System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently grasp administrative services and support information applied to the user.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs information on a family structure, an annual income, a chronic disease, a result of a medical checkup, and an examination item of the user. The analysis unit analyzes the information input by the reception unit and specifies information on a service or support that can be received in the area where the user lives. The providing unit summarizes and provides the information specified by the analysis 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 that it is difficult for users to efficiently grasp administrative services and support information that apply to them.

[0005] The system according to the embodiment aims to enable users to efficiently grasp administrative services and support information that apply to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs information such as the user's family structure, annual income, chronic illnesses, health checkup results, and considerations. The analysis unit analyzes the information input by the reception unit and identifies information on services and support available in the user's area. The provision unit summarizes and provides the information identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to efficiently grasp administrative services and support information that are applicable to the user. [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 administrative service support system according to an embodiment of the present invention allows users to easily understand and use information about administrative services and support that apply to them. The administrative service support system inputs information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The system analyzes this information to identify and summarize information about services and support available in the user's area. For example, a user inputs information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The administrative service support system then analyzes the input information and identifies information about services and support available in the user's area. Furthermore, the provided information summarizes and explains even complex content. This allows users to easily understand information about services and support that apply to them. For example, a user considering moving can learn about services and support available in their new area in advance. Furthermore, a user with a family member who requires care can easily obtain information about care-related support and services. This allows users to receive support tailored to their needs without having to frantically search local government websites or visit crowded service counters. For example, by entering information such as family composition, annual income, chronic illnesses, health checkup results, and other considerations, users can easily find information about services and support available in their area.In addition, the information provided is summarized and explained, even if it is complex, so users can easily understand the information about services and support that applies to them.

[0029] The administrative service support system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs information such as a user's family structure, annual income, chronic illnesses, health checkup results, and considerations. The information input by the user includes, but is not limited to, the number of family members, ages, relationships, pre-tax income, whether bonuses are included, chronic illnesses, allergies, blood test results, electrocardiogram results, home purchases, and education funds. For example, when the user inputs their family structure, the reception unit can input the number of family members, ages, and relationships. Furthermore, when the user inputs their annual income, the reception unit can input whether pre-tax income and bonuses are included. Furthermore, when the user inputs their chronic illnesses and health checkup results, the reception unit can input chronic illnesses, allergies, blood test results, and electrocardiogram results. Furthermore, when the user inputs their considerations, the reception unit can input home purchases, education funds, and the like. The analysis unit analyzes the information input by the reception unit and identifies information on services and support available in the user's area. The analysis is performed using, for example, data mining, statistical analysis, or other methods, but is not limited to, these examples. For example, the analysis unit may use data mining technology to analyze the user's information and identify information on services and support available in the user's area. The analysis unit may also use statistical analysis technology to analyze the user's information and identify information on services and support available in the user's area. The analysis unit may also use a machine learning algorithm to analyze the user's information and identify information on services and support available in the user's area. The providing unit may summarize and provide the information identified by the analysis unit. The summarization may be performed, for example, based on the importance of the information and the length of the summary, but is not limited to such examples. For example, the providing unit may prioritize and provide a summary of important information based on the importance of the information. The providing unit may also provide a concise summary based on the length of the summary. The providing unit may also provide an appropriate summary based on the content of the information. This allows the administrative service support system according to the embodiment to easily grasp and use information on administrative services and support applicable to the user.For example, by entering information such as family composition, annual income, chronic illnesses, health checkup results, and other considerations, users can easily find information about services and support available in their area.In addition, the information provided is summarized and explained, even if it is complex, so users can easily understand the information about services and support that applies to them.

[0030] The providing unit can summarize complex information posted on a government website to make it easier to understand and provide it. Examples of complex information include, but are not limited to, laws and regulations and procedural details. For example, the providing unit can summarize information about laws and regulations posted on a government website to make it easier to understand and provide it. The providing unit can also summarize procedural details to make it easier to understand and provide it. For example, the providing unit can briefly summarize the provisions of laws and regulations and provide it to a user. The providing unit can also briefly summarize procedural steps and provide it to a user. This allows the user to easily understand complex government information. For example, when a user searches for information about laws and regulations, the user can easily understand the content of the laws and regulations by referring to the information briefly summarized by the providing unit. Furthermore, when a user searches for procedural details, the user can easily understand the procedural steps by referring to the information briefly summarized by the providing unit.

[0031] The analysis unit can periodically update the data to provide the latest information. Periodically includes, but is not limited to, daily, weekly, monthly, and the like. For example, the analysis unit can update the data daily to provide the latest information. The analysis unit can also update the data weekly to provide the latest information. For example, the analysis unit can update the database daily to provide the latest service and support information. The analysis unit can also update the database weekly to provide the latest service and support information. This allows the user to always be provided with the latest information. For example, when a user searches for service or support information, the analysis unit can provide the latest information, allowing the user to grasp the latest service and support information. Furthermore, when a user periodically checks service or support information, the analysis unit can provide the latest information, allowing the user to always be provided with the latest information.

[0032] The reception unit can analyze the user's past input history and suggest an optimal input method. Examples of optimal input methods include, but are not limited to, input speed and accuracy. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest information that the user will use in a specific time period based on the user's past input history. This makes it possible to provide an optimal input method based on the user's past input history. For example, the reception unit can suggest an optimal input method based on information that the user has input in the past. The reception unit can also analyze the user's past input history and suggest an optimal input method. For example, the reception unit can suggest an optimal input method based on input methods that the user has used in the past. This makes it possible to provide an optimal input method based on the user's past input history.

[0033] The reception unit can automatically customize input items based on the user's current living situation and areas of interest when inputting information. Examples of living situations include, but are not limited to, family structure, income, and health status. Examples of areas of interest include, but are not limited to, hobbies, occupations, and academic fields. For example, if the user is pregnant, the reception unit can prioritize displaying input items related to pregnancy. Furthermore, if the user is considering changing jobs, the reception unit can automatically add input items related to changing jobs. For example, if the user needs nursing care, the reception unit can prioritize displaying input items related to nursing care. This makes it possible to provide optimal input items according to the user's living situation and areas of interest. For example, if the user is pregnant, the reception unit can prioritize displaying input items related to pregnancy. Furthermore, if the user is considering changing jobs, the reception unit can automatically add input items related to changing jobs. Furthermore, if the user needs nursing care, the reception unit can prioritize displaying input items related to nursing care. This makes it possible to provide optimal input items according to the user's living situation and areas of interest. For example, if the user is pregnant, the reception unit can prioritize displaying input items related to pregnancy. In addition, if the user is considering changing jobs, the reception unit can automatically add input items related to changing jobs. Furthermore, if the user needs nursing care, the reception unit can preferentially display input items related to nursing care. This makes it possible to provide optimal input items according to the user's living situation and areas of interest.

[0034] The reception unit can select the optimal input means depending on the user's input method at the time of input. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially provide keyboard input. For example, if the user selects image input, the reception unit inputs information using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially provide keyboard input. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method.

[0035] When inputting data, the reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if the user lives in a specific area, the reception unit can prioritize displaying input items related to that area. Furthermore, if the user is traveling, the reception unit can automatically add input items related to the travel destination. For example, if the user is considering moving, the reception unit can prioritize displaying input items related to the new area. This makes it possible to provide optimal input items based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying input items related to that area. Furthermore, if the user is traveling, the reception unit can automatically add input items related to the travel destination. Furthermore, if the user is considering moving, the reception unit can prioritize displaying input items related to the new area. This makes it possible to provide optimal input items based on the user's geographical location information.

[0036] The reception unit can analyze the user's social media activity at the time of input and suggest related input items. 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 reception unit can suggest related input items, for example, based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically add related input items. For example, the reception unit can suggest related input items by referring to the activities of the user's friends on social media. This makes it possible to provide optimal input items based on the user's social media activity. For example, the reception unit can suggest related input items based on the information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically add related input items. Furthermore, the reception unit can suggest related input items by referring to the activities of the user's friends on social media. This makes it possible to provide optimal input items based on the user's social media activity.

[0037] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. Past feedback includes, but is not limited to, user ratings and comments, for example. The reception unit can suggest an optimal input method based on, for example, feedback previously provided by the user. The reception unit can also improve the input interface and make it easier to use based on the user's past feedback. For example, the reception unit can adjust the order and content of input items by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback. For example, the reception unit can suggest an optimal input method based on the user's past feedback. The reception unit can also improve the input interface and make it easier to use based on the user's past feedback. For example, the reception unit can suggest an optimal input method based on the user's past feedback. The reception unit can also improve the input interface and make it easier to use based on the user's past feedback. Furthermore, the reception unit can adjust the order and content of input items by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. Past data includes, for example, past input history, past analysis results, etc., but is not limited to these examples. The analysis unit can improve the accuracy of the analysis, for example, based on the user's past input data. The analysis unit can also optimize the analysis results by referring to the user's past usage history. For example, the analysis unit can improve the analysis algorithm by reflecting the user's past feedback. This makes it possible to provide optimal analysis results based on the user's past data. For example, the analysis unit can improve the accuracy of the analysis based on the user's past input data. The analysis unit can also optimize the analysis results by referring to the user's past usage history. Furthermore, the analysis unit can improve the analysis algorithm by reflecting the user's past feedback. This makes it possible to provide optimal analysis results based on the user's past data.

[0039] During analysis, the analysis unit can optimize the analysis results by taking into account the user's lifestyle rhythm and behavioral patterns. Lifestyle rhythms include, for example, but are not limited to, sleep patterns and meal times. Behavior patterns include, for example, but are not limited to, commute time and exercise habits. The analysis unit provides optimal analysis results based on, for example, the user's lifestyle rhythm. The analysis unit can also customize the analysis results by taking into account the user's behavioral patterns. For example, the analysis unit can analyze the user's lifestyle rhythm and behavioral patterns and provide the most suitable analysis results. This makes it possible to provide optimal analysis results based on the user's lifestyle rhythm and behavioral patterns. For example, the analysis unit provides optimal analysis results based on the user's lifestyle rhythm. The analysis unit can also customize the analysis results by taking into account the user's behavioral patterns. Furthermore, the analysis unit can analyze the user's lifestyle rhythm and behavioral patterns and provide the most suitable analysis results. This makes it possible to provide optimal analysis results based on the user's lifestyle rhythm and behavioral patterns.

[0040] The analysis unit can perform the analysis while taking into account the user's attribute information. The attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit provides optimal analysis results, for example, taking into account the user's age. The analysis unit can also customize the analysis results based on the user's gender. For example, the analysis unit optimizes the analysis results by taking into account the user's occupation. This makes it possible to provide optimal analysis results based on the user's attribute information. For example, the analysis unit provides optimal analysis results by taking into account the user's age. The analysis unit can also customize the analysis results based on the user's gender. Furthermore, the analysis unit can optimize the analysis results by taking into account the user's occupation. This makes it possible to provide optimal analysis results based on the user's attribute information.

[0041] The analysis unit may perform the analysis while taking into account the geographical distribution of the user. Examples of the geographical distribution include, but are not limited to, urban areas and rural areas. The analysis unit may provide the analysis result while taking into account the characteristics of the area where the user lives. The analysis unit may also provide an optimal analysis result based on the geographical distribution of the user. For example, the analysis unit may refer to data related to the user's area to improve the accuracy of the analysis. This allows the optimal analysis result to be provided based on the user's geographical distribution. For example, the analysis unit may provide the analysis result while taking into account the characteristics of the area where the user lives. The analysis unit may also provide an optimal analysis result based on the user's geographical distribution. The analysis unit may also refer to data related to the user's area to improve the accuracy of the analysis. This allows the optimal analysis result to be provided based on the user's geographical distribution.

[0042] During analysis, the analysis unit may refer to related external data sources to improve the accuracy of the analysis. Examples of external data sources include, but are not limited to, statistical data and market data. For example, the analysis unit may refer to statistical data to improve the accuracy of the analysis results. The analysis unit may also optimize the analysis results based on market data. For example, the analysis unit may refer to related external data sources to improve the analysis algorithm. This makes it possible to provide optimal analysis results based on the related external data sources. For example, the analysis unit may refer to statistical data to improve the accuracy of the analysis results. The analysis unit may also optimize the analysis results based on market data. Furthermore, the analysis unit may refer to related external data sources to improve the analysis algorithm. This makes it possible to provide optimal analysis results based on the related external data sources.

[0043] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, etc., but is not limited to these examples. The analysis unit can adjust the analysis algorithm, for example, based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis result by reflecting the user's feedback. For example, the analysis unit can improve the analysis algorithm by referring to the user's past feedback. This makes it possible to provide an optimal analysis result based on the user's past feedback. For example, the analysis unit can adjust the analysis algorithm based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis result by reflecting the user's feedback. Furthermore, the analysis unit can improve the analysis algorithm by referring to the user's past feedback. This makes it possible to provide an optimal analysis result based on the user's past feedback.

[0044] The providing unit can adjust the level of detail of the display based on the importance of the information when providing the information. The importance of the information includes, for example, legal importance, the level of interest of the user, etc., but is not limited to such examples. For example, the providing unit displays important information in detail and other information in a concise manner. The providing unit can also preferentially display information in which the user is interested in detail. For example, the providing unit dynamically adjusts the level of detail of the display according to the importance of the information. This makes it possible to provide an optimal level of display detail according to the importance of the information. For example, the providing unit displays important information in detail and other information in a concise manner. The providing unit can also preferentially display information in which the user is interested in detail. Furthermore, the providing unit can dynamically adjust the level of detail of the display according to the importance of the information. This makes it possible to provide an optimal level of display detail according to the importance of the information.

[0045] The providing unit can apply different display algorithms depending on the category of information when providing the information. Information categories include, for example, legal information, procedural information, and the like, but are not limited to these examples. For example, the providing unit displays health-related information in a visually easy-to-understand graph or chart. The providing unit can also display legal information in concise text. For example, the providing unit displays service-related information together with detailed explanations. This makes it possible to provide an optimal display algorithm depending on the category of information. For example, the providing unit displays health-related information in a visually easy-to-understand graph or chart. The providing unit can also display legal information in concise text. The providing unit can also display service-related information together with detailed explanations. This makes it possible to provide an optimal display algorithm depending on the category of information.

[0046] When providing the information, the providing unit can improve the accuracy of the display by referring to the user's past browsing history. The past browsing history includes, for example, viewed pages, browsing time, etc., but is not limited to these examples. For example, the providing unit preferentially displays related information based on the user's past browsing history. The providing unit can also customize the display content by referring to information previously viewed by the user. For example, the providing unit analyzes the user's past browsing history and provides an optimal display method. This makes it possible to provide optimal display content based on the user's past browsing history. For example, the providing unit preferentially displays related information based on the user's past browsing history. The providing unit can also customize the display content by referring to information previously viewed by the user. Furthermore, the providing unit can analyze the user's past browsing history and provide an optimal display method. This makes it possible to provide optimal display content based on the user's past browsing history.

[0047] The providing unit can determine a display priority based on the submission time of the information when the information is provided. The submission time of the information includes, but is not limited to, for example, the submission date and the submission deadline. For example, the providing unit can prioritize displaying the latest information and postpone displaying older information. The providing unit can also prioritize displaying information with an upcoming submission time and postpone displaying information with a more distant deadline. For example, the providing unit dynamically adjusts the display order of the information according to the submission time. This makes it possible to provide an optimal display priority according to the submission time of the information. For example, the providing unit prioritizes displaying the latest information and postpones displaying older information. The providing unit can also prioritize displaying information with an upcoming submission time and postpone displaying information with a more distant deadline. Furthermore, the providing unit can dynamically adjust the display order of the information according to the submission time. This makes it possible to provide an optimal display priority according to the submission time of the information.

[0048] The providing unit can adjust the display order based on the relevance of the information when providing the information. The relevance of the information includes, for example, similarity of content, related topics, etc., but is not limited to such examples. For example, the providing unit preferentially displays information related to a user's field of interest. Furthermore, the providing unit can also preferentially display highly relevant information based on the user's past browsing history. For example, the providing unit dynamically adjusts the display order according to the relevance of the information. This makes it possible to provide an optimal display order according to the relevance of the information. For example, the providing unit preferentially displays information related to a user's field of interest. Furthermore, the providing unit can also preferentially display highly relevant information based on the user's past browsing history. Furthermore, the providing unit can dynamically adjust the display order according to the relevance of the information. This makes it possible to provide an optimal display order according to the relevance of the information.

[0049] The providing unit can customize the display content according to the user's expertise level when providing the information. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, the providing unit can provide detailed technical information if the user has expertise. Furthermore, the providing unit can provide concise and easy-to-understand information if the user is a beginner. For example, the providing unit dynamically adjusts the display content according to the user's expertise level. This makes it possible to provide optimal display content according to the user's expertise level. For example, the providing unit can provide detailed technical information if the user has expertise. Furthermore, the providing unit can provide concise and easy-to-understand information if the user is a beginner. Furthermore, the providing unit can dynamically adjust the display content according to the user's expertise level. This makes it possible to provide optimal display content according to the user's expertise level.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can optimize the analysis results by taking into account the user's lifestyle and behavioral patterns. For example, it can provide optimal analysis results based on the user's sleep patterns and meal times. It can also customize the analysis results by taking into account the user's commute time and exercise habits. This makes it possible to provide optimal analysis results based on the user's lifestyle and behavioral patterns. For example, if the user is a nocturnal person, services available at night can be displayed preferentially. Also, if the user is health-conscious, it can emphasize and provide health-related services. Furthermore, if the user travels frequently, it can suggest services available at the user's business trip destination.

[0052] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently input in the past as a candidate. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. This makes it possible to provide the optimal input method based on the user's past input history. For example, it can predict and suggest information that the user will use in a specific time period based on information that the user has input in the past. It can also suggest the optimal input method based on input methods that the user has used in the past. It can also analyze the user's past input history and provide advice to improve input speed and accuracy.

[0053] The providing unit can adjust the level of detail of the display based on the importance of the information when providing the information. For example, important information can be displayed in detail, and other information can be displayed briefly. Also, information that the user is interested in can be preferentially displayed in detail. This makes it possible to provide an optimal level of detail of display according to the importance of the information. For example, information of high legal importance can be displayed in detail, and general information can be displayed briefly. Also, information related to topics that the user is particularly interested in can be preferentially displayed. Furthermore, the level of detail of display can be dynamically adjusted according to the importance of the information.

[0054] The providing unit can apply different display algorithms depending on the category of information when providing the information. For example, health information can be displayed in visually easy-to-understand graphs and charts. Legal information can also be displayed in simple text. This makes it possible to provide an optimal display algorithm depending on the category of information. For example, information about services can be displayed with detailed descriptions. Education information can also be displayed visually using infographics. Furthermore, the display style and format can be dynamically adjusted depending on the category of information.

[0055] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data sources. For example, it can improve the accuracy of the analysis results by referring to statistical data. It can also optimize the analysis results based on market data. This makes it possible to provide optimal analysis results based on related external data sources. For example, it can analyze the service provision status by region by referring to government statistical data. It can also propose support measures based on the user's income based on economic data. It can also improve the analysis algorithm by referring to related external data sources.

[0056] When inputting data, the reception unit can prioritize displaying highly relevant input items taking into account the user's geographical location information. For example, if the user lives in a specific area, input items related to that area can be prioritized. Also, if the user is traveling, input items related to the travel destination can be automatically added. This makes it possible to provide optimal input items based on the user's geographical location information. For example, if the user is considering moving, input items related to the new area can be prioritized. Also, if the user is looking for services in a specific area, information related to that area can be prioritized. Furthermore, it is possible to suggest services and support measures specific to the area based on the user's geographical location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The receptionist inputs information such as the user's family structure, annual income, chronic illnesses and health checkup results, and considerations, such as the number of family members, ages, relationships, pre-tax income, whether bonuses are included, chronic illnesses, allergies, blood test results, electrocardiogram results, home purchases, and education funds. Step 2: The analysis unit analyzes the information entered by the reception unit and identifies information about services and support available in the user's area. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. Step 3: The providing unit summarizes and provides the information identified by the analyzing unit, based on the importance of the information and the length of the summary.

[0059] (Example 2) An administrative service support system according to an embodiment of the present invention allows users to easily understand and use information about administrative services and support that apply to them. The administrative service support system inputs information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The system analyzes this information to identify and summarize information about services and support available in the user's area. For example, a user inputs information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The administrative service support system then analyzes the input information and identifies information about services and support available in the user's area. Furthermore, the provided information summarizes and explains even complex content. This allows users to easily understand information about services and support that apply to them. For example, a user considering moving can learn about services and support available in their new area in advance. Furthermore, a user with a family member who requires care can easily obtain information about care-related support and services. This allows users to receive support tailored to their needs without having to frantically search local government websites or visit crowded service counters. For example, by entering information such as family composition, annual income, chronic illnesses, health checkup results, and other considerations, users can easily find information about services and support available in their area.In addition, the information provided is summarized and explained, even if it is complex, so users can easily understand the information about services and support that applies to them.

[0060] The administrative service support system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs information such as a user's family structure, annual income, chronic illnesses, health checkup results, and considerations. The information input by the user includes, but is not limited to, the number of family members, ages, relationships, pre-tax income, whether bonuses are included, chronic illnesses, allergies, blood test results, electrocardiogram results, home purchases, and education funds. For example, when the user inputs their family structure, the reception unit can input the number of family members, ages, and relationships. Furthermore, when the user inputs their annual income, the reception unit can input whether pre-tax income and bonuses are included. Furthermore, when the user inputs their chronic illnesses and health checkup results, the reception unit can input chronic illnesses, allergies, blood test results, and electrocardiogram results. Furthermore, when the user inputs their considerations, the reception unit can input home purchases, education funds, and the like. The analysis unit analyzes the information input by the reception unit and identifies information on services and support available in the user's area. The analysis is performed using, for example, data mining, statistical analysis, or other methods, but is not limited to, these examples. For example, the analysis unit may use data mining technology to analyze the user's information and identify information on services and support available in the user's area. The analysis unit may also use statistical analysis technology to analyze the user's information and identify information on services and support available in the user's area. The analysis unit may also use a machine learning algorithm to analyze the user's information and identify information on services and support available in the user's area. The providing unit may summarize and provide the information identified by the analysis unit. The summarization may be performed, for example, based on the importance of the information and the length of the summary, but is not limited to such examples. For example, the providing unit may prioritize and provide a summary of important information based on the importance of the information. The providing unit may also provide a concise summary based on the length of the summary. The providing unit may also provide an appropriate summary based on the content of the information. This allows the administrative service support system according to the embodiment to easily grasp and use information on administrative services and support applicable to the user.For example, by entering information such as family composition, annual income, chronic illnesses, health checkup results, and other considerations, users can easily find information about services and support available in their area.In addition, the information provided is summarized and explained, even if it is complex, so users can easily understand the information about services and support that applies to them.

[0061] The providing unit can summarize complex information posted on a government website to make it easier to understand and provide it. Examples of complex information include, but are not limited to, laws and regulations and procedural details. For example, the providing unit can summarize information about laws and regulations posted on a government website to make it easier to understand and provide it. The providing unit can also summarize procedural details to make it easier to understand and provide it. For example, the providing unit can briefly summarize the provisions of laws and regulations and provide it to a user. The providing unit can also briefly summarize procedural steps and provide it to a user. This allows the user to easily understand complex government information. For example, when a user searches for information about laws and regulations, the user can easily understand the content of the laws and regulations by referring to the information briefly summarized by the providing unit. Furthermore, when a user searches for procedural details, the user can easily understand the procedural steps by referring to the information briefly summarized by the providing unit.

[0062] The analysis unit can periodically update the data to provide the latest information. Periodically includes, but is not limited to, daily, weekly, monthly, and the like. For example, the analysis unit can update the data daily to provide the latest information. The analysis unit can also update the data weekly to provide the latest information. For example, the analysis unit can update the database daily to provide the latest service and support information. The analysis unit can also update the database weekly to provide the latest service and support information. This allows the user to always be provided with the latest information. For example, when a user searches for service or support information, the analysis unit can provide the latest information, allowing the user to grasp the latest service and support information. Furthermore, when a user periodically checks service or support information, the analysis unit can provide the latest information, allowing the user to always be provided with the latest information.

[0063] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow quick information input. This allows for an optimal input interface to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal input interface according to the user's emotions. Furthermore, the reception unit can accumulate the user's emotion data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotion data over time to identify patterns of emotion change. The reception unit also has an algorithm that provides individual feedback based on the emotion data. For example, the reception unit can provide encouraging messages or advice according to the user's emotional state. This makes it possible to grasp the user's emotions in detail and provide feedback according to individual needs.

[0064] The reception unit can analyze the user's past input history and suggest an optimal input method. Examples of optimal input methods include, but are not limited to, input speed and accuracy. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest information that the user will use in a specific time period based on the user's past input history. This makes it possible to provide an optimal input method based on the user's past input history. For example, the reception unit can suggest an optimal input method based on information that the user has input in the past. The reception unit can also analyze the user's past input history and suggest an optimal input method. For example, the reception unit can suggest an optimal input method based on input methods that the user has used in the past. This makes it possible to provide an optimal input method based on the user's past input history.

[0065] The reception unit can automatically customize input items based on the user's current living situation and areas of interest when inputting information. Examples of living situations include, but are not limited to, family structure, income, and health status. Examples of areas of interest include, but are not limited to, hobbies, occupations, and academic fields. For example, if the user is pregnant, the reception unit can prioritize displaying input items related to pregnancy. Furthermore, if the user is considering changing jobs, the reception unit can automatically add input items related to changing jobs. For example, if the user needs nursing care, the reception unit can prioritize displaying input items related to nursing care. This makes it possible to provide optimal input items according to the user's living situation and areas of interest. For example, if the user is pregnant, the reception unit can prioritize displaying input items related to pregnancy. Furthermore, if the user is considering changing jobs, the reception unit can automatically add input items related to changing jobs. Furthermore, if the user needs nursing care, the reception unit can prioritize displaying input items related to nursing care. This makes it possible to provide optimal input items according to the user's living situation and areas of interest. For example, if the user is pregnant, the reception unit can prioritize displaying input items related to pregnancy. In addition, if the user is considering changing jobs, the reception unit can automatically add input items related to changing jobs. Furthermore, if the user needs nursing care, the reception unit can preferentially display input items related to nursing care. This makes it possible to provide optimal input items according to the user's living situation and areas of interest.

[0066] The reception unit can select the optimal input means depending on the user's input method at the time of input. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially provide keyboard input. For example, if the user selects image input, the reception unit inputs information using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially provide keyboard input. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method.

[0067] The reception unit can estimate the user's emotions and dynamically change the priority of input items based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is nervous, the reception unit can prioritize displaying important input items and postpone other items. Furthermore, if the user is relaxed, the reception unit can sequentially display detailed input items. For example, if the user is in a hurry, the reception unit can display only the most important input items to allow the user to complete input quickly. This allows optimal prioritization of input items 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the system to provide optimal prioritization of input items according to the user's emotions. Furthermore, the reception unit can accumulate the user's emotion data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotion data over time to identify patterns of emotion change. The reception unit also has an algorithm that provides individual feedback based on the emotion data. For example, the reception unit can provide encouraging messages or advice according to the user's emotional state. This allows the system to grasp the user's emotions in detail and provide feedback according to individual needs.

[0068] When inputting data, the reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if the user lives in a specific area, the reception unit can prioritize displaying input items related to that area. Furthermore, if the user is traveling, the reception unit can automatically add input items related to the travel destination. For example, if the user is considering moving, the reception unit can prioritize displaying input items related to the new area. This makes it possible to provide optimal input items based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying input items related to that area. Furthermore, if the user is traveling, the reception unit can automatically add input items related to the travel destination. Furthermore, if the user is considering moving, the reception unit can prioritize displaying input items related to the new area. This makes it possible to provide optimal input items based on the user's geographical location information.

[0069] The reception unit can analyze the user's social media activity at the time of input and suggest related input items. 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 reception unit can suggest related input items, for example, based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically add related input items. For example, the reception unit can suggest related input items by referring to the activities of the user's friends on social media. This makes it possible to provide optimal input items based on the user's social media activity. For example, the reception unit can suggest related input items based on the information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically add related input items. Furthermore, the reception unit can suggest related input items by referring to the activities of the user's friends on social media. This makes it possible to provide optimal input items based on the user's social media activity.

[0070] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. Past feedback includes, but is not limited to, user ratings and comments, for example. The reception unit can suggest an optimal input method based on, for example, feedback previously provided by the user. The reception unit can also improve the input interface and make it easier to use based on the user's past feedback. For example, the reception unit can adjust the order and content of input items by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback. For example, the reception unit can suggest an optimal input method based on the user's past feedback. The reception unit can also improve the input interface and make it easier to use based on the user's past feedback. For example, the reception unit can suggest an optimal input method based on the user's past feedback. The reception unit can also improve the input interface and make it easier to use based on the user's past feedback. Furthermore, the reception unit can adjust the order and content of input items by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback.

[0071] The analysis unit can estimate the user's emotions and dynamically adjust the parameters of the analysis algorithm 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 analysis unit performs a detailed analysis and provides highly accurate results. Furthermore, when the user is in a hurry, the analysis unit can perform a quick analysis and provide results quickly. For example, when the user is stressed, the analysis unit provides a concise and easy-to-understand analysis result. This allows for providing optimal analysis results 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to provide optimal analysis results according to the user's emotions. Furthermore, the analysis unit can accumulate the user's emotion data and analyze long-term emotional trends. For example, the analysis unit can analyze the user's emotion data over time to identify patterns of emotional change. The analysis unit also has an algorithm that provides individual feedback based on the emotion data. For example, the analysis unit can provide encouraging messages or advice according to the user's emotional state. This allows the analysis unit to grasp the user's emotions in detail and provide feedback that meets their individual needs.

[0072] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. Past data includes, for example, past input history, past analysis results, etc., but is not limited to these examples. The analysis unit can improve the accuracy of the analysis, for example, based on the user's past input data. The analysis unit can also optimize the analysis results by referring to the user's past usage history. For example, the analysis unit can improve the analysis algorithm by reflecting the user's past feedback. This makes it possible to provide optimal analysis results based on the user's past data. For example, the analysis unit can improve the accuracy of the analysis based on the user's past input data. The analysis unit can also optimize the analysis results by referring to the user's past usage history. Furthermore, the analysis unit can improve the analysis algorithm by reflecting the user's past feedback. This makes it possible to provide optimal analysis results based on the user's past data.

[0073] During analysis, the analysis unit can optimize the analysis results by taking into account the user's lifestyle rhythm and behavioral patterns. Lifestyle rhythms include, for example, but are not limited to, sleep patterns and meal times. Behavior patterns include, for example, but are not limited to, commute time and exercise habits. The analysis unit provides optimal analysis results based on, for example, the user's lifestyle rhythm. The analysis unit can also customize the analysis results by taking into account the user's behavioral patterns. For example, the analysis unit can analyze the user's lifestyle rhythm and behavioral patterns and provide the most suitable analysis results. This makes it possible to provide optimal analysis results based on the user's lifestyle rhythm and behavioral patterns. For example, the analysis unit provides optimal analysis results based on the user's lifestyle rhythm. The analysis unit can also customize the analysis results by taking into account the user's behavioral patterns. Furthermore, the analysis unit can analyze the user's lifestyle rhythm and behavioral patterns and provide the most suitable analysis results. This makes it possible to provide optimal analysis results based on the user's lifestyle rhythm and behavioral patterns.

[0074] The analysis unit can perform the analysis while taking into account the user's attribute information. The attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit provides optimal analysis results, for example, taking into account the user's age. The analysis unit can also customize the analysis results based on the user's gender. For example, the analysis unit optimizes the analysis results by taking into account the user's occupation. This makes it possible to provide optimal analysis results based on the user's attribute information. For example, the analysis unit provides optimal analysis results by taking into account the user's age. The analysis unit can also customize the analysis results based on the user's gender. Furthermore, the analysis unit can optimize the analysis results by taking into account the user's occupation. This makes it possible to provide optimal analysis results based on the user's attribute information.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows for providing an optimal display method of the analysis results 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal display method for analysis results according to the user's emotions. Furthermore, the analysis unit can accumulate the user's emotion data and analyze long-term emotional trends. For example, the analysis unit can analyze the user's emotion data over time to identify patterns of emotional change. The analysis unit also has an algorithm that provides individual feedback based on the emotion data. For example, the analysis unit can provide encouraging messages or advice according to the user's emotional state. This makes it possible to understand the user's emotions in detail and provide feedback that meets individual needs.

[0076] The analysis unit may perform the analysis while taking into account the geographical distribution of the user. Examples of the geographical distribution include, but are not limited to, urban areas and rural areas. The analysis unit may provide the analysis result while taking into account the characteristics of the area where the user lives. The analysis unit may also provide an optimal analysis result based on the geographical distribution of the user. For example, the analysis unit may refer to data related to the user's area to improve the accuracy of the analysis. This allows the optimal analysis result to be provided based on the user's geographical distribution. For example, the analysis unit may provide the analysis result while taking into account the characteristics of the area where the user lives. The analysis unit may also provide an optimal analysis result based on the user's geographical distribution. The analysis unit may also refer to data related to the user's area to improve the accuracy of the analysis. This allows the optimal analysis result to be provided based on the user's geographical distribution.

[0077] During analysis, the analysis unit may refer to related external data sources to improve the accuracy of the analysis. Examples of external data sources include, but are not limited to, statistical data and market data. For example, the analysis unit may refer to statistical data to improve the accuracy of the analysis results. The analysis unit may also optimize the analysis results based on market data. For example, the analysis unit may refer to related external data sources to improve the analysis algorithm. This makes it possible to provide optimal analysis results based on the related external data sources. For example, the analysis unit may refer to statistical data to improve the accuracy of the analysis results. The analysis unit may also optimize the analysis results based on market data. Furthermore, the analysis unit may refer to related external data sources to improve the analysis algorithm. This makes it possible to provide optimal analysis results based on the related external data sources.

[0078] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, etc., but is not limited to these examples. The analysis unit can adjust the analysis algorithm, for example, based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis result by reflecting the user's feedback. For example, the analysis unit can improve the analysis algorithm by referring to the user's past feedback. This makes it possible to provide an optimal analysis result based on the user's past feedback. For example, the analysis unit can adjust the analysis algorithm based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis result by reflecting the user's feedback. Furthermore, the analysis unit can improve the analysis algorithm by referring to the user's past feedback. This makes it possible to provide an optimal analysis result based on the user's past feedback.

[0079] The providing unit can estimate the user's emotions and dynamically change the information display method based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method that includes detailed information. For example, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. This makes it possible to provide an optimal information display method according to the user's emotions. 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. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal information display method according to the user's emotions. Furthermore, the providing unit can accumulate the user's emotion data and analyze long-term emotional trends. For example, the providing unit can analyze the user's emotion data over time to identify patterns of emotion change. The providing unit also has an algorithm that provides individual feedback based on the emotion data. For example, the providing unit can provide encouraging messages or advice according to the user's emotional state. This makes it possible to grasp the user's emotions in detail and provide feedback according to individual needs.

[0080] The providing unit can adjust the level of detail of the display based on the importance of the information when providing the information. The importance of the information includes, for example, legal importance, the level of interest of the user, etc., but is not limited to such examples. For example, the providing unit displays important information in detail and other information in a concise manner. The providing unit can also preferentially display information in which the user is interested in detail. For example, the providing unit dynamically adjusts the level of detail of the display according to the importance of the information. This makes it possible to provide an optimal level of display detail according to the importance of the information. For example, the providing unit displays important information in detail and other information in a concise manner. The providing unit can also preferentially display information in which the user is interested in detail. Furthermore, the providing unit can dynamically adjust the level of detail of the display according to the importance of the information. This makes it possible to provide an optimal level of display detail according to the importance of the information.

[0081] The providing unit can apply different display algorithms depending on the category of information when providing the information. Information categories include, for example, legal information, procedural information, and the like, but are not limited to these examples. For example, the providing unit displays health-related information in a visually easy-to-understand graph or chart. The providing unit can also display legal information in concise text. For example, the providing unit displays service-related information together with detailed explanations. This makes it possible to provide an optimal display algorithm depending on the category of information. For example, the providing unit displays health-related information in a visually easy-to-understand graph or chart. The providing unit can also display legal information in concise text. The providing unit can also display service-related information together with detailed explanations. This makes it possible to provide an optimal display algorithm depending on the category of information.

[0082] When providing the information, the providing unit can improve the accuracy of the display by referring to the user's past browsing history. The past browsing history includes, for example, viewed pages, browsing time, etc., but is not limited to these examples. For example, the providing unit preferentially displays related information based on the user's past browsing history. The providing unit can also customize the display content by referring to information previously viewed by the user. For example, the providing unit analyzes the user's past browsing history and provides an optimal display method. This makes it possible to provide optimal display content based on the user's past browsing history. For example, the providing unit preferentially displays related information based on the user's past browsing history. The providing unit can also customize the display content by referring to information previously viewed by the user. Furthermore, the providing unit can analyze the user's past browsing history and provide an optimal display method. This makes it possible to provide optimal display content based on the user's past browsing history.

[0083] The providing unit can estimate the user's emotions and adjust the display order of information based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is nervous, the providing unit can prioritize displaying important information and postpone other information. Furthermore, if the user is relaxed, the providing unit can sequentially display detailed information. For example, if the user is in a hurry, the providing unit can display only the most important information and provide it quickly. This allows for an optimal display order of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal display order for information according to the user's emotions. Furthermore, the providing unit can accumulate the user's emotion data and analyze long-term emotional trends. For example, the providing unit analyzes the user's emotion data over time to identify patterns of emotion change. The providing unit also has an algorithm that provides individual feedback based on the emotion data. For example, the providing unit provides encouraging messages or advice according to the user's emotional state. This makes it possible to grasp the user's emotions in detail and provide feedback according to individual needs.

[0084] The providing unit can determine a display priority based on the submission time of the information when the information is provided. The submission time of the information includes, but is not limited to, for example, the submission date and the submission deadline. For example, the providing unit can prioritize displaying the latest information and postpone displaying older information. The providing unit can also prioritize displaying information with an upcoming submission time and postpone displaying information with a more distant deadline. For example, the providing unit dynamically adjusts the display order of the information according to the submission time. This makes it possible to provide an optimal display priority according to the submission time of the information. For example, the providing unit prioritizes displaying the latest information and postpones displaying older information. The providing unit can also prioritize displaying information with an upcoming submission time and postpone displaying information with a more distant deadline. Furthermore, the providing unit can dynamically adjust the display order of the information according to the submission time. This makes it possible to provide an optimal display priority according to the submission time of the information.

[0085] The providing unit can adjust the display order based on the relevance of the information when providing the information. The relevance of the information includes, for example, similarity of content, related topics, etc., but is not limited to such examples. For example, the providing unit preferentially displays information related to a user's field of interest. Furthermore, the providing unit can also preferentially display highly relevant information based on the user's past browsing history. For example, the providing unit dynamically adjusts the display order according to the relevance of the information. This makes it possible to provide an optimal display order according to the relevance of the information. For example, the providing unit preferentially displays information related to a user's field of interest. Furthermore, the providing unit can also preferentially display highly relevant information based on the user's past browsing history. Furthermore, the providing unit can dynamically adjust the display order according to the relevance of the information. This makes it possible to provide an optimal display order according to the relevance of the information.

[0086] The providing unit can customize the display content according to the user's expertise level when providing the information. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, the providing unit can provide detailed technical information if the user has expertise. Furthermore, the providing unit can provide concise and easy-to-understand information if the user is a beginner. For example, the providing unit dynamically adjusts the display content according to the user's expertise level. This makes it possible to provide optimal display content according to the user's expertise level. For example, the providing unit can provide detailed technical information if the user has expertise. Furthermore, the providing unit can provide concise and easy-to-understand information if the user is a beginner. Furthermore, the providing unit can dynamically adjust the display content according to the user's expertise level. This makes it possible to provide optimal display content according to the user's expertise level. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows the user to input information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information to identify information on services and support available in the user's area. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and summarizes the identified information and provides it to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis 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, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows the user to input information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information to identify information on services and support available in the user's area. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and summarizes the identified information and provides it to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and allows the user to input information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information to identify information on services and support available in the user's area. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and summarizes the identified information and provides it to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to input information such as family composition, annual income, chronic illnesses, health checkup results, and considerations. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information to identify information on services and support available in the user's area. The provision unit is realized, for example, by the control unit 46A of the robot 414, and summarizes the identified information and provides it to the user.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The analysis unit can optimize the analysis results by taking into account the user's lifestyle and behavioral patterns. For example, it can provide optimal analysis results based on the user's sleep patterns and meal times. It can also customize the analysis results by taking into account the user's commute time and exercise habits. This makes it possible to provide optimal analysis results based on the user's lifestyle and behavioral patterns. For example, if the user is a nocturnal person, services available at night can be displayed preferentially. Also, if the user is health-conscious, it can emphasize and provide health-related services. Furthermore, if the user travels frequently, it can suggest services available at the user's business trip destination.

[0089] The providing unit can estimate the user's emotions and dynamically change the way information is displayed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. This makes it possible to provide an optimal information display method according to the user's emotions. For example, if the user is in a hurry, a display method that focuses on the main points can be provided. Also, if the user is feeling stressed, an interface with calm colors can be provided. Furthermore, if the user is happy, a positive message can be displayed.

[0090] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently input in the past as a candidate. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. This makes it possible to provide the optimal input method based on the user's past input history. For example, it can predict and suggest information that the user will use in a specific time period based on information that the user has input in the past. It can also suggest the optimal input method based on input methods that the user has used in the past. It can also analyze the user's past input history and provide advice to improve input speed and accuracy.

[0091] The analysis unit can estimate the user's emotions and dynamically adjust the parameters of the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. Alternatively, if the user is in a hurry, a quick analysis can be performed to provide results quickly. This allows the system to provide optimal analysis results according to the user's emotions. For example, if the user is feeling stressed, a concise and easy-to-understand analysis result can be provided. Alternatively, if the user is happy, an analysis result including positive feedback can be provided. Furthermore, the system can accumulate user emotion data and analyze long-term emotional trends.

[0092] The providing unit can adjust the level of detail of the display based on the importance of the information when providing the information. For example, important information can be displayed in detail, and other information can be displayed briefly. Also, information that the user is interested in can be preferentially displayed in detail. This makes it possible to provide an optimal level of detail of display according to the importance of the information. For example, information of high legal importance can be displayed in detail, and general information can be displayed briefly. Also, information related to topics that the user is particularly interested in can be preferentially displayed. Furthermore, the level of detail of display can be dynamically adjusted according to the importance of the information.

[0093] The reception unit can estimate the user's emotions and dynamically change the priority of input items based on the estimated user emotions. For example, if the user is nervous, important input items can be displayed first, and other items can be postponed. Also, if the user is relaxed, detailed input items can be displayed sequentially. This makes it possible to provide optimal input item priorities according to the user's emotions. For example, if the user is in a hurry, only the most important input items can be displayed, allowing the user to complete input quickly. Also, if the user is feeling stressed, the input procedure can be simplified. Furthermore, user emotion data can be accumulated and long-term emotional trends can be analyzed.

[0094] The providing unit can apply different display algorithms depending on the category of information when providing the information. For example, health information can be displayed in visually easy-to-understand graphs and charts. Legal information can also be displayed in simple text. This makes it possible to provide an optimal display algorithm depending on the category of information. For example, information about services can be displayed with detailed descriptions. Education information can also be displayed visually using infographics. Furthermore, the display style and format can be dynamically adjusted depending on the category of information.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data sources. For example, it can improve the accuracy of the analysis results by referring to statistical data. It can also optimize the analysis results based on market data. This makes it possible to provide optimal analysis results based on related external data sources. For example, it can analyze the service provision status by region by referring to government statistical data. It can also propose support measures based on the user's income based on economic data. It can also improve the analysis algorithm by referring to related external data sources.

[0096] The providing unit can estimate the user's emotions and adjust the display order of information based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed preferentially, with other information postponed. Also, if the user is relaxed, detailed information can be displayed sequentially. This makes it possible to provide an optimal display order of information according to the user's emotions. For example, if the user is in a hurry, only the most important information can be displayed and the information can be provided quickly. Also, if the user is feeling stressed, information that is visually easy to understand can be displayed preferentially. Furthermore, it is possible to accumulate user emotion data and analyze long-term emotional trends.

[0097] When inputting data, the reception unit can prioritize displaying highly relevant input items taking into account the user's geographical location information. For example, if the user lives in a specific area, input items related to that area can be prioritized. Also, if the user is traveling, input items related to the travel destination can be automatically added. This makes it possible to provide optimal input items based on the user's geographical location information. For example, if the user is considering moving, input items related to the new area can be prioritized. Also, if the user is looking for services in a specific area, information related to that area can be prioritized. Furthermore, it is possible to suggest services and support measures specific to the area based on the user's geographical location information.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The receptionist inputs information such as the user's family structure, annual income, chronic illnesses and health checkup results, and considerations, such as the number of family members, ages, relationships, pre-tax income, whether bonuses are included, chronic illnesses, allergies, blood test results, electrocardiogram results, home purchases, and education funds. Step 2: The analysis unit analyzes the information entered by the reception unit and identifies information about services and support available in the user's area. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. Step 3: The providing unit summarizes and provides the information identified by the analyzing unit, based on the importance of the information and the length of the summary.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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. A reception section where users input information such as family composition, annual income, chronic illnesses, health checkup results, and other considerations; an analysis unit that analyzes the information input by the reception unit and identifies information on services and support that can be received in the area where the user lives; a providing unit that summarizes and provides the information identified by the analyzing unit. A system characterized by:

2. The providing unit Provides easy-to-understand summaries of complex information found on government websites 2. The system of claim 1.

3. The analysis unit Regularly update data to provide the latest information 2. The system of claim 1.

4. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

6. The reception unit Automatically customize your input as you type based on your current life situation and interests 2. The system of claim 1.

7. The reception unit When inputting, select the most appropriate input method depending on the user's input method 2. The system of claim 1.

8. The reception unit Estimate user emotions and dynamically change the priority of input items based on the estimated user emotions 2. The system of claim 1.

9. The reception unit As you type, it takes your geographic location into account to prioritize relevant input fields.

2. The system of claim 1.

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