System

Abstract

An object of the system according to the embodiment is to allow a user to grasp and execute a necessary procedure or application at an appropriate timing.SOLUTION: A system includes an input part, an analysis part, a recommendation part, and a provision part. The input unit inputs attribute information of a user. The analysis unit analyzes the information input by the input unit. The recommendation unit recommends a necessary procedure or application based on the information analyzed by the analysis unit. The providing unit provides a format of the procedure or the application recommended by the recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult for users to understand and carry out the necessary procedures and applications at the appropriate time.

[0005] The system according to the embodiment aims to enable users to grasp and execute necessary procedures and applications at the appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a recommendation unit, and a provision unit. The input unit inputs user attribute information. The analysis unit analyzes the information input by the input unit. The recommendation unit recommends a necessary procedure or application based on the information analyzed by the analysis unit. The provision unit provides a format for the procedure or application recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to grasp and execute necessary procedures and applications at the appropriate time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An application support system according to an embodiment of the present invention supports users in completing necessary procedures and applications at the appropriate time. In this system, users input personal attribute information in advance, and AI uses that information to recommend procedures and applications necessary to accommodate the user's upcoming events. Furthermore, the AI ​​learns different formats for different age groups and areas and provides the appropriate format at the appropriate time. For example, in this application support system, users input basic information such as their name, address, age, and occupation in advance. The AI ​​then analyzes the input information and recommends procedures and applications necessary to accommodate the user's upcoming events. Examples of recommended procedures include address change procedures for moving and child school enrollment procedures. Furthermore, the application support system provides different formats for different age groups and areas. Examples include sports club membership procedures in specific areas and formats for subsidy applications. This allows the application support system to easily understand where, to whom, and what kind of application (procedure) is required. This allows the user to prevent omissions or delays in procedures and efficiently complete procedures. For example, the application support system allows users to smoothly complete sports club membership procedures and subsidy application procedures in their new area. Additionally, for small and medium-sized enterprises, the system can recommend necessary government application documents.

[0029] The application support system according to the embodiment includes an input unit, an analysis unit, a recommendation unit, and a provision unit. The input unit inputs user attribute information. The user attribute information includes, for example, age, gender, occupation, and place of residence, but is not limited to these examples. The input unit allows the user to input basic information such as name, address, age, and occupation. The input unit can also input information such as the area to which the user is moving and family composition. The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. The analysis unit identifies necessary procedures and applications based on the user attribute information, for example. The analysis unit can also perform analysis based on the user's past behavioral history. The recommendation unit recommends necessary procedures and applications based on the information analyzed by the analysis unit. The recommendation is performed based on, for example, user attribute information and event information, but is not limited to these examples. The recommendation unit recommends, for example, address change procedures due to relocation and school enrollment procedures for children to the user. The recommendation unit can also make recommendations based on the user's past behavioral history. The provision unit provides the procedure or application format recommended by the recommendation unit. Examples of formats include, but are not limited to, PDF format, Word document, and online form. The provision unit can provide the user with, for example, a procedure for joining a sports club in a specific area or a format for applying for a subsidy. The provision unit can also provide different formats for different age groups and areas. This allows the application support system according to the embodiment to support users in completing the necessary procedures and applications at the appropriate time.

[0030] The analysis unit includes a collection unit that collects data to respond to a specific event. The collection unit collects data to respond to the specific event. Specific events include, but are not limited to, moving, marriage, and childbirth, for example. For example, if a user is planning to move, the collection unit collects data related to the move. Furthermore, if a user is planning to get married, the collection unit can also collect data related to the marriage. Furthermore, if a user is planning to give birth, the collection unit can also collect data related to the childbirth. By collecting data to respond to specific events, the accuracy of the analysis is improved. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data related to the user's move into AI, which can analyze the data and collect necessary information.

[0031] The providing unit includes a notification unit that notifies the user. The notification unit notifies the user. The notification may be performed by, for example, email, SMS, in-app notification, or other methods, but is not limited to these examples. The notification unit may notify the user by email, for example. The notification unit may also notify the user by SMS. Furthermore, the notification unit may also notify the user in-app. By notifying the user in this way, the timing for procedures or applications can be avoided. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the timing for the user's procedures or applications into AI, and the AI ​​may notify at the appropriate timing.

[0032] The recommendation unit can notify the user of necessary procedures at an appropriate time based on the user's attribute information and event information. The recommendation unit notifies the user of necessary procedures at an appropriate time based on the user's attribute information and event information. Examples of appropriate times include, but are not limited to, when an event occurs or within a specific period of time. For example, if the user is planning to move, the recommendation unit can notify the user of address change procedures associated with the move. Furthermore, if the user is planning to get married, the recommendation unit can also notify the user of procedures associated with the marriage. Furthermore, if the user is planning to give birth, the recommendation unit can also notify the user of procedures associated with the birth. This makes it possible to prevent procedures from being overlooked by notifying the user of procedures at an appropriate time based on the user's attribute information and event information. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's attribute information and event information into AI, which can then notify the user of procedures at an appropriate time.

[0033] The providing unit can provide different formats for each age group and area. The providing unit provides different formats for each age group and area. Age groups and areas include, but are not limited to, age groups such as teens, twenties, and thirties, and areas such as prefectures, cities, towns, and villages. For example, the providing unit provides a specific format for teenage users. The providing unit can also provide a different format for users in their twenties. Furthermore, the providing unit can also provide a specific format for users living in a specific prefecture. By providing different formats for each age group and area, users can smoothly complete the necessary procedures. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input formats for each age group and area into AI, which can then provide an appropriate format.

[0034] The collection unit can collect information on the area or family structure of the user's new residence. The collection unit collects information such as the area or family structure of the user's new residence. Examples of the area include, but are not limited to, prefectures, cities, wards, towns, and villages. Examples of the family structure include, but are not limited to, the number of family members, ages, and relationships. For example, if the user is planning to move, the collection unit collects information related to the area to which the user is moving. The collection unit can also collect information related to the user's family structure. By collecting information such as the area or family structure of the user's new residence, more appropriate procedures and applications can be recommended. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information on the area or family structure of the user's new residence into AI, which can collect appropriate information.

[0035] The input unit can analyze the user's past input history and select the optimal input method. The input unit analyzes the user's past input history and selects the optimal input method. Optimal input methods include, but are not limited to, voice input, text input, and image input. The input unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also automatically complete information previously input by the user to simplify the input work. Furthermore, the input unit can suggest the optimal input method for a specific time period based on the user's past input history. This allows the analysis of the user's past input history to suggest the optimal input method and improve the efficiency of the input work. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history into AI, which can select the optimal input method.

[0036] The input unit can customize input items based on the user's current living situation or areas of interest when inputting attribute information. The input unit customizes input items based on the user's current living situation or areas of interest when inputting attribute information. Examples of current living situations include, but are not limited to, occupation, housing situation, and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is planning to move, the input unit can prioritize displaying input items related to the area to which the user is moving. Furthermore, if the user's child is about to start school, the input unit can add school-related input items. Furthermore, if the user has a specific hobby, the input unit can customize and display input items related to the hobby. This allows more appropriate information to be collected by customizing the input items based on the user's living situation and areas of interest. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input information about the user's living situation and areas of interest into AI, which can then customize the input items.

[0037] The input unit can select the optimal input means according to the user's input method when inputting attribute information. The input unit can select the optimal input means according to the user's input method (voice, text, image, etc.) when inputting attribute information. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can provide a voice input interface. Furthermore, if the user prefers text input, the input unit can also provide a text input interface. Furthermore, if the user prefers image input, the input unit can support input using image recognition technology. This allows for smooth input work by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's input method to AI, which can then select the optimal input means.

[0038] When inputting attribute information, the input unit can prioritize inputting highly relevant information based on the user's geographical location information. When inputting attribute information, the input unit prioritizes inputting highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the user lives in a specific area, the input unit can prioritize inputting attribute information related to that area. Furthermore, if the user is traveling, the input unit can prioritize inputting attribute information related to the travel destination. Furthermore, if the user is planning to move, the input unit can prioritize inputting attribute information related to the area to which the user will move. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI, and the AI ​​can prioritize inputting highly relevant information.

[0039] The input unit can analyze the user's social media activity and input related information when the attribute information is input. The input unit can analyze the user's social media activity and input related information when the attribute information is input. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The input unit, for example, automatically acquires information that the user has made public on social media and inputs it as attribute information. The input unit can also analyze the user's social media activity and suggest related attribute information. Furthermore, the input unit can input related attribute information based on the user's friendships on social media. In this way, related information can be automatically input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using AI, or can be performed without using AI. For example, the input unit can input the user's social media activity to AI, which can then input related information.

[0040] The input unit can customize the input method based on the user's past feedback when inputting attribute information. The input unit customizes the input method by reflecting the user's past feedback when inputting attribute information. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The input unit can, for example, suggest an optimal input method based on feedback previously input by the user. The input unit can also customize the input interface based on the user's past feedback. Furthermore, the input unit can analyze the user's past feedback and optimize the input procedure. This allows the optimal input method to be suggested by reflecting the user's past feedback, thereby making input work more efficient. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback into AI, which can then customize the input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the priority of the attribute information during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the attribute information during analysis. Priority includes, but is not limited to, importance and urgency, for example. The analysis unit, for example, performs a detailed analysis of important attribute information and simplifies other information. The analysis unit can also determine the priority of the analysis based on the importance of the attribute information. Furthermore, the analysis unit can allocate more resources to attribute information with high importance during analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the attribute information, it is possible to perform a detailed analysis of important information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the attribute information into AI, which can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the type of attribute information during analysis. The analysis unit can apply different analysis algorithms depending on the type of attribute information during analysis. Types of attribute information include, but are not limited to, age, gender, and occupation. For example, the analysis unit can apply an analysis algorithm that emphasizes privacy protection to personal information. The analysis unit can also apply a geographic analysis algorithm to geographic information. Furthermore, the analysis unit can apply a social network analysis algorithm to social media information. In this way, by applying different analysis algorithms depending on the type of attribute information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the type of attribute information into AI, which can then apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data, analysis reports, etc. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm from the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI, which can improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission time of the attribute information during analysis. The analysis unit can determine the analysis priority based on the submission time of the attribute information during analysis. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. For example, the analysis unit prioritizes analysis of attribute information whose submission deadline is approaching. The analysis unit can also adjust the analysis schedule based on the submission time. Furthermore, the analysis unit can postpone analysis of attribute information whose submission time has passed. In this way, by determining the analysis priority based on the submission time of the attribute information, information whose submission deadline is approaching can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the attribute information into AI, and the AI ​​can determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of attribute information during analysis. The analysis unit adjusts the order of analysis based on the relevance of attribute information during analysis. Relevance includes, but is not limited to, common attributes, related topics, etc. The analysis unit, for example, prioritizes analysis of highly relevant attribute information. The analysis unit can also determine the order of analysis based on the relevance of attribute information. Furthermore, the analysis unit can postpone analysis of less relevant attribute information. In this way, by adjusting the order of analysis based on the relevance of attribute information, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of attribute information into AI, and the AI ​​can adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user has expert knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have expert knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented based on the user's level of expertise. By adjusting the use of technical terms in the analysis based on the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into AI, which can then adjust the use of technical terms in the analysis.

[0047] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the procedure or application when making a recommendation. The recommendation unit can adjust the level of detail of the recommendation based on the importance of the procedure or application when making a recommendation. The importance includes, but is not limited to, urgency and impact, for example. The recommendation unit can provide detailed recommendations for important procedures or applications, for example. The recommendation unit can also provide concise recommendations for less important procedures or applications. Furthermore, the recommendation unit can determine the priority of recommendations according to the importance of the procedure or application. By adjusting the level of detail of the recommendation based on the importance of the procedure or application, detailed recommendations can be provided for important procedures or applications. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the importance of the procedure or application into AI, which can then adjust the level of detail of the recommendation.

[0048] The recommendation unit can apply different recommendation algorithms depending on the category of the procedure or application when making a recommendation. The recommendation unit can apply different recommendation algorithms depending on the category of the procedure or application when making a recommendation. Categories include, for example, procedure type and application type, but are not limited to these examples. For example, the recommendation unit can apply a recommendation algorithm specialized for administrative procedures to administrative procedures. Furthermore, the recommendation unit can also apply a recommendation algorithm specialized for subsidy applications to subsidy applications. Furthermore, the recommendation unit can apply a recommendation algorithm specialized for sports club membership procedures to sports club membership procedures. In this way, by applying different recommendation algorithms depending on the category of the procedure or application, more appropriate recommendations can be provided. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the category of the procedure or application into AI, which can then apply an appropriate recommendation algorithm.

[0049] The recommendation unit can improve the accuracy of recommendations based on the user's past recommendation results when making recommendations. The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results when making recommendations. Past recommendation results include, for example, past recommendation history, user reactions, etc., but are not limited to these examples. The recommendation unit, for example, corrects current recommendations based on the user's past recommendation results. The recommendation unit can also optimize the recommendation algorithm from the user's past recommendation results. Furthermore, the recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results. As a result, the accuracy of recommendations can be improved by referring to the user's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or without using AI. For example, the recommendation unit can input the user's past recommendation results into the AI, allowing the AI ​​to improve the accuracy of its recommendations.

[0050] The recommendation unit can determine the priority of recommendations based on the submission time of the procedure or application when making a recommendation. The recommendation unit determines the priority of recommendations based on the submission time of the procedure or application when making a recommendation. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. The recommendation unit, for example, prioritizes recommending procedures and applications with upcoming submission deadlines. The recommendation unit can also adjust the recommendation schedule based on the submission time. Furthermore, the recommendation unit can postpone recommending procedures and applications whose submission time has passed. In this way, by determining the priority of recommendations based on the submission time of the procedure or application, it is possible to prioritize recommending procedures and applications with upcoming submission deadlines. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the submission time of the procedure or application into AI, and the AI ​​can determine the priority of recommendations.

[0051] The recommendation unit can adjust the order of recommendations based on the relevance of the procedures or applications when making recommendations. The recommendation unit adjusts the order of recommendations based on the relevance of the procedures or applications when making recommendations. Relevance includes, but is not limited to, common attributes, related topics, and the like. The recommendation unit, for example, prioritizes recommending highly relevant procedures or applications. The recommendation unit can also determine the order of recommendations based on the relevance of the procedures or applications. Furthermore, the recommendation unit can recommend less relevant procedures or applications at a later date. In this way, by adjusting the order of recommendations based on the relevance of the procedures or applications, highly relevant procedures or applications can be prioritized. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the relevance of the procedures or applications into AI, and the AI ​​can adjust the order of recommendations.

[0052] The recommendation unit can adjust the use of technical terms in the recommendation based on the user's level of expertise when making a recommendation. The recommendation unit can adjust the use of technical terms in the recommendation based on the user's level of expertise when making a recommendation. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the recommendation unit can provide a recommendation that uses a lot of technical terms. Also, if the user does not have expert knowledge, the recommendation unit can provide a recommendation in simple language. Furthermore, the recommendation unit can adjust the way the recommendation is expressed based on the user's level of expertise. This allows the recommendation to be easily understood by the user by adjusting the use of technical terms in the recommendation based on the user's level of expertise. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or can be performed without AI. For example, the recommendation unit can input the user's level of expertise into AI, and the AI ​​can adjust the use of technical terms in the recommendation.

[0053] The providing unit can adjust the level of detail of the format to be provided based on the importance of the procedure or application when providing the format. The providing unit can adjust the level of detail of the format to be provided based on the importance of the procedure or application when providing the format. The importance includes, but is not limited to, urgency and impact, for example. The providing unit can provide a detailed format for important procedures or applications, for example. The providing unit can also provide a concise format for less important procedures or applications. Furthermore, the providing unit can determine the priority of the format according to the importance of the procedure or application. In this way, by adjusting the level of detail of the format to be provided based on the importance of the procedure or application, detailed formats can be provided for important procedures or applications. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the importance of the procedure or application into AI and adjust the level of detail of the format provided by AI.

[0054] The providing unit can apply different formats depending on the category of the procedure or application when providing the data. The providing unit applies different formats depending on the category of the procedure or application when providing the data. Categories include, but are not limited to, for example, the type of procedure and the type of application. For example, the providing unit provides a format specialized for administrative procedures for administrative procedures. Furthermore, the providing unit can provide a format specialized for subsidy applications for subsidy applications. Furthermore, the providing unit can provide a format specialized for sports club membership procedures for sports club membership procedures. In this way, by applying different formats depending on the category of the procedure or application, a more appropriate format can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of the procedure or application into AI, which can then apply an appropriate format.

[0055] The providing unit can improve the accuracy of the format provided based on the user's past provision results when providing the format. The providing unit can improve the accuracy of the format provided by referring to the user's past provision results when providing the format. Past provision results include, but are not limited to, past provision history, user responses, etc. For example, the providing unit corrects the current format based on the user's past provision results. The providing unit can also optimize the format based on the user's past provision results. Furthermore, the providing unit can improve the accuracy of the format by referring to the user's past provision results. In this way, the accuracy of the format provided can be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into AI to improve the accuracy of the format provided by the AI.

[0056] The providing unit can determine the priority of formats to be provided based on the submission time of the procedure or application at the time of provision. The providing unit determines the priority of formats to be provided based on the submission time of the procedure or application at the time of provision. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. For example, the providing unit can prioritize providing procedures or applications whose submission deadlines are approaching. The providing unit can also adjust the format provision schedule based on the submission time. Furthermore, the providing unit can postpone providing procedures or applications whose submission time has passed. In this way, by determining the priority of formats to be provided based on the submission time of the procedure or application, it is possible to prioritize procedures or applications whose submission deadlines are approaching. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time of the procedure or application into AI and determine the priority of formats to be provided by the AI.

[0057] The providing unit can adjust the order of formats to be provided based on the relevance of the procedures or applications when providing them. The providing unit adjusts the order of formats to be provided based on the relevance of the procedures or applications when providing them. Relevance includes, but is not limited to, common attributes, related topics, etc. For example, the providing unit can prioritize providing highly relevant procedures or applications. The providing unit can also determine the order of providing formats based on the relevance of the procedures or applications. Furthermore, the providing unit can postpone providing less relevant procedures or applications. In this way, by adjusting the order of formats to be provided based on the relevance of the procedures or applications, highly relevant procedures or applications can be prioritized. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of procedures or applications into AI and adjust the order of formats provided by the AI.

[0058] The providing unit can adjust the use of technical terms in the format to be provided based on the user's level of expertise when providing the format. The providing unit can adjust the use of technical terms in the format to be provided based on the user's level of expertise when providing the format. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the providing unit can provide a format that uses a lot of technical terms. Also, if the user does not have expert knowledge, the providing unit can provide a format in simple language. Furthermore, the providing unit can adjust the way the format is expressed based on the user's level of expertise. This allows for the provision of a format that is easy for the user to understand by adjusting the use of technical terms in the format to be provided based on the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's level of expertise into AI and adjust the use of technical terms in the format provided by the AI.

[0059] The collection unit can analyze the user's past data collection history at the time of collection and select the optimal collection method. The collection unit can analyze the user's past data collection history at the time of collection and select the optimal collection method. The past data collection history includes, for example, past collected data and collection methods, but is not limited to these examples. The collection unit can, for example, suggest the optimal collection method based on collection methods used by the user in the past. The collection unit can also select an efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and optimize the collection method. In this way, by analyzing the user's past data collection history, the optimal collection method can be suggested and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal collection method.

[0060] The collection unit can customize the collection items based on the user's current living situation or areas of interest at the time of collection. The collection unit customizes the collection items based on the user's current living situation or areas of interest at the time of collection. Current living situations include, but are not limited to, occupation, housing situation, and family structure. Areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is planning to move, the collection unit may prioritize collecting data related to the area to which the user is moving. Also, if the user's child is about to start school, the collection unit may collect school-related data. Furthermore, if the user has a specific hobby, the collection unit may customize and collect data related to the hobby. This allows for more appropriate data to be collected by customizing the collection items based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input information about the user's living situation and areas of interest into AI, which may then customize the collection items.

[0061] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information during collection. The collection unit prioritizes collecting highly relevant data taking into account the user's geographical location information during collection. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the user lives in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the user's travel destination. Furthermore, if the user is planning to move, the collection unit can prioritize collecting data related to the area to which the user will move. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0062] The collection unit can analyze the user's social media activity and collect related data at the time of collection. The collection unit can analyze the user's social media activity and collect related data at the time of collection. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit, for example, automatically acquires information that the user has made public on social media and collects it as data. The collection unit can also analyze the user's social media activity and collect related data. Furthermore, the collection unit can collect related data by referring to the user's friendships on social media. In this way, related data can be automatically collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the user's social media activity into AI, which can collect related data.

[0063] The notification unit can adjust the level of detail of the notification based on the importance of the procedure or application when sending a notification. The notification unit can adjust the level of detail of the notification based on the importance of the procedure or application when sending a notification. The importance includes, but is not limited to, urgency and impact, for example. The notification unit can provide detailed notifications for important procedures and applications, for example. The notification unit can also provide concise notifications for less important procedures and applications. Furthermore, the notification unit can determine the priority of notifications according to the importance of the procedure or application. By adjusting the level of detail of notifications based on the importance of the procedure or application, detailed notifications can be provided for important procedures and applications. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the importance of the procedure or application into AI, which can then adjust the level of detail of the notification.

[0064] The notification unit can apply different notification algorithms depending on the category of the procedure or application when notifying. The notification unit applies different notification algorithms depending on the category of the procedure or application when notifying. Categories include, but are not limited to, for example, the type of procedure and the type of application. For example, the notification unit can apply a notification algorithm specialized for administrative procedures to administrative procedures. Furthermore, the notification unit can also apply a notification algorithm specialized for subsidy applications to subsidy applications. Furthermore, the notification unit can also apply a notification algorithm specialized for sports club membership procedures to sports club membership procedures. In this way, by applying different notification algorithms depending on the category of the procedure or application, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the category of the procedure or application into AI, which can then apply an appropriate notification algorithm.

[0065] The notification unit can improve the accuracy of notifications based on the user's past notification results when notifying the user. The notification unit can improve the accuracy of notifications by referring to the user's past notification results when notifying the user. Past notification results include, but are not limited to, past notification history, user reactions, etc. The notification unit, for example, corrects the current notification based on the user's past notification results. The notification unit can also optimize the notification algorithm based on the user's past notification results. Furthermore, the notification unit can improve the accuracy of notifications by referring to the user's past notification results. In this way, the accuracy of notifications can be improved by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification results into AI, which can improve the accuracy of notifications.

[0066] The notification unit can determine the priority of notifications based on the submission time of the procedure or application at the time of notification. The notification unit determines the priority of notifications based on the submission time of the procedure or application at the time of notification. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. The notification unit, for example, prioritizes notifications for procedures or applications with upcoming submission deadlines. The notification unit can also adjust the notification schedule based on the submission time. Furthermore, the notification unit can postpone notifications for procedures or applications whose submission deadlines have passed. In this way, by determining the priority of notifications based on the submission time of the procedure or application, it is possible to prioritize notifications for procedures or applications with upcoming submission deadlines. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the submission time of the procedure or application into AI, and the AI ​​can determine the priority of notifications.

[0067] The notification unit can adjust the order of notifications based on the relevance of the procedures or applications when notifying. The notification unit adjusts the order of notifications based on the relevance of the procedures or applications when notifying. Relevance includes, but is not limited to, common attributes, related topics, and the like. For example, the notification unit prioritizes notifications of highly relevant procedures or applications. The notification unit can also determine the order of notifications based on the relevance of the procedures or applications. Furthermore, the notification unit can postpone notifications of less relevant procedures or applications. In this way, by adjusting the order of notifications based on the relevance of the procedures or applications, highly relevant procedures or applications can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance of the procedures or applications into AI, and the AI ​​can adjust the order of notifications.

[0068] The notification unit may adjust the use of technical terms in the notification based on the user's level of expertise. The notification unit may adjust the use of technical terms in the notification based on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has specialized knowledge, the notification unit may provide a notification that uses a lot of technical terms. Alternatively, if the user does not have specialized knowledge, the notification unit may provide a notification in simple language. Furthermore, the notification unit may adjust the way the notification is presented based on the user's level of expertise. By adjusting the use of technical terms in the notification based on the user's level of expertise, it is possible to provide a notification that is easy for the user to understand. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit may input the user's level of expertise into AI, which may then adjust the use of technical terms in the notification.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The analysis unit can predict future behavior based on the user's past behavior history and determine analysis priorities based on the predicted behavior. For example, procedures that the user frequently performed in the past can be analyzed first, and other procedures can be postponed. The analysis priorities can also be determined based on the frequency of procedures that the user performed in the past. Furthermore, the analysis priorities can also be determined based on the importance of procedures that the user performed in the past. In this way, by determining the analysis priorities based on the user's past behavior history, important procedures can be analyzed quickly. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavior history into AI, which can predict future behavior and determine analysis priorities.

[0071] The providing unit can select the optimal format based on the user's past format usage history. For example, formats that the user has frequently used in the past can be provided preferentially, with other formats being provided later. The optimal format can also be selected based on the user's evaluation of formats used in the past. Furthermore, the optimal format can also be selected based on the frequency of use of formats used in the past by the user. In this way, by selecting the optimal format based on the user's past format usage history, it is possible to provide a format that is easy for the user to use. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past format usage history into AI, which can select the optimal format.

[0072] The notification unit can select the optimal notification method based on the user's past notification history. For example, it can prioritize notification methods (email, SMS, etc.) that the user has frequently used in the past, and postpone other notification methods. The optimal notification method can also be selected based on the user's evaluation of notifications received in the past. Furthermore, the optimal notification method can also be selected based on the user's reaction to notifications received in the past. In this way, by selecting the optimal notification method based on the user's past notification history, it is possible to provide notifications that are easy for the user to receive. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history into AI, which can select the optimal notification method.

[0073] The recommendation unit can select optimal recommendation content based on the user's past recommendation history. For example, the recommendation unit provides optimal recommendation content based on the user's evaluation of recommendations received in the past. The recommendation unit can also select optimal recommendation content based on the user's reaction to recommendations received in the past. Furthermore, the recommendation unit can also select optimal recommendation content based on the frequency of use of recommendations received in the past. In this way, by selecting optimal recommendation content based on the user's past recommendation history, it is possible to provide recommendations that are beneficial to the user. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation history into AI, which can select optimal recommendation content.

[0074] The collection unit can select the optimal data collection method based on the user's past data collection history. For example, the collection unit can provide data collection methods (such as questionnaires and interviews) that the user has used in the past with priority, and postpone other data collection methods. The collection unit can also select the optimal data collection method based on the user's evaluation of data collected in the past. Furthermore, the collection unit can also select the optimal data collection method based on the user's frequency of use of data collected in the past. In this way, efficient data collection can be provided to the user by selecting the optimal data collection method based on the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal data collection method.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The input unit inputs the user's attribute information. The user's attribute information includes, for example, age, gender, occupation, and place of residence. The input unit allows the user to input basic information such as name, address, age, and occupation. The user can also input information such as the area to which they are moving and their family composition. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. The analysis unit identifies the necessary procedures and applications based on the user's attribute information. It can also perform analysis based on the user's past behavioral history. Step 3: The recommendation unit recommends the necessary procedures and applications based on the information analyzed by the analysis unit. Recommendations are made based on the user's attribute information and event information. For example, it may recommend procedures for changing the user's address when moving, or procedures for enrolling a child in school. Recommendations can also be made based on the user's past behavioral history. Step 4: The provision unit provides the procedures and application formats recommended by the recommendation unit. Formats include, for example, PDF format, Word document, and online form. The provision unit provides the user with a format for joining a sports club in a specific area or for applying for a subsidy. It can also provide different formats for different age groups and areas.

[0077] (Example 2) An application support system according to an embodiment of the present invention supports users in completing necessary procedures and applications at the appropriate time. In this system, users input personal attribute information in advance, and AI uses that information to recommend procedures and applications necessary to accommodate the user's upcoming events. Furthermore, the AI ​​learns different formats for different age groups and areas and provides the appropriate format at the appropriate time. For example, in this application support system, users input basic information such as their name, address, age, and occupation in advance. The AI ​​then analyzes the input information and recommends procedures and applications necessary to accommodate the user's upcoming events. Examples of recommended procedures include address change procedures for moving and child school enrollment procedures. Furthermore, the application support system provides different formats for different age groups and areas. Examples include sports club membership procedures in specific areas and formats for subsidy applications. This allows the application support system to easily understand where, to whom, and what kind of application (procedure) is required. This allows the user to prevent omissions or delays in procedures and efficiently complete procedures. For example, the application support system allows users to smoothly complete sports club membership procedures and subsidy application procedures in their new area. Additionally, for small and medium-sized enterprises, the system can recommend necessary government application documents.

[0078] The application support system according to the embodiment includes an input unit, an analysis unit, a recommendation unit, and a provision unit. The input unit inputs user attribute information. The user attribute information includes, for example, age, gender, occupation, and place of residence, but is not limited to these examples. The input unit allows the user to input basic information such as name, address, age, and occupation. The input unit can also input information such as the area to which the user is moving and family composition. The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. The analysis unit identifies necessary procedures and applications based on the user attribute information, for example. The analysis unit can also perform analysis based on the user's past behavioral history. The recommendation unit recommends necessary procedures and applications based on the information analyzed by the analysis unit. The recommendation is performed based on, for example, user attribute information and event information, but is not limited to these examples. The recommendation unit recommends, for example, address change procedures due to relocation and school enrollment procedures for children to the user. The recommendation unit can also make recommendations based on the user's past behavioral history. The provision unit provides the procedure or application format recommended by the recommendation unit. Examples of formats include, but are not limited to, PDF format, Word document, and online form. The provision unit can provide the user with, for example, a procedure for joining a sports club in a specific area or a format for applying for a subsidy. The provision unit can also provide different formats for different age groups and areas. This allows the application support system according to the embodiment to support users in completing the necessary procedures and applications at the appropriate time.

[0079] The analysis unit includes a collection unit that collects data to respond to a specific event. The collection unit collects data to respond to the specific event. Specific events include, but are not limited to, moving, marriage, and childbirth, for example. For example, if a user is planning to move, the collection unit collects data related to the move. Furthermore, if a user is planning to get married, the collection unit can also collect data related to the marriage. Furthermore, if a user is planning to give birth, the collection unit can also collect data related to the childbirth. By collecting data to respond to specific events, the accuracy of the analysis is improved. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data related to the user's move into AI, which can analyze the data and collect necessary information.

[0080] The providing unit includes a notification unit that notifies the user. The notification unit notifies the user. The notification may be performed by, for example, email, SMS, in-app notification, or other methods, but is not limited to these examples. The notification unit may notify the user by email, for example. The notification unit may also notify the user by SMS. Furthermore, the notification unit may also notify the user in-app. By notifying the user in this way, the timing for procedures or applications can be avoided. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the timing for the user's procedures or applications into AI, and the AI ​​may notify at the appropriate timing.

[0081] The recommendation unit can notify the user of necessary procedures at an appropriate time based on the user's attribute information and event information. The recommendation unit notifies the user of necessary procedures at an appropriate time based on the user's attribute information and event information. Examples of appropriate times include, but are not limited to, when an event occurs or within a specific period of time. For example, if the user is planning to move, the recommendation unit can notify the user of address change procedures associated with the move. Furthermore, if the user is planning to get married, the recommendation unit can also notify the user of procedures associated with the marriage. Furthermore, if the user is planning to give birth, the recommendation unit can also notify the user of procedures associated with the birth. This makes it possible to prevent procedures from being overlooked by notifying the user of procedures at an appropriate time based on the user's attribute information and event information. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's attribute information and event information into AI, which can then notify the user of procedures at an appropriate time.

[0082] The providing unit can provide different formats for each age group and area. The providing unit provides different formats for each age group and area. Age groups and areas include, but are not limited to, age groups such as teens, twenties, and thirties, and areas such as prefectures, cities, towns, and villages. For example, the providing unit provides a specific format for teenage users. The providing unit can also provide a different format for users in their twenties. Furthermore, the providing unit can also provide a specific format for users living in a specific prefecture. By providing different formats for each age group and area, users can smoothly complete the necessary procedures. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input formats for each age group and area into AI, which can then provide an appropriate format.

[0083] The collection unit can collect information on the area or family structure of the user's new residence. The collection unit collects information such as the area or family structure of the user's new residence. Examples of the area include, but are not limited to, prefectures, cities, wards, towns, and villages. Examples of the family structure include, but are not limited to, the number of family members, ages, and relationships. For example, if the user is planning to move, the collection unit collects information related to the area to which the user is moving. The collection unit can also collect information related to the user's family structure. By collecting information such as the area or family structure of the user's new residence, more appropriate procedures and applications can be recommended. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information on the area or family structure of the user's new residence into AI, which can collect appropriate information.

[0084] The input unit can estimate the user's emotion and adjust the timing of inputting attribute information based on the estimated user emotion. The input unit can estimate the user's emotion and adjust the timing of inputting attribute information based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is feeling stressed, the input unit can postpone input and prompt the user to input again when they are relaxed. Furthermore, if the user is relaxed, the input unit can prompt the user to input attribute information immediately, allowing the user to proceed smoothly. Furthermore, if the user is in a hurry, the input unit can provide a simplified input form to allow the user to complete the input quickly. This reduces the burden on the user by adjusting the timing of inputting attribute information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the input timing.

[0085] The input unit can analyze the user's past input history and select the optimal input method. The input unit analyzes the user's past input history and selects the optimal input method. Optimal input methods include, but are not limited to, voice input, text input, and image input. The input unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also automatically complete information previously input by the user to simplify the input work. Furthermore, the input unit can suggest the optimal input method for a specific time period based on the user's past input history. This allows the analysis of the user's past input history to suggest the optimal input method and improve the efficiency of the input work. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history into AI, which can select the optimal input method.

[0086] The input unit can customize input items based on the user's current living situation or areas of interest when inputting attribute information. The input unit customizes input items based on the user's current living situation or areas of interest when inputting attribute information. Examples of current living situations include, but are not limited to, occupation, housing situation, and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is planning to move, the input unit can prioritize displaying input items related to the area to which the user is moving. Furthermore, if the user's child is about to start school, the input unit can add school-related input items. Furthermore, if the user has a specific hobby, the input unit can customize and display input items related to the hobby. This allows more appropriate information to be collected by customizing the input items based on the user's living situation and areas of interest. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input information about the user's living situation and areas of interest into AI, which can then customize the input items.

[0087] The input unit can select the optimal input means according to the user's input method when inputting attribute information. The input unit can select the optimal input means according to the user's input method (voice, text, image, etc.) when inputting attribute information. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can provide a voice input interface. Furthermore, if the user prefers text input, the input unit can also provide a text input interface. Furthermore, if the user prefers image input, the input unit can support input using image recognition technology. This allows for smooth input work by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's input method to AI, which can then select the optimal input means.

[0088] The input unit can estimate the user's emotion and determine the priority of attribute information to be input based on the estimated user emotion. The input unit can estimate the user's emotion and determine the priority of attribute information to be input based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is stressed, the input unit can prioritize input of important attribute information and postpone input of other information. The input unit can also input all attribute information at once when the user is relaxed. Furthermore, when the user is in a hurry, the input unit can input only the most important attribute information and delay input of other information. In this way, by determining the priority of attribute information according to the user's emotion, important information can be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's emotional data into the generation AI, which can then estimate the emotion and determine the priority of the attribute information to be input.

[0089] When inputting attribute information, the input unit can prioritize inputting highly relevant information based on the user's geographical location information. When inputting attribute information, the input unit prioritizes inputting highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the user lives in a specific area, the input unit can prioritize inputting attribute information related to that area. Furthermore, if the user is traveling, the input unit can prioritize inputting attribute information related to the travel destination. Furthermore, if the user is planning to move, the input unit can prioritize inputting attribute information related to the area to which the user will move. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI, and the AI ​​can prioritize inputting highly relevant information.

[0090] The input unit can analyze the user's social media activity and input related information when the attribute information is input. The input unit can analyze the user's social media activity and input related information when the attribute information is input. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The input unit, for example, automatically acquires information that the user has made public on social media and inputs it as attribute information. The input unit can also analyze the user's social media activity and suggest related attribute information. Furthermore, the input unit can input related attribute information based on the user's friendships on social media. In this way, related information can be automatically input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using AI, or can be performed without using AI. For example, the input unit can input the user's social media activity to AI, which can then input related information.

[0091] The input unit can customize the input method based on the user's past feedback when inputting attribute information. The input unit customizes the input method by reflecting the user's past feedback when inputting attribute information. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The input unit can, for example, suggest an optimal input method based on feedback previously input by the user. The input unit can also customize the input interface based on the user's past feedback. Furthermore, the input unit can analyze the user's past feedback and optimize the input procedure. This allows the optimal input method to be suggested by reflecting the user's past feedback, thereby making input work more efficient. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback into AI, which can then customize the input method.

[0092] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results that are concise when the user is in a hurry. This allows the analysis results to be easily understood by adjusting the way the analysis is presented based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the way the analysis is expressed.

[0093] The analysis unit can adjust the level of detail of the analysis based on the priority of the attribute information during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the attribute information during analysis. Priority includes, but is not limited to, importance and urgency, for example. The analysis unit, for example, performs a detailed analysis of important attribute information and simplifies other information. The analysis unit can also determine the priority of the analysis based on the importance of the attribute information. Furthermore, the analysis unit can allocate more resources to attribute information with high importance during analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the attribute information, it is possible to perform a detailed analysis of important information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the attribute information into AI, which can adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the type of attribute information during analysis. The analysis unit can apply different analysis algorithms depending on the type of attribute information during analysis. Types of attribute information include, but are not limited to, age, gender, and occupation. For example, the analysis unit can apply an analysis algorithm that emphasizes privacy protection to personal information. The analysis unit can also apply a geographic analysis algorithm to geographic information. Furthermore, the analysis unit can apply a social network analysis algorithm to social media information. In this way, by applying different analysis algorithms depending on the type of attribute information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the type of attribute information into AI, which can then apply an appropriate analysis algorithm.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data, analysis reports, etc. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm from the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI, which can improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results that are concise when the user is in a hurry. This allows the analysis length to be adjusted according to the user's emotion, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can estimate the emotion and adjust the length of the analysis.

[0097] The analysis unit can determine the analysis priority based on the submission time of the attribute information during analysis. The analysis unit can determine the analysis priority based on the submission time of the attribute information during analysis. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. For example, the analysis unit prioritizes analysis of attribute information whose submission deadline is approaching. The analysis unit can also adjust the analysis schedule based on the submission time. Furthermore, the analysis unit can postpone analysis of attribute information whose submission time has passed. In this way, by determining the analysis priority based on the submission time of the attribute information, information whose submission deadline is approaching can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the attribute information into AI, and the AI ​​can determine the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of attribute information during analysis. The analysis unit adjusts the order of analysis based on the relevance of attribute information during analysis. Relevance includes, but is not limited to, common attributes, related topics, etc. The analysis unit, for example, prioritizes analysis of highly relevant attribute information. The analysis unit can also determine the order of analysis based on the relevance of attribute information. Furthermore, the analysis unit can postpone analysis of less relevant attribute information. In this way, by adjusting the order of analysis based on the relevance of attribute information, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of attribute information into AI, and the AI ​​can adjust the order of analysis.

[0099] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user has expert knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have expert knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented based on the user's level of expertise. By adjusting the use of technical terms in the analysis based on the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into AI, which can then adjust the use of technical terms in the analysis.

[0100] The recommendation unit can analyze the user's emotions and adjust the way the recommendation is presented based on the analyzed user's emotions. The recommendation unit can estimate the user's emotions and adjust the way the recommendation is presented based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also provide concise recommendations when the user is stressed. Furthermore, the recommendation unit can provide recommendations that are concise to the point when the user is in a hurry. In this way, by adjusting the way the recommendation is presented based on the user's emotions, it is possible to provide recommendations that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input user emotion data to the generation AI, which may infer the emotion and adjust the way the recommendation is presented.

[0101] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the procedure or application when making a recommendation. The recommendation unit can adjust the level of detail of the recommendation based on the importance of the procedure or application when making a recommendation. The importance includes, but is not limited to, urgency and impact, for example. The recommendation unit can provide detailed recommendations for important procedures or applications, for example. The recommendation unit can also provide concise recommendations for less important procedures or applications. Furthermore, the recommendation unit can determine the priority of recommendations according to the importance of the procedure or application. By adjusting the level of detail of the recommendation based on the importance of the procedure or application, detailed recommendations can be provided for important procedures or applications. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the importance of the procedure or application into AI, which can then adjust the level of detail of the recommendation.

[0102] The recommendation unit can apply different recommendation algorithms depending on the category of the procedure or application when making a recommendation. The recommendation unit can apply different recommendation algorithms depending on the category of the procedure or application when making a recommendation. Categories include, for example, procedure type and application type, but are not limited to these examples. For example, the recommendation unit can apply a recommendation algorithm specialized for administrative procedures to administrative procedures. Furthermore, the recommendation unit can also apply a recommendation algorithm specialized for subsidy applications to subsidy applications. Furthermore, the recommendation unit can apply a recommendation algorithm specialized for sports club membership procedures to sports club membership procedures. In this way, by applying different recommendation algorithms depending on the category of the procedure or application, more appropriate recommendations can be provided. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the category of the procedure or application into AI, which can then apply an appropriate recommendation algorithm.

[0103] The recommendation unit can improve the accuracy of recommendations based on the user's past recommendation results when making recommendations. The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results when making recommendations. Past recommendation results include, for example, past recommendation history, user reactions, etc., but are not limited to these examples. The recommendation unit, for example, corrects current recommendations based on the user's past recommendation results. The recommendation unit can also optimize the recommendation algorithm from the user's past recommendation results. Furthermore, the recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results. As a result, the accuracy of recommendations can be improved by referring to the user's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or without using AI. For example, the recommendation unit can input the user's past recommendation results into the AI, allowing the AI ​​to improve the accuracy of its recommendations.

[0104] The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user emotion. The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also provide concise recommendations when the user is stressed. Furthermore, the recommendation unit can provide recommendations that are concise to the point when the user is in a hurry. By adjusting the length of the recommendation according to the user's emotion, it is possible to provide recommendations that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI or without AI. For example, the recommendation unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the length of the recommendation.

[0105] The recommendation unit can determine the priority of recommendations based on the submission time of the procedure or application when making a recommendation. The recommendation unit determines the priority of recommendations based on the submission time of the procedure or application when making a recommendation. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. The recommendation unit, for example, prioritizes recommending procedures and applications with upcoming submission deadlines. The recommendation unit can also adjust the recommendation schedule based on the submission time. Furthermore, the recommendation unit can postpone recommending procedures and applications whose submission time has passed. In this way, by determining the priority of recommendations based on the submission time of the procedure or application, it is possible to prioritize recommending procedures and applications with upcoming submission deadlines. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the submission time of the procedure or application into AI, and the AI ​​can determine the priority of recommendations.

[0106] The recommendation unit can adjust the order of recommendations based on the relevance of the procedures or applications when making recommendations. The recommendation unit adjusts the order of recommendations based on the relevance of the procedures or applications when making recommendations. Relevance includes, but is not limited to, common attributes, related topics, and the like. The recommendation unit, for example, prioritizes recommending highly relevant procedures or applications. The recommendation unit can also determine the order of recommendations based on the relevance of the procedures or applications. Furthermore, the recommendation unit can recommend less relevant procedures or applications at a later date. In this way, by adjusting the order of recommendations based on the relevance of the procedures or applications, highly relevant procedures or applications can be prioritized. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the relevance of the procedures or applications into AI, and the AI ​​can adjust the order of recommendations.

[0107] The recommendation unit can adjust the use of technical terms in the recommendation based on the user's level of expertise when making a recommendation. The recommendation unit can adjust the use of technical terms in the recommendation based on the user's level of expertise when making a recommendation. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the recommendation unit can provide a recommendation that uses a lot of technical terms. Also, if the user does not have expert knowledge, the recommendation unit can provide a recommendation in simple language. Furthermore, the recommendation unit can adjust the way the recommendation is expressed based on the user's level of expertise. This allows the recommendation to be easily understood by the user by adjusting the use of technical terms in the recommendation based on the user's level of expertise. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or can be performed without AI. For example, the recommendation unit can input the user's level of expertise into AI, and the AI ​​can adjust the use of technical terms in the recommendation.

[0108] The providing unit can analyze the user's emotions and adjust the expression method of the format to be provided based on the analyzed user's emotions. The providing unit can estimate the user's emotions and adjust the expression method of the format to be provided based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the providing unit can provide a detailed format when the user is relaxed. The providing unit can also provide a concise format when the user is stressed. Furthermore, the providing unit can also provide a format that focuses on the main points when the user is in a hurry. In this way, by adjusting the expression method of the format to be provided according to the user's emotions, it is possible to provide a format that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's emotional data into the generating AI, which can then estimate the emotion and adjust the expression method of the format provided.

[0109] The providing unit can adjust the level of detail of the format to be provided based on the importance of the procedure or application when providing the format. The providing unit can adjust the level of detail of the format to be provided based on the importance of the procedure or application when providing the format. The importance includes, but is not limited to, urgency and impact, for example. The providing unit can provide a detailed format for important procedures or applications, for example. The providing unit can also provide a concise format for less important procedures or applications. Furthermore, the providing unit can determine the priority of the format according to the importance of the procedure or application. In this way, by adjusting the level of detail of the format to be provided based on the importance of the procedure or application, detailed formats can be provided for important procedures or applications. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the importance of the procedure or application into AI and adjust the level of detail of the format provided by AI.

[0110] The providing unit can apply different formats depending on the category of the procedure or application when providing the data. The providing unit applies different formats depending on the category of the procedure or application when providing the data. Categories include, but are not limited to, for example, the type of procedure and the type of application. For example, the providing unit provides a format specialized for administrative procedures for administrative procedures. Furthermore, the providing unit can provide a format specialized for subsidy applications for subsidy applications. Furthermore, the providing unit can provide a format specialized for sports club membership procedures for sports club membership procedures. In this way, by applying different formats depending on the category of the procedure or application, a more appropriate format can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of the procedure or application into AI, which can then apply an appropriate format.

[0111] The providing unit can improve the accuracy of the format provided based on the user's past provision results when providing the format. The providing unit can improve the accuracy of the format provided by referring to the user's past provision results when providing the format. Past provision results include, but are not limited to, past provision history, user responses, etc. For example, the providing unit corrects the current format based on the user's past provision results. The providing unit can also optimize the format based on the user's past provision results. Furthermore, the providing unit can improve the accuracy of the format by referring to the user's past provision results. In this way, the accuracy of the format provided can be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into AI to improve the accuracy of the format provided by the AI.

[0112] The providing unit can estimate the user's emotion and adjust the length of the format to be provided based on the estimated user emotion. The providing unit can estimate the user's emotion and adjust the length of the format to be provided based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the providing unit can provide a detailed format when the user is relaxed. The providing unit can also provide a concise format when the user is stressed. Furthermore, the providing unit can also provide a format that focuses on the main points when the user is in a hurry. This allows the length of the format to be adjusted according to the user's emotion, thereby providing a format that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the length of the format to be provided.

[0113] The providing unit can determine the priority of formats to be provided based on the submission time of the procedure or application at the time of provision. The providing unit determines the priority of formats to be provided based on the submission time of the procedure or application at the time of provision. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. For example, the providing unit can prioritize providing procedures or applications whose submission deadlines are approaching. The providing unit can also adjust the format provision schedule based on the submission time. Furthermore, the providing unit can postpone providing procedures or applications whose submission time has passed. In this way, by determining the priority of formats to be provided based on the submission time of the procedure or application, it is possible to prioritize procedures or applications whose submission deadlines are approaching. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time of the procedure or application into AI and determine the priority of formats to be provided by the AI.

[0114] The providing unit can adjust the order of formats to be provided based on the relevance of the procedures or applications when providing them. The providing unit adjusts the order of formats to be provided based on the relevance of the procedures or applications when providing them. Relevance includes, but is not limited to, common attributes, related topics, etc. For example, the providing unit can prioritize providing highly relevant procedures or applications. The providing unit can also determine the order of providing formats based on the relevance of the procedures or applications. Furthermore, the providing unit can postpone providing less relevant procedures or applications. In this way, by adjusting the order of formats to be provided based on the relevance of the procedures or applications, highly relevant procedures or applications can be prioritized. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of procedures or applications into AI and adjust the order of formats provided by the AI.

[0115] The providing unit can adjust the use of technical terms in the format to be provided based on the user's level of expertise when providing the format. The providing unit can adjust the use of technical terms in the format to be provided based on the user's level of expertise when providing the format. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the providing unit can provide a format that uses a lot of technical terms. Also, if the user does not have expert knowledge, the providing unit can provide a format in simple language. Furthermore, the providing unit can adjust the way the format is expressed based on the user's level of expertise. This allows for the provision of a format that is easy for the user to understand by adjusting the use of technical terms in the format to be provided based on the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's level of expertise into AI and adjust the use of technical terms in the format provided by the AI.

[0116] The collection unit can analyze the user's emotions and determine the priority of data to be collected based on the analyzed user emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is stressed, the collection unit can prioritize collecting concise data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that is concise. In this way, by determining the priority of data to be collected according to the user's emotions, it is possible to prioritize collecting data that is important to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then infer the emotions and determine the priority of the data to be collected.

[0117] The collection unit can analyze the user's past data collection history at the time of collection and select the optimal collection method. The collection unit can analyze the user's past data collection history at the time of collection and select the optimal collection method. The past data collection history includes, for example, past collected data and collection methods, but is not limited to these examples. The collection unit can, for example, suggest the optimal collection method based on collection methods used by the user in the past. The collection unit can also select an efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and optimize the collection method. In this way, by analyzing the user's past data collection history, the optimal collection method can be suggested and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal collection method.

[0118] The collection unit can customize the collection items based on the user's current living situation or areas of interest at the time of collection. The collection unit customizes the collection items based on the user's current living situation or areas of interest at the time of collection. Current living situations include, but are not limited to, occupation, housing situation, and family structure. Areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is planning to move, the collection unit may prioritize collecting data related to the area to which the user is moving. Also, if the user's child is about to start school, the collection unit may collect school-related data. Furthermore, if the user has a specific hobby, the collection unit may customize and collect data related to the hobby. This allows for more appropriate data to be collected by customizing the collection items based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input information about the user's living situation and areas of interest into AI, which may then customize the collection items.

[0119] The collection unit can analyze the user's emotions and adjust the display method of the collected data based on the analyzed user's emotions. The collection unit can estimate the user's emotions and adjust the display method of the collected data based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the collection unit can display detailed data when the user is relaxed. The collection unit can also display concise data when the user is stressed. Furthermore, the collection unit can display data that is concise when the user is in a hurry. This allows the display method of the collected data to be adjusted according to the user's emotions, thereby providing data that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the display method of the collected data.

[0120] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information during collection. The collection unit prioritizes collecting highly relevant data taking into account the user's geographical location information during collection. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the user lives in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to the user's travel destination. Furthermore, if the user is planning to move, the collection unit can prioritize collecting data related to the area to which the user will move. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0121] The collection unit can analyze the user's social media activity and collect related data at the time of collection. The collection unit can analyze the user's social media activity and collect related data at the time of collection. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit, for example, automatically acquires information that the user has made public on social media and collects it as data. The collection unit can also analyze the user's social media activity and collect related data. Furthermore, the collection unit can collect related data by referring to the user's friendships on social media. In this way, related data can be automatically collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the user's social media activity into AI, which can collect related data.

[0122] The notification unit can analyze the user's emotions and adjust the notification expression method based on the analyzed user's emotions. The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the notification unit can provide a detailed notification when the user is relaxed. The notification unit can also provide a concise notification when the user is stressed. Furthermore, the notification unit can provide a notification that focuses on the main points when the user is in a hurry. This allows the notification expression method to be adjusted according to the user's emotions, making it possible to provide a notification that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the way the notification is expressed.

[0123] The notification unit can adjust the level of detail of the notification based on the importance of the procedure or application when sending a notification. The notification unit can adjust the level of detail of the notification based on the importance of the procedure or application when sending a notification. The importance includes, but is not limited to, urgency and impact, for example. The notification unit can provide detailed notifications for important procedures and applications, for example. The notification unit can also provide concise notifications for less important procedures and applications. Furthermore, the notification unit can determine the priority of notifications according to the importance of the procedure or application. By adjusting the level of detail of notifications based on the importance of the procedure or application, detailed notifications can be provided for important procedures and applications. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the importance of the procedure or application into AI, which can then adjust the level of detail of the notification.

[0124] The notification unit can apply different notification algorithms depending on the category of the procedure or application when notifying. The notification unit applies different notification algorithms depending on the category of the procedure or application when notifying. Categories include, but are not limited to, for example, the type of procedure and the type of application. For example, the notification unit can apply a notification algorithm specialized for administrative procedures to administrative procedures. Furthermore, the notification unit can also apply a notification algorithm specialized for subsidy applications to subsidy applications. Furthermore, the notification unit can also apply a notification algorithm specialized for sports club membership procedures to sports club membership procedures. In this way, by applying different notification algorithms depending on the category of the procedure or application, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the category of the procedure or application into AI, which can then apply an appropriate notification algorithm.

[0125] The notification unit can improve the accuracy of notifications based on the user's past notification results when notifying the user. The notification unit can improve the accuracy of notifications by referring to the user's past notification results when notifying the user. Past notification results include, but are not limited to, past notification history, user reactions, etc. The notification unit, for example, corrects the current notification based on the user's past notification results. The notification unit can also optimize the notification algorithm based on the user's past notification results. Furthermore, the notification unit can improve the accuracy of notifications by referring to the user's past notification results. In this way, the accuracy of notifications can be improved by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification results into AI, which can improve the accuracy of notifications.

[0126] The notification unit can analyze the user's emotions and adjust the length of the notification based on the analyzed user's emotions. The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the notification unit can provide a detailed notification when the user is relaxed. The notification unit can also provide a concise notification when the user is stressed. Furthermore, the notification unit can provide a notification that is concise when the user is in a hurry. This allows the notification length to be adjusted according to the user's emotions, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into a generation AI, which can estimate the emotion and adjust the length of the notification.

[0127] The notification unit can determine the priority of notifications based on the submission time of the procedure or application at the time of notification. The notification unit determines the priority of notifications based on the submission time of the procedure or application at the time of notification. The submission time includes, but is not limited to, for example, a submission deadline or a submission date. The notification unit, for example, prioritizes notifications for procedures or applications with upcoming submission deadlines. The notification unit can also adjust the notification schedule based on the submission time. Furthermore, the notification unit can postpone notifications for procedures or applications whose submission deadlines have passed. In this way, by determining the priority of notifications based on the submission time of the procedure or application, it is possible to prioritize notifications for procedures or applications with upcoming submission deadlines. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the submission time of the procedure or application into AI, and the AI ​​can determine the priority of notifications.

[0128] The notification unit can adjust the order of notifications based on the relevance of the procedures or applications when notifying. The notification unit adjusts the order of notifications based on the relevance of the procedures or applications when notifying. Relevance includes, but is not limited to, common attributes, related topics, and the like. For example, the notification unit prioritizes notifications of highly relevant procedures or applications. The notification unit can also determine the order of notifications based on the relevance of the procedures or applications. Furthermore, the notification unit can postpone notifications of less relevant procedures or applications. In this way, by adjusting the order of notifications based on the relevance of the procedures or applications, highly relevant procedures or applications can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance of the procedures or applications into AI, and the AI ​​can adjust the order of notifications.

[0129] The notification unit may adjust the use of technical terms in the notification based on the user's level of expertise. The notification unit may adjust the use of technical terms in the notification based on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has specialized knowledge, the notification unit may provide a notification that uses a lot of technical terms. Alternatively, if the user does not have specialized knowledge, the notification unit may provide a notification in simple language. Furthermore, the notification unit may adjust the way the notification is presented based on the user's level of expertise. By adjusting the use of technical terms in the notification based on the user's level of expertise, it is possible to provide a notification that is easy for the user to understand. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit may input the user's level of expertise into AI, which may then adjust the use of technical terms in the notification. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, each of the multiple elements including the input unit, analysis unit, recommendation unit, and provision unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input user attribute information using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends necessary procedures or applications based on the analysis results. The provision unit provides the recommended procedure or application format using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described input unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, each of the multiple elements including the input unit, analysis unit, recommendation unit, and provision unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input user attribute information using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends necessary procedures or applications based on the analysis results. The provision unit provides the recommended procedures or application formats using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, each of the multiple elements including the input unit, analysis unit, recommendation unit, and provision unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can input user attribute information using the microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends necessary procedures and applications based on the analysis results. The provision unit provides the recommended procedures and application formats using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, each of the multiple elements including the input unit, analysis unit, recommendation unit, and provision unit is realized by at least one of the robot 414 and the data processing device 12. For example, the input unit can input user attribute information using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends necessary procedures or applications based on the analysis results. The provision unit provides the recommended procedures or application formats using the speaker 240 of the robot 414.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is stressed, important procedures and applications can be analyzed first, and other procedures can be postponed. Alternatively, if the user is relaxed, all procedures can be analyzed equally. Furthermore, if the user is in a hurry, only the most important procedures can be analyzed first, and other procedures can be analyzed later. This allows important procedures to be analyzed quickly by determining the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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 can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can then estimate the emotion and determine the analysis priority.

[0132] The providing unit can estimate the user's emotions and adjust the design of the format to be provided based on the estimated user's emotions. For example, if the user is relaxed, a detailed and visually rich format can be provided. If the user is stressed, a concise and intuitive format can be provided. Furthermore, if the user is in a hurry, a simple format that focuses on the main points can be provided. By adjusting the design of the format to be provided according to the user's emotions, a format that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the design of the format to be provided.

[0133] The notification unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification frequency can be reduced and only important notifications can be sent. Alternatively, if the user is relaxed, notifications can be sent at a normal frequency. Furthermore, if the user is in a hurry, important notifications can be prioritized and other notifications can be postponed. This allows notifications to be sent at an appropriate time for the user by adjusting the frequency of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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 notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into a generation AI, which can estimate the emotion and adjust the frequency of notifications.

[0134] The recommendation unit can estimate the user's emotions and adjust the content of recommendations based on the estimated user emotions. For example, if the user is relaxed, detailed recommendations can be provided. Also, if the user is stressed, concise recommendations can be provided. Furthermore, if the user is in a hurry, recommendations that focus on the main points can be provided. By adjusting the content of recommendations according to the user's emotions, it is possible to provide recommendations that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the content of the recommendation.

[0135] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is relaxed, detailed data can be collected. Also, if the user is stressed, concise data can be collected. Furthermore, if the user is in a hurry, data that focuses on the main points can be collected. By adjusting the type of data to be collected according to the user's emotions, data that is important to the user can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the type of data to be collected.

[0136] The analysis unit can predict future behavior based on the user's past behavior history and determine analysis priorities based on the predicted behavior. For example, procedures that the user frequently performed in the past can be analyzed first, and other procedures can be postponed. The analysis priorities can also be determined based on the frequency of procedures that the user performed in the past. Furthermore, the analysis priorities can also be determined based on the importance of procedures that the user performed in the past. In this way, by determining the analysis priorities based on the user's past behavior history, important procedures can be analyzed quickly. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavior history into AI, which can predict future behavior and determine analysis priorities.

[0137] The providing unit can select the optimal format based on the user's past format usage history. For example, formats that the user has frequently used in the past can be provided preferentially, with other formats being provided later. The optimal format can also be selected based on the user's evaluation of formats used in the past. Furthermore, the optimal format can also be selected based on the frequency of use of formats used in the past by the user. In this way, by selecting the optimal format based on the user's past format usage history, it is possible to provide a format that is easy for the user to use. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past format usage history into AI, which can select the optimal format.

[0138] The notification unit can select the optimal notification method based on the user's past notification history. For example, it can prioritize notification methods (email, SMS, etc.) that the user has frequently used in the past, and postpone other notification methods. The optimal notification method can also be selected based on the user's evaluation of notifications received in the past. Furthermore, the optimal notification method can also be selected based on the user's reaction to notifications received in the past. In this way, by selecting the optimal notification method based on the user's past notification history, it is possible to provide notifications that are easy for the user to receive. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history into AI, which can select the optimal notification method.

[0139] The recommendation unit can select optimal recommendation content based on the user's past recommendation history. For example, the recommendation unit provides optimal recommendation content based on the user's evaluation of recommendations received in the past. The recommendation unit can also select optimal recommendation content based on the user's reaction to recommendations received in the past. Furthermore, the recommendation unit can also select optimal recommendation content based on the frequency of use of recommendations received in the past. In this way, by selecting optimal recommendation content based on the user's past recommendation history, it is possible to provide recommendations that are beneficial to the user. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation history into AI, which can select optimal recommendation content.

[0140] The collection unit can select the optimal data collection method based on the user's past data collection history. For example, the collection unit can provide data collection methods (such as questionnaires and interviews) that the user has used in the past with priority, and postpone other data collection methods. The collection unit can also select the optimal data collection method based on the user's evaluation of data collected in the past. Furthermore, the collection unit can also select the optimal data collection method based on the user's frequency of use of data collected in the past. In this way, efficient data collection can be provided to the user by selecting the optimal data collection method based on the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal data collection method.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The input unit inputs the user's attribute information. The user's attribute information includes, for example, age, gender, occupation, and place of residence. The input unit allows the user to input basic information such as name, address, age, and occupation. The user can also input information such as the area to which they are moving and their family composition. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. The analysis unit identifies the necessary procedures and applications based on the user's attribute information. It can also perform analysis based on the user's past behavioral history. Step 3: The recommendation unit recommends the necessary procedures and applications based on the information analyzed by the analysis unit. Recommendations are made based on the user's attribute information and event information. For example, it may recommend procedures for changing the user's address when moving, or procedures for enrolling a child in school. Recommendations can also be made based on the user's past behavioral history. Step 4: The provision unit provides the procedures and application formats recommended by the recommendation unit. Formats include, for example, PDF format, Word document, and online form. The provision unit provides the user with a format for joining a sports club in a specific area or for applying for a subsidy. It can also provide different formats for different age groups and areas.

[0143] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, a 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.

[0181] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0187] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0193] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0204] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0205] 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.

[0206] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0207] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0208] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0209] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0211] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting user attribute information; an analysis unit that analyzes the information input by the input unit; a recommendation unit that recommends a necessary procedure or application based on the information analyzed by the analysis unit; a providing unit that provides the procedure or application format recommended by the recommendation unit. A system characterized by:

2. The analysis unit A collection unit is provided to collect data to respond to specific events.

2. The system of claim 1.

3. The providing unit A notification unit is provided to notify the user.

2. The system of claim 1.

4. The recommendation unit Notify users of necessary procedures at the appropriate time based on their attribute information and event information 2. The system of claim 1.

5. The providing unit Providing different formats for different age groups and areas 2. The system of claim 1.

6. The collecting unit Collect information about the area or family structure of the user when they move 3. The system of claim 2.

7. The input unit The system estimates the user's emotions and adjusts the timing of inputting attribute information based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

9. The input unit When entering demographic information, customize the input based on the user's current life situation or interests 2. The system of claim 1.

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