedure. The notification unit notifies the user of the progress of the procedure, for example, using a generation AI. The notification unit can also notify the user of the progress of the procedure in real time. This allows the user to understand the progress of the procedure in real time by notifying the user of the progress of the procedure. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the progress of the procedure into a generation AI, and the generation AI can notify the user of the progress of the procedure.

[0081] The procedural department can contact the individual via social media if there are any unclear points. The procedural department can, for example, use a generative AI to contact the individual via social media if there are any unclear points. The procedural department can also supplement necessary information by contacting the individual via social media if there are any unclear points. This improves the accuracy of the procedure by allowing confirmation via social media when there are unclear points. Some or all of the above processing in the procedural department may be performed using a generative AI, or it may be performed without a generative AI. For example, if there are unclear points, the procedural department can input a message to the generative AI to contact the individual via social media, and the generative AI can create and send the message.

[0082] The reception desk can estimate the user's emotions and dynamically change the design of the input form based on the estimated emotions. For example, the reception desk can use generative AI to estimate the user's emotions and dynamically change the design of the input form based on the estimated emotions. Furthermore, by changing the design of the input form according to the user's emotions, the reception desk can improve the user's input experience. For example, if the user is stressed, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick input of basic information. This improves the user's input experience by changing the design of the input form according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception area can input data for estimating the user's emotions into a generative AI, which can then estimate the emotions and dynamically change the design of the input form.

[0083] The reception desk can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception desk can use a generative AI to analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception desk can use a generative AI to analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. The reception desk can also automatically complete frequently entered information based on the user's past input history. For example, it can automatically display names and addresses that the user has frequently entered in the past as suggestions. It can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can predict and suggest information that will be used at specific times based on the user's past input history. In this way, the effort required for input can be reduced by analyzing past input history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without using a generative AI. For example, the reception desk can input the user's past input history into a generative AI, and the generative AI can provide an auto-completion function to reduce the effort required for input.

[0084] The reception desk can customize input fields based on the user's current situation and environment during input. For example, the reception desk can use generative AI to customize input fields based on the user's current situation and environment during input. Furthermore, the reception desk can improve input efficiency by changing input fields according to the user's situation and environment. For example, if the user is out, simplified input fields can be provided to allow for quick input. If the user is at home, detailed input fields can be provided to allow for accurate information entry. If the user is participating in a specific event, input fields related to that event can be prioritized. This improves input efficiency by customizing input fields according to the user's situation and environment. Some or all of the above processing in the reception desk may be performed using generative AI, or without it. For example, the reception desk can input data about the user's current situation and environment into the generative AI, which can then customize the input fields.

[0085] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, the reception desk can use generative AI to estimate the user's emotions and determine the priority of inputs based on the estimated emotions. Furthermore, the reception desk can improve input efficiency by changing the priority of inputs according to the user's emotions. For example, if the user is nervous, the most important input items are displayed first, and other items are postponed. If the user is relaxed, all input items are displayed at once, allowing for free input. If the user is in a hurry, the easiest items to input are displayed first, allowing for quick processing. This improves input efficiency by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception area can input data for estimating the user's emotions into a generative AI, which can then estimate the emotions and determine the priority of the inputs.

[0086] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, the reception desk can use a generation AI to prioritize inputting highly relevant information by considering the user's geographical location during data entry. The reception desk can also improve the efficiency of data entry by changing input fields based on the user's geographical location. For example, if the user is in a specific region, information related to that region will be prioritized. If the user is traveling, information related to the travel destination will be prioritized. If the user is at home, information related to home will be prioritized. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk can input the user's geographical location information into a generation AI, which will then prioritize inputting highly relevant information.

[0087] The reception unit can analyze the user's social media activity during input and automatically input relevant information. For example, the reception unit can use generative AI to analyze the user's social media activity and automatically input relevant information during input. Furthermore, the reception unit can improve input efficiency by modifying input fields based on the user's social media activity. For example, it can automatically complete input fields based on information shared by the user on social media. It can automatically complete input fields based on phrases and keywords frequently used by the user on social media. It can automatically input relevant information based on the user's social media activity history. This allows for the automatic input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using generative AI, or without it. For example, the reception unit can input the user's social media activity into a generative AI, which can then automatically input relevant information.

[0088] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit uses generative AI to estimate the user's emotions and dynamically adjusts the analysis algorithm based on the estimated emotions. Furthermore, the analysis unit can improve the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. For example, if the user is relaxed, a detailed analysis is performed to provide highly accurate results. If the user is in a hurry, a quick analysis is performed to provide the minimum necessary results. If the user is stressed, a simplified analysis is performed to reduce the user's burden. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data for estimating the user's emotions into a generative AI, which can then estimate the emotions and dynamically adjust the analysis algorithm.

[0089] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit can use a generation AI to improve the accuracy of its analysis by referring to past analysis data during the analysis process. Furthermore, the analysis unit can improve the accuracy of its analysis when analyzing similar procedures based on past analysis data. For example, it can improve the accuracy of its analysis when analyzing similar procedures based on past analysis data. It can extract specific patterns from past analysis data to improve the accuracy of its analysis. It can improve the reliability of its analysis results by referring to past analysis data. Thus, by referring to past analysis data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input past analysis data into a generation AI, which can then improve the accuracy of its analysis.

[0090] The analysis unit can customize the analysis method based on the user's attribute information during analysis. For example, the analysis unit can use a generative AI to customize the analysis method based on the user's attribute information during analysis. For example, the analysis unit can use a generative AI to customize the analysis method based on the user's attribute information during analysis. Furthermore, the analysis unit can improve the accuracy of the analysis by changing the analysis method based on the user's attribute information. For example, it can select an appropriate analysis method based on the user's age and gender. It can customize the analysis method based on the user's occupation and lifestyle. It can adjust the analysis method based on the user's past behavioral history. In this way, the accuracy of the analysis is improved by customizing the analysis method based on the user's attribute information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the user's attribute information into a generative AI, and the generative AI can customize the analysis method.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can use a generative AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can use a generative AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Furthermore, the analysis unit can improve user understanding by changing the display method of the analysis results according to the user's emotions. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that gets straight to the point is provided. In this way, user understanding is improved by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit can input data to estimate the user's emotions into a generating AI, which then estimates the emotions and adjusts how the analysis results are displayed.

[0092] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can use a generation AI to perform analysis while considering the user's geographical location information. For example, the analysis unit can use a generation AI to perform analysis while considering the user's geographical location information. Furthermore, the analysis unit can improve the accuracy of the analysis by changing the analysis items based on the user's geographical location information. For example, if the user is in a specific region, information related to that region will be prioritized in the analysis. If the user is traveling, information related to the travel destination will be prioritized in the analysis. If the user is at home, information related to home will be prioritized in the analysis. In this way, by considering the user's geographical location information, highly relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize the analysis of highly relevant information.

[0093] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during analysis. For example, the analysis unit can use a generative AI to improve the accuracy of its analysis by referring to the user's social media activity during analysis. For example, the analysis unit can use a generative AI to improve the accuracy of its analysis by referring to the user's social media activity during analysis. The analysis unit can also improve the accuracy of its analysis by changing the analysis items based on the user's social media activity. For example, it can improve the accuracy of its analysis based on information shared by the user on social media. It can improve the accuracy of its analysis based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of its analysis based on the user's social media activity history. In this way, the accuracy of the analysis is improved by referring to social media activity. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI, and the generative AI can improve the accuracy of the analysis.

[0094] The selection unit can estimate the user's emotions and adjust the procedure selection criteria based on the estimated user emotions. For example, the selection unit can use generative AI to estimate the user's emotions and adjust the procedure selection criteria based on the estimated user emotions. Furthermore, the selection unit can improve the efficiency of procedures by changing the procedure selection criteria according to the user's emotions. For example, if the user is relaxed, it can provide detailed procedure options and broaden the choices. If the user is in a hurry, it can prioritize selecting the easiest and quickest procedure. If the user is stressed, it can prioritize selecting a simple and intuitive procedure. This improves the efficiency of procedures by adjusting the procedure selection criteria 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using generative AI or not. For example, the selection unit can input data to estimate the user's emotions into a generating AI, which can then estimate the emotions and adjust the selection criteria for the procedure.

[0095] The selection unit can improve the accuracy of its selection by referring to past selection data during the selection process. For example, the selection unit can use a generative AI to improve the accuracy of its selection by referring to past selection data during the selection process. The selection unit can also improve the accuracy of selecting similar procedures based on past selection data. For example, it can improve the accuracy of selecting similar procedures based on past selection data. It can extract specific patterns from past selection data to improve selection accuracy. It can improve the reliability of the selection result by referring to past selection data. As a result, the accuracy of the selection is improved by referring to past selection data. Some or all of the above processing in the selection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the selection unit can input past selection data into a generative AI, and the generative AI can improve the accuracy of the selection.

[0096] The selection unit can customize the selection method based on the user's attribute information during the selection process. For example, the selection unit can use a generative AI to customize the selection method based on the user's attribute information during the selection process. For example, the selection unit can use a generative AI to customize the selection method based on the user's attribute information during the selection process. Furthermore, the selection unit can improve the accuracy of selections by changing the selection method based on the user's attribute information. For example, it can select an appropriate selection method based on the user's age and gender. It can customize the selection method based on the user's occupation and lifestyle. It can adjust the selection method based on the user's past behavioral history. As a result, the accuracy of selections is improved by customizing the selection method based on the user's attribute information. Some or all of the above-described processes in the selection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the selection unit can input the user's attribute information into a generative AI, which can then customize the selection method.

[0097] The selection unit can estimate the user's emotions and adjust the display method of the selection results based on the estimated user emotions. The selection unit can estimate the user's emotions using, for example, generative AI and adjust the display method of the selection results based on the estimated user emotions. For example, the selection unit can estimate the user's emotions using generative AI and adjust the display method of the selection results based on the estimated user emotions. Furthermore, the selection unit can improve user understanding by changing the display method of the selection results according to the user's emotions. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that gets straight to the point is provided. In this way, user understanding is improved by adjusting the display method of the selection results according to the user's emotions. Emotion estimation is achieved using, for example, 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 selection unit may be performed using generative AI or not using generative AI. For example, the selection unit can input data to estimate the user's emotions into a generating AI, which can then estimate the emotions and adjust how the selection results are displayed.

[0098] The selection unit can make selections while considering the user's geographical location information. The selection unit can, for example, use a generative AI to make selections while considering the user's geographical location information. For example, the selection unit can, for example, use a generative AI to make selections while considering the user's geographical location information. Furthermore, the selection unit can improve the accuracy of selections by changing the selection items based on the user's geographical location information. For example, if the user is in a specific region, procedures related to that region will be selected preferentially. If the user is traveling, procedures related to the travel destination will be selected preferentially. If the user is at home, procedures related to home will be selected preferentially. This allows for highly relevant selections by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the selection unit can input the user's geographical location information into a generative AI, which can then preferentially select highly relevant information.

[0099] The selection unit can improve the accuracy of its selections by referring to the user's social media activity during the selection process. For example, the selection unit can use a generative AI to improve the accuracy of its selections by referring to the user's social media activity during the selection process. For example, the selection unit can use a generative AI to improve the accuracy of its selections by referring to the user's social media activity during the selection process. The selection unit can also improve the accuracy of its selections by changing the selection items based on the user's social media activity. For example, it can improve the accuracy of its selections based on information shared by the user on social media. It can improve the accuracy of its selections based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of its selections based on the user's social media activity history. In this way, the accuracy of the selections is improved by referring to social media activity. Some or all of the above processing in the selection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the selection unit can input the user's social media activity into a generative AI, which can then improve the accuracy of its selections.

[0100] The procedure unit can estimate the user's emotions and adjust the procedure's execution based on those emotions. For example, the procedure unit can use generative AI to estimate the user's emotions and adjust the procedure's execution based on those emotions. Furthermore, the procedure unit can improve the efficiency of the procedure by changing its execution method according to the user's emotions. For example, if the user is relaxed, it can provide detailed procedural instructions and proceed with the procedure. If the user is in a hurry, it can proceed quickly and provide only the necessary explanations. If the user is stressed, it can provide a simple and intuitive procedure execution method. This improves the efficiency of the procedure by adjusting its execution method 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the procedure unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the procedure unit can input data for estimating the user's emotions into a generative AI, which can then estimate the emotions and adjust how the procedure proceeds.

[0101] The procedure unit can improve the accuracy of procedures by referring to past procedure data during the procedure. For example, the procedure unit can use a generation AI to improve the accuracy of procedures by referring to past procedure data during the procedure. For example, the procedure unit can use a generation AI to improve the accuracy of procedures by referring to past procedure data during the procedure. The procedure unit can also improve the accuracy when performing similar procedures based on past procedure data. For example, it can improve the accuracy when performing similar procedures based on past procedure data. It can extract specific patterns from past procedure data to improve the accuracy of procedures. It can improve the reliability of procedure results by referring to past procedure data. As a result, the accuracy of procedures is improved by referring to past procedure data. Some or all of the above processing in the procedure unit may be performed using a generation AI or not. For example, the procedure unit can input past procedure data into a generation AI, and the generation AI can improve the accuracy of procedures.

[0102] The procedure unit can customize the procedure method based on the user's attribute information during the procedure. For example, the procedure unit can use a generative AI to customize the procedure method based on the user's attribute information during the procedure. For example, the procedure unit can use a generative AI to customize the procedure method based on the user's attribute information during the procedure. Furthermore, the procedure unit can improve the accuracy of the procedure by changing the procedure method based on the user's attribute information. For example, it can select an appropriate procedure method based on the user's age and gender. It can customize the procedure method based on the user's occupation and lifestyle. It can adjust the procedure method based on the user's past behavioral history. In this way, the accuracy of the procedure is improved by customizing the procedure method based on the user's attribute information. Some or all of the above processing in the procedure unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the procedure unit can input the user's attribute information into a generative AI, and the generative AI can customize the procedure method.

[0103] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated emotions. For example, the procedure unit can use generative AI to estimate the user's emotions and determine the priority of procedures based on the estimated emotions. Furthermore, the procedure unit can improve the efficiency of procedures by changing the priority of procedures according to the user's emotions. For example, if the user is nervous, the most important procedures are prioritized, and other procedures are postponed. If the user is relaxed, all procedures are performed at once, allowing for free selection. If the user is in a hurry, the easiest procedures to complete are prioritized. This improves the efficiency of procedures by determining the priority of procedures 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the procedure unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the procedure unit can input data for estimating the user's emotions into a generative AI, which can then estimate the emotions and determine the priority of the procedure.

[0104] The procedure unit can perform procedures while considering the user's geographical location information. For example, the procedure unit can use a generation AI to perform procedures while considering the user's geographical location information. For example, the procedure unit can use a generation AI to perform procedures while considering the user's geographical location information. Furthermore, the procedure unit can improve the accuracy of procedures by changing procedure items based on the user's geographical location information. For example, if the user is in a specific region, procedures related to that region will be prioritized. If the user is traveling, procedures related to the travel destination will be prioritized. If the user is at home, procedures related to home will be prioritized. In this way, by considering the user's geographical location information, highly relevant procedures can be performed. Some or all of the above processing in the procedure unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the procedure unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize processing information that is highly relevant.

[0105] The procedure unit can improve the accuracy of procedures by referring to the user's social media activity during the procedure. For example, the procedure unit can use a generative AI to improve the accuracy of procedures by referring to the user's social media activity during the procedure. For example, the procedure unit can use a generative AI to improve the accuracy of procedures by referring to the user's social media activity during the procedure. The procedure unit can also improve the accuracy of procedures by changing procedure items based on the user's social media activity. For example, it can improve the accuracy of procedures based on information shared by the user on social media. It can improve the accuracy of procedures based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of procedures based on the user's social media activity history. In this way, the accuracy of procedures is improved by referring to social media activity. Some or all of the above processing in the procedure unit may be performed using a generative AI or not. For example, the procedure unit can input the user's social media activity into a generative AI, and the generative AI can improve the accuracy of the procedure.

[0106] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, the notification unit can use generative AI to estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, the notification unit can use generative AI to estimate the user's emotions and adjust the content of the notification based on the estimated emotions. The notification unit can also improve user understanding by changing the content of the notification according to the user's emotions. For example, if the user is nervous, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a notification that includes detailed information. If the user is in a hurry, it can provide a notification that gets straight to the point. In this way, user understanding is improved by adjusting the content of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using generative AI or not using generative AI. For example, the notification unit can input data to estimate the user's emotions into a generating AI, which can then estimate the emotions and adjust the content of the notification.

[0107] The notification unit can improve the accuracy of notifications by referring to past notification data when issuing notifications. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to past notification data when issuing notifications. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to past notification data when issuing notifications. The notification unit can also improve the accuracy when notifying similar procedures based on past notification data. For example, it can improve the accuracy when notifying similar procedures based on past notification data. It can extract specific patterns from past notification data to improve the accuracy of notifications. It can improve the reliability of notification results by referring to past notification data. As a result, the accuracy of notifications is improved by referring to past notification data. Some or all of the above processing in the notification unit may be performed using a generation AI or not. For example, the notification unit can input past notification data into a generation AI, and the generation AI can improve the accuracy of notifications.

[0108] The notification unit can customize the notification method based on the user's attribute information when sending a notification. For example, the notification unit can use a generative AI to customize the notification method based on the user's attribute information when sending a notification. For example, the notification unit can use a generative AI to customize the notification method based on the user's attribute information when sending a notification. The notification unit can also improve the accuracy of notifications by changing the notification method based on the user's attribute information. For example, it can select an appropriate notification method based on the user's age and gender. It can customize the notification method based on the user's occupation and lifestyle. It can adjust the notification method based on the user's past behavior history. In this way, the accuracy of notifications is improved by customizing the notification method based on the user's attribute information. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the notification unit can input the user's attribute information into a generative AI, and the generative AI can customize the notification method.

[0109] The notification unit can estimate the user's emotions and determine the priority of notifications based on those emotions. For example, the notification unit can use generative AI to estimate the user's emotions and determine the priority of notifications based on those emotions. Furthermore, the notification unit can prioritize important notifications by changing the priority of notifications according to the user's emotions. For example, if the user is stressed, the most important notifications will be displayed first, and other notifications will be delayed. If the user is relaxed, all notifications will be displayed at once for the user to review freely. If the user is in a hurry, the easiest notifications to review will be displayed first. In this way, important notifications can be prioritized by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the notification unit can input data for estimating the user's emotions into a generative AI, which can then estimate the emotions and determine the priority of notifications.

[0110] The notification unit can send notifications while considering the user's geographical location. For example, the notification unit can use a generation AI to send notifications while considering the user's geographical location. The notification unit can also improve the accuracy of notifications by changing the notification content based on the user's geographical location. For example, if the user is in a specific region, notifications related to that region will be displayed preferentially. If the user is traveling, notifications related to the travel destination will be displayed preferentially. If the user is at home, notifications related to home will be displayed preferentially. In this way, by considering the user's geographical location, highly relevant notifications can be sent. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize sending highly relevant information.

[0111] The notification unit can improve the accuracy of notifications by referring to the user's social media activity at the time of notification. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to the user's social media activity at the time of notification. For example, the notification unit can use a generation AI to improve the accuracy of notifications by referring to the user's social media activity at the time of notification. The notification unit can also improve the accuracy of notifications by changing the notification content based on the user's social media activity. For example, it can improve the accuracy of notifications based on information shared by the user on social media. It can improve the accuracy of notifications based on phrases and keywords frequently used by the user on social media. It can improve the accuracy of notifications based on the user's activity history on social media. In this way, the accuracy of notifications is improved by referring to social media activity. Some or all of the above processing in the notification unit may be performed using a generation AI or not. For example, the notification unit can input the user's social media activity into a generation AI, and the generation AI can improve the accuracy of notifications.

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

[0113] The reception system can dynamically change the input order based on the user's input. For example, if a user interrupts their input, the system adjusts the order so that they can resume from where they left off the next time they input. Furthermore, if a user wants to prioritize entering specific information, the system can change the order so that this information is entered first. It is also possible to optimize the input order according to the user's input speed and accuracy. This improves the user's input experience and increases the efficiency of the procedure.

[0114] The analysis unit checks for consistency in user input and can display warnings if inconsistencies are found. For example, if a user enters different addresses in different places, the analysis unit will detect the inconsistency and prompt the user for confirmation. Furthermore, if the entered information is incomplete, it can provide guidance to complete the necessary information. The analysis unit can also check whether the input meets legal requirements and notify the user if any information is missing. This improves the accuracy of the procedure and reduces the effort required from the user.

[0115] The selection function can refer to the user's past procedure history and prioritize suggesting similar procedures. For example, if a user has filed a tax return in the past, that procedure will be prioritized when filing the next tax return. It can also select the most suitable procedure based on the results of procedures the user has performed in the past. Furthermore, based on the user's procedure history, it can predict the progress of the procedure and prepare necessary documents and information in advance. This improves the efficiency of the procedure and reduces the burden on the user.

[0116] The procedure unit can customize how the procedure proceeds based on the user's input. For example, if the user is in a hurry, it can provide options to expedite the procedure. If the user requests detailed explanations, it can provide detailed guidance for each step of the procedure. Furthermore, it can optimize the order of the procedure and efficiently collect necessary information based on the user's input. This results in a smoother procedure and improved user satisfaction.

[0117] The notification unit can adjust the frequency of notifications according to the user's progress in the process. For example, if the process is progressing smoothly, the frequency of notifications can be reduced to lessen the user's burden. Conversely, if the process is behind schedule, the frequency of notifications can be increased to allow the user to check the progress. Furthermore, it is possible to customize the content of notifications based on the user's progress in the process and provide necessary information. This allows the user to properly understand the progress of the process and take necessary actions quickly.

[0118] The reception desk can estimate the user's emotions and provide input support based on those emotions. For example, if the user is stressed, it can enhance input support and suggest simpler input methods. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick entry of basic information. This provides emotion-responsive input support and improves the input experience.

[0119] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is nervous, it can provide simple and easy-to-understand feedback. If the user is relaxed, it can provide detailed analysis results and add explanations to make them easier for the user to understand. Furthermore, if the user is in a hurry, it can provide concise feedback to help them quickly move on to the next step. This provides feedback that is tailored to the user's emotions and improves their understanding of the analysis results.

[0120] The selection function can estimate the user's emotions and customize the procedural options based on those emotions. For example, if the user is stressed, it will prioritize simple and intuitive procedures. If the user is relaxed, it will offer more detailed procedural options, broadening the choices. Furthermore, if the user is in a hurry, it will prioritize the easiest and quickest procedure to complete. This provides procedural options tailored to the user's emotions, improving the efficiency of the process.

[0121] The procedure unit can estimate the user's emotions and adjust the pace of the procedure based on those emotions. For example, if the user is relaxed, it can provide detailed instructions and proceed with the procedure. If the user is in a hurry, it can proceed quickly and provide only the necessary explanations. Furthermore, if the user is stressed, it can provide a simple and intuitive way to proceed with the procedure. This adjusts the pace of the procedure according to the user's emotions, improving the efficiency of the procedure.

[0122] The notification system can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, important notifications will be displayed first, while other notifications will be delayed. If the user is relaxed, all notifications can be displayed at once for them to review at their leisure. Furthermore, if the user is in a hurry, the notifications that are easiest to review will be displayed first. This ensures that notification timing is adjusted according to the user's emotions, and important notifications are displayed appropriately.

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

[0124] Step 1: The reception desk allows the user to enter personal information. When the user enters personal information, they will enter basic information such as their name, address, and contact information. For example, the reception desk provides an interface for the user to enter basic information such as their name, address, and contact information. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the entered information using, for example, a generation AI, and extracts data to select the appropriate procedure. Step 3: The selection unit selects the appropriate procedure based on the information analyzed by the analysis unit. For example, the selection unit uses a generation AI to automatically select the procedure the user needs, such as filing a tax return or canceling a subscription service, based on the analyzed information. Step 4: The Procedure Unit proceeds with the procedure selected by the Selection Unit. The Procedure Unit, for example, uses a Generative AI to automatically create and submit the necessary documents based on the selected procedure. Step 5: The notification unit notifies the user of the progress of the procedure carried out by the procedure unit. The notification unit notifies the user of the progress of the procedure, for example, using a generative AI.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, procedure unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input basic information such as name, address, and contact information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generating AI to analyze the input information and extract data for selecting the appropriate procedure. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically selects the procedure required by the user, such as filing a tax return or canceling a subscription service, based on the analyzed information. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically creates and submits the necessary documents based on the selected procedure. The notification unit is implemented by the output device 40 of the smart device 14 and notifies the user of the progress of the procedure. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

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

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice 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.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, procedure unit, and notification unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input basic information such as name, address, and contact information. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze the input information and extract data for selecting the appropriate procedure. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically selects the procedure required by the user, such as filing a tax return or canceling a subscription service, based on the analyzed information. The procedure unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically creates and submits the necessary documents based on the selected procedure. The notification unit is implemented by, for example, the speaker 240 of the smart glasses 214 and notifies the user of the progress of the procedure. 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.

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

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice 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.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, procedure unit, and notification 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 microphone 238 of the headset terminal 314 and provides an interface for the user to input basic information such as name, address, and contact information. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses a generating AI to analyze the input information and extract data for selecting the appropriate procedure. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically selects the procedure required by the user, such as filing a tax return or canceling a subscription service, based on the analyzed information. The procedure unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically creates and submits the necessary documents based on the selected procedure. The notification unit is implemented by, for example, the speaker 240 of the headset terminal 314 and notifies the user of the progress of the procedure. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice 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.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, procedure unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input basic information such as name, address, and contact information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses a generating AI to analyze the input information and extract data for selecting the appropriate procedure. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically selects the procedure required by the user, such as filing a tax return or canceling a subscription service, based on the analyzed information. The procedure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates and submits the necessary documents based on the selected procedure. The notification unit is implemented by, for example, the speaker 240 of the robot 414 and notifies the user of the progress of the procedure. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The reception area where you enter your personal information, An analysis unit analyzes the information input by the reception unit, A selection unit that selects an appropriate procedure based on the information analyzed by the analysis unit, The procedure selected by the aforementioned selection unit proceeds with the procedure unit, The system includes a notification unit that notifies the progress of the procedure carried out by the procedure unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter basic information such as the user's name, address, and contact information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Select the appropriate procedure based on the information you entered. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is The system automatically selects the procedures the user needs, such as filing tax returns or canceling subscription services. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned procedural department, The system automatically generates and submits the necessary documents based on the selected procedure. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Notify the user of the progress of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned procedural department, If you have any questions, please contact the person directly via social media. 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 dynamically changes the design of the input form based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and provides an auto-completion function to reduce the effort required for input. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting data, the input fields are customized based on the user's current situation and environment. 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 inputs 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 users input data, the system prioritizes inputting more relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During input, the system analyzes the user's social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past analysis data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is The system estimates the user's emotions and adjusts the procedure selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When making a selection, past selection data is referenced to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is When making a selection, the selection method is customized based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is It estimates the user's emotions and adjusts how the selection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is When making a selection, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is When making a selection, we refer to the user's social media activity to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned procedural department, It estimates the user's emotions and adjusts the procedure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned procedural department, During the procedure, past procedure data is referenced to improve the accuracy of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned procedural department, During the procedure, the procedure method is customized based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned procedural department, The system estimates the user's emotions and determines the priority of procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned procedural department, The procedure will be carried out taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned procedural department, During the process, we refer to the user's social media activity to improve the accuracy of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, past notification data is referenced to improve the accuracy of notifications. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, the system will take the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, When sending notifications, we refer to the user's social media activity to improve the accuracy of the notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. The reception area where you enter your personal information, An analysis unit analyzes the information input by the reception unit, A selection unit that selects an appropriate procedure based on the information analyzed by the analysis unit, The procedure selected by the aforementioned selection unit proceeds with the procedure unit, The system includes a notification unit that notifies the progress of the procedure carried out by the procedure unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter basic information such as the user's name, address, and contact information. The system according to feature 1.

3. The aforementioned analysis unit, Select the appropriate procedure based on the information you entered. The system according to feature 1.

4. The aforementioned selection unit is The system automatically selects the procedures the user needs, such as filing tax returns or canceling subscription services. The system according to feature 1.

5. The aforementioned procedural department, The system automatically generates and submits the necessary documents based on the selected procedure. The system according to feature 1.

6. The aforementioned notification unit, Notify the user of the progress of the procedure. The system according to feature 1.

7. The aforementioned procedural department, If you have any questions, please contact the person directly via social media. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input form based on the estimated emotions. The system according to feature 1.

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