system

Abstract

The system according to this embodiment aims to automate administrative tasks in community management and to present data-driven policy proposals. [Solution] The system according to the embodiment comprises a data acquisition unit, an analysis unit, a proposal unit, and an automation unit. The data acquisition unit acquires data. The analysis unit analyzes the data acquired by the data acquisition unit. The proposal unit presents policy proposals based on the analysis results obtained by the analysis unit. The automation unit automates administrative tasks.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the efficiency of administrative work in community operation and the presentation of policy proposals based on data are not sufficiently carried out, and there is room for improvement.​​​​​​​​​​The system according to this embodiment comprises a data acquisition unit, an analysis unit, a proposal unit, and an automation unit. The data acquisition unit acquires data. The analysis unit analyzes the data acquired by the data acquisition unit. The proposal unit presents policy proposals based on the analysis results obtained by the analysis unit. The automation unit automates administrative tasks. [Effects of the Invention]

[0007] The system according to this embodiment can automate administrative tasks in community management and present data-driven policy proposals. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication among a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The community management support system according to an embodiment of the present invention is a mechanism in which an AI agent performs administrative tasks such as creating event forms, producing content, and creating banners, which are implemented from the design and overall perspective of community management, allowing humans to dedicate time to interacting with users. Specifically, it consists of the following steps. First, the AI ​​agent analyzes data that can be obtained from the community platform and then presents proposed measures to be implemented and suggestions for enhancing the user experience. This frees community managers from administrative tasks and allows them to concentrate on direct interaction with users. First, the AI ​​agent obtains data from the community platform. This data includes user behavior history, feedback, and history of events attended. Next, the AI ​​agent analyzes this data to understand the current state of the community. For example, by analyzing user participation rates, event popularity, and feedback content, it identifies the strengths and weaknesses of the community. Next, the AI ​​agent presents proposed measures based on the analysis results. For example, if the user participation rate is low, it proposes new events to encourage participation or the creation of content tailored to the interests of participants. It also presents ideas for enhancing the user experience, such as providing personalized experiences based on the interests and needs of individual users. For example, the AI ​​agent can suggest discussions and workshops on topics of interest to specific users. Furthermore, the AI ​​agent automates administrative tasks such as creating event forms, developing content, and generating banners. This allows community managers to focus on direct interaction with users without having to spend time on these tasks. For instance, it reduces the burden of administrative work by automatically generating event announcement banners and managing participant lists. This system allows community managers to spend more time interacting with users and revitalizing the community. In addition, the suggested initiatives and experience enhancements provided by the AI ​​agent improve user satisfaction and promote community growth.For example, providing events and content tailored to users' interests can increase participation rates and strengthen community cohesion. This allows community management support systems to free community managers from administrative tasks, enabling them to focus on direct interaction with users.

[0029] The community management support system according to this embodiment comprises a data acquisition unit, an analysis unit, a proposal unit, and an automation unit. The data acquisition unit acquires data from the community platform. The data acquisition unit acquires data such as user behavior history, feedback, and participation history of events. The data acquisition unit collects user behavior history to understand user interests and concerns. The data acquisition unit can also collect feedback to understand user opinions and requests. Furthermore, the data acquisition unit can collect participation history of events to understand user participation trends. The analysis unit analyzes the data acquired by the data acquisition unit. The analysis unit analyzes user behavior history to identify user interests and concerns. The analysis unit can also analyze feedback to identify user opinions and requests. Furthermore, the analysis unit can analyze participation history of events to identify user participation trends. The proposal unit presents policy proposals based on the analysis results obtained by the analysis unit. The proposal unit proposes new events based on user interests and concerns. The proposal department can, for example, suggest improvements to content based on user feedback and requests. Furthermore, the proposal department can suggest measures to encourage user participation based on user participation trends. The automation department automates administrative tasks. For example, the automation department can automate the creation of event forms. For example, the automation department can also automate content creation. Furthermore, the automation department can also automate banner creation. As a result, the community management support system can efficiently acquire and analyze data, suggest measures, and automate administrative tasks.

[0030] The data acquisition unit acquires data from the community platform. Specifically, the data acquisition unit collects diverse data such as user behavior history, feedback, and event participation history. User behavior history includes login time, page viewing history, click history, posted content, and comment history. This allows for a detailed understanding of what kind of content users are interested in and what kind of behavior patterns they have. Feedback collection includes survey results, reviews, ratings, and opinion submissions. This allows for an understanding of user satisfaction, dissatisfaction, and requests for improvement. Event participation history includes which events users participated in, participation frequency, participation time, and activities at events. This allows for an understanding of users' event participation trends and the types of events they are interested in. The data acquisition unit collects this data in real time and stores it in a central database. Data collection is performed automatically via API and is designed to maintain data consistency and accuracy. This allows the data acquisition unit to build a foundation for comprehensively understanding user behavior and opinions on the community platform and providing this information to the analysis and proposal departments.

[0031] The analysis department analyzes the data acquired by the data acquisition department. Specifically, the analysis department analyzes user behavior history to identify user interests and preferences. For example, using machine learning algorithms, it can predict what kind of content users are interested in based on their browsing and click history. Furthermore, using natural language processing technology, it can analyze user posts and comments to extract user opinions and requests. In feedback analysis, text mining technology can be used to identify user satisfaction and dissatisfaction from survey results and reviews. When analyzing the history of events attended, clustering algorithms can be used to group user participation trends and identify which events are popular and which users are participating in which events. Based on these analysis results, the analysis department gains a detailed understanding of user behavior patterns, interests, opinions and requests, and participation trends, and provides this information to the proposal department. This allows the analysis department to provide important insights into community management and contribute to the planning and improvement of measures.

[0032] The Proposal Department presents policy proposals based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department proposes new events based on user interests and preferences. For example, it can suggest similar events or related content based on events users have previously participated in or content they have viewed. Furthermore, it can also propose content improvement proposals based on user opinions and requests. For example, it can propose improving the content of specific items or adding new features based on user feedback. It can also propose policy proposals to encourage participation based on user participation trends. For example, it can propose measures to provide benefits or incentives to encourage participation from specific user groups. The Proposal Department presents these policy proposals as concrete action plans and supports community managers in implementing them. In this way, the Proposal Department can contribute to the revitalization of the community and the improvement of user satisfaction.

[0033] The Automation Department automates administrative tasks. Specifically, it automates the creation of event forms. For example, it allows users to easily create forms using templates when creating events. It can also automate content creation. For example, it can use AI to automatically generate content based on users' interests. Furthermore, it can automate banner creation. For example, it can automatically generate event announcement banners and display them on the community platform. Through these automation functions, the Automation Department reduces the burden on community administrators and supports efficient operation. In addition, the Automation Department can continuously improve the automation process for administrative tasks based on user behavior history and feedback. For example, it can improve event form templates or optimize content generation algorithms based on user feedback. In this way, the Automation Department can contribute to the efficiency and quality improvement of community management.

[0034] The Experience Enhancement Department can propose experience enhancement suggestions for each user. For example, the Experience Enhancement Department can propose ideas for providing a personalized experience based on the user's interests and needs. For example, the Experience Enhancement Department can also suggest holding discussions or workshops on topics of interest to specific users. For example, the Experience Enhancement Department can also propose the most suitable experience enhancement suggestions for each individual user based on the user's behavior history and feedback. By providing experience enhancement suggestions for each user, user satisfaction is improved. Some or all of the above processes in the Experience Enhancement Department may be performed using AI, for example, or not using AI. For example, the Experience Enhancement Department can input the user's behavior history and feedback into a generating AI and have the generating AI generate experience enhancement suggestions.

[0035] The form creation unit can create event forms. For example, the form creation unit can automatically create event forms such as participation application forms and survey forms. For example, the form creation unit can automatically generate a participation application form simply by entering event details. For example, the form creation unit can also automatically generate a survey form simply by entering survey questions. This automates the creation of event forms, reducing the burden of administrative work. Some or all of the above-described processes in the form creation unit may be performed using AI, or not. For example, the form creation unit can input event details into a generation AI and have the generation AI execute the generation of the event form.

[0036] The data acquisition unit can acquire data such as the user's behavior history, feedback, and history of events attended. For example, the data acquisition unit can collect the user's behavior history to understand the user's interests and concerns. For example, the data acquisition unit can also collect feedback to understand the user's opinions and requests. Furthermore, the data acquisition unit can collect the history of events attended to understand the user's participation trends. By acquiring data such as the user's behavior history, feedback, and history of events attended, detailed data analysis becomes possible. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's behavior history and feedback into a generating AI and have the generating AI perform the data acquisition.

[0037] The analysis unit can analyze the acquired data and understand the current state of the community. For example, the analysis unit can analyze users' behavioral history to identify users' interests and concerns. For example, the analysis unit can analyze feedback to identify users' opinions and requests. Furthermore, the analysis unit can analyze the history of events attended to identify users' participation trends. In this way, the current state of the community can be understood by analyzing the acquired data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input users' behavioral history and feedback into a generating AI and have the generating AI perform the data analysis.

[0038] The proposal department can present policy proposals based on the analysis results. For example, the proposal department can propose new events based on user interests and preferences. For example, the proposal department can also present suggestions for improving content based on user opinions and requests. Furthermore, the proposal department can present policy proposals to encourage participation based on user participation trends. This allows for the implementation of effective policies by presenting policy proposals based on analysis results. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate policy proposals.

[0039] The automation unit can automate administrative tasks such as creating event forms, producing content, and creating banners. For example, the automation unit can automate the creation of event forms. The automation unit can also automate content production. Furthermore, the automation unit can also automate banner creation. This reduces the burden on community managers by automating administrative tasks. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the creation of event forms, content production, and banner creation into a generation AI, and have the generation AI perform the automation of administrative tasks.

[0040] The data acquisition unit can analyze the user's past behavior history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring data from pages that the user has frequently accessed in the past. The data acquisition unit can also analyze the user's past behavior patterns and set the optimal data acquisition timing. Furthermore, the data acquisition unit can acquire relevant data based on feedback provided by the user in the past. This allows the optimal data acquisition method to be selected by analyzing the user's past behavior history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past behavior history into a generating AI and have the generating AI select the data acquisition method.

[0041] The data acquisition unit can filter data based on the user's current areas of interest during data acquisition. For example, the data acquisition unit can prioritize acquiring data related to topics the user is currently interested in. The data acquisition unit can also filter out unnecessary data based on the user's current areas of interest. Furthermore, the data acquisition unit can acquire and provide data related to content that the user has shown interest in. This allows for the acquisition of highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's current areas of interest into a generating AI and have the generating AI perform data filtering.

[0042] The data acquisition unit can prioritize acquiring highly relevant data by considering the user's geographical location information during data acquisition. For example, the data acquisition unit can prioritize acquiring event information related to the area where the user is currently located. The data acquisition unit can also acquire region-specific data based on the user's geographical location information. Furthermore, if the user is on the move, the data acquisition unit can acquire real-time data based on their current location. This allows for the collection of region-specific data by acquiring highly relevant data based on the user's geographical location information. Some or all of the above-described processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform data acquisition.

[0043] The data acquisition unit can analyze the user's social media activity and acquire relevant data during data acquisition. For example, the data acquisition unit can acquire data related to content shared by the user on social media. The data acquisition unit can also analyze the user's social media activity history and acquire data of interest. Furthermore, the data acquisition unit can acquire relevant data based on the content of posts from accounts that the user follows. This allows for the efficient acquisition of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity into a generating AI and have the generating AI perform the data acquisition.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a behavioral pattern analysis algorithm to user behavior history data. For example, the analysis unit can also apply an emotion analysis algorithm to user feedback data. Furthermore, the analysis unit can apply an event participation tendency analysis algorithm to user event participation history data. By applying different analysis algorithms depending on the data category, highly accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data acquisition timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the data acquisition timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition timing into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The proposal department can adjust the level of detail in a proposal based on the importance of the measure. For example, the proposal department can provide detailed proposals for measures of high importance, and simplified proposals for measures of low importance. Furthermore, the proposal department can adjust the depth of the proposal according to the importance of the measure. This allows for efficient proposals by adjusting the level of detail according to the importance of the measure. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the measure into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the measure when making a proposal. For example, the proposal unit can apply a behavioral pattern analysis algorithm to measures based on user behavior history. For example, the proposal unit can also apply an emotion analysis algorithm to measures based on user feedback. Furthermore, the proposal unit can apply an event participation trend analysis algorithm to measures based on user event participation history. By applying different proposal algorithms depending on the category of the measure, highly accurate proposals can be obtained. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the measure into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0050] The proposal department can determine the priority of proposals based on the submission deadlines for each measure. For example, the proposal department can prioritize proposals for measures that are urgent. For example, the proposal department can postpone proposals that have ample time for submission. Furthermore, the proposal department can adjust the priority of proposals according to the submission deadlines. This allows for prioritizing proposals for urgent measures by determining the priority of proposals based on the submission deadlines. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission deadlines for measures into a generating AI and have the generating AI determine the priority of proposals.

[0051] The proposal department can adjust the order of proposals based on the relevance of the measures when making a proposal. For example, the proposal department can prioritize proposing measures that are highly relevant. For example, the proposal department can also postpone measures that are less relevant. Furthermore, the proposal department can adjust the order of proposals according to the relevance of the measures. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the relevance of the measures into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0052] The automation unit can analyze past administrative work history to select the optimal automation method during automation. For example, the automation unit can propose the optimal automation method based on the history of administrative work performed by the user in the past. The automation unit can also analyze past administrative work history to select an efficient automation method. Furthermore, the automation unit can automate frequently performed tasks based on the user's past administrative work history. This allows for the selection of the optimal automation method by analyzing past administrative work history. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input past administrative work history into a generating AI and have the generating AI select an automation method.

[0053] The automation unit can customize the automation methods based on the current status of the office work during automation. For example, the automation unit can grasp the progress of the current office work in real time and provide the optimal automation method. The automation unit can also adjust the automation methods according to the current status of the office work. Furthermore, the automation unit can analyze the current status of the office work and propose efficient automation methods. This enables efficient automation by customizing the automation methods based on the current status of the office work. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the current status of the office work into a generating AI and have the generating AI perform the customization of the automation methods.

[0054] The automation unit can select the optimal automation method during automation, taking into account the geographical distribution of office work. For example, the automation unit proposes the optimal automation method based on data related to the region where the office work is performed. The automation unit can also analyze the geographical distribution of office work and select an efficient automation method. Furthermore, the automation unit can adjust the automation means for office work based on the geographical distribution. This allows for the efficient automation of region-specific office work by considering the geographical distribution of office work. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the geographical distribution of office work into a generating AI and have the generating AI select an automation method.

[0055] The automation unit can improve the accuracy of automation by referring to relevant literature on office work during the automation process. For example, the automation unit can refer to literature related to office work and propose the optimal automation method. The automation unit can also improve the accuracy of automating office work based on relevant literature. Furthermore, the automation unit can analyze relevant literature on office work and select efficient automation methods. This allows for improved automation accuracy by referring to relevant literature on office work. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input relevant literature on office work into a generating AI and have the generating AI perform the automation accuracy improvement.

[0056] The Experience Enhancement Unit can analyze a user's past experience history to select the optimal experience enhancement method during the experience enhancement process. For example, the Experience Enhancement Unit can propose the optimal experience enhancement method based on a user's past event participation history. The Experience Enhancement Unit can also analyze a user's past experience history to select an efficient experience enhancement method. Furthermore, the Experience Enhancement Unit can enhance frequently occurring experiences based on a user's past experience history. This allows the optimal experience enhancement method to be selected by analyzing a user's past experience history. Some or all of the above processes in the Experience Enhancement Unit may be performed using AI, for example, or without AI. For example, the Experience Enhancement Unit can input a user's past experience history into a generating AI and have the generating AI select an experience enhancement method.

[0057] The experience enhancement unit can customize the means of enhancing the experience based on the user's current interests and needs. For example, the experience enhancement unit can provide experience enhancement methods related to topics the user is currently interested in. The experience enhancement unit can also filter out unnecessary experience enhancement methods based on the user's current interests and needs. Furthermore, the experience enhancement unit can also provide experience enhancement methods related to content the user has shown interest in. This allows the system to provide the optimal experience enhancement for the user by customizing the means of enhancing the experience based on the user's current interests and needs. Some or all of the above processing in the experience enhancement unit may be performed using AI, for example, or without AI. For example, the experience enhancement unit can input the user's current interests and needs into a generating AI and have the generating AI perform the customization of the experience enhancement means.

[0058] The experience enhancement unit can select the optimal experience enhancement method when enhancing the user experience, taking into account the user's geographical location information. For example, the experience enhancement unit may prioritize providing experience enhancement methods related to the user's current location. The experience enhancement unit can also provide region-specific experience enhancement methods based on the user's geographical location information. Furthermore, if the user is on the move, the experience enhancement unit can provide real-time experience enhancement methods based on their current location. This allows for the provision of region-specific experience enhancements by selecting the optimal experience enhancement method based on the user's geographical location information. Some or all of the above processing in the experience enhancement unit may be performed using AI, for example, or without AI. For example, the experience enhancement unit can input the user's geographical location information into a generating AI and have the generating AI select the experience enhancement method.

[0059] The Experience Enhancement Unit can analyze a user's social media activity and propose ways to enhance the user experience during the enhancement process. For example, the Experience Enhancement Unit can provide methods for enhancing the user experience related to content shared by the user on social media. The Experience Enhancement Unit can also analyze a user's social media activity history and provide methods for enhancing the user experience that are of interest to them. Furthermore, the Experience Enhancement Unit can provide relevant methods for enhancing the user experience based on the content posted by accounts that the user follows. This allows the unit to propose relevant methods for enhancing the user experience by analyzing the user's social media activity. Some or all of the above-described processes in the Experience Enhancement Unit may be performed using AI, for example, or without AI. For example, the Experience Enhancement Unit can input the user's social media activity into a generating AI and have the generating AI execute suggestions for methods to enhance the user experience.

[0060] The form creation unit can analyze past form creation history to select the optimal form creation method when creating a form. For example, the form creation unit can suggest the optimal form creation method based on the user's past form creation history. The form creation unit can also analyze past form creation history to select an efficient form creation method. Furthermore, the form creation unit can automatically generate frequently used forms from the user's past form creation history. This allows for the selection of the optimal form creation method by analyzing past form creation history. Some or all of the above processes in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input past form creation history into a generation AI and have the generation AI select a form creation method.

[0061] The form creation unit can customize the form creation method based on the current event status when creating a form. For example, the form creation unit can grasp the progress of the current event in real time and provide the optimal form creation method. The form creation unit can also adjust the form creation method according to the current event status. Furthermore, the form creation unit can analyze the current event status and propose an efficient form creation method. This makes efficient form creation possible by customizing the form creation method based on the current event status. Some or all of the above processing in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input the current event status into a generating AI and have the generating AI perform the customization of the form creation method.

[0062] The form creation unit can select the optimal form creation method when creating a form, taking into account the geographical distribution of events. For example, the form creation unit can propose the optimal form creation method based on data related to the region where the event takes place. For example, the form creation unit can also analyze the geographical distribution of events and select an efficient form creation method. Furthermore, the form creation unit can adjust the form creation method for events based on geographical distribution. This allows for efficient creation of region-specific forms by considering the geographical distribution of events. Some or all of the above-described processes in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input the geographical distribution of events into a generation AI and have the generation AI select a form creation method.

[0063] The form creation unit can improve the accuracy of form creation by referring to event-related literature during the form creation process. For example, the form creation unit can refer to event-related literature and propose the optimal form creation method. The form creation unit can also improve the accuracy of event form creation based on the relevant literature. Furthermore, the form creation unit can analyze event-related literature and select efficient form creation methods. In this way, the accuracy of form creation can be improved by referring to event-related literature. Some or all of the above processes in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input event-related literature into a generation AI and have the generation AI perform the improvement of form creation accuracy.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The community management support system can also include a Hobbies & Skills section to understand users' hobbies and skills. For example, the Hobbies & Skills section can suggest relevant events and content based on the hobbies and skills registered by users. It can also suggest workshops to help users discover new hobbies. Furthermore, the Hobbies & Skills section can match users with shared hobbies to promote interaction among them. This allows for the provision of personalized experiences based on users' hobbies and skills.

[0066] The community management support system can also include a learning management unit to manage users' learning progress. For example, the learning management unit monitors the progress of online courses and workshops that users participate in. It can also provide support if users encounter difficulties in their learning. Furthermore, based on the user's learning progress, the learning management unit can suggest the next content they should learn. This can improve the user's learning experience and promote growth within the community.

[0067] The community management support system can also include a purchase analysis unit that analyzes users' purchase history. For example, the purchase analysis unit analyzes the history of products and services that users have purchased in the past. It can also suggest relevant products and services based on users' purchasing trends. Furthermore, the purchase analysis unit can offer benefits and discounts based on users' purchase history. This can improve the user's purchasing experience and promote consumption activity within the community.

[0068] The community management support system can also include a skills assessment unit to evaluate users' skills. This unit can, for example, evaluate the results of workshops and training sessions that users have participated in. It can also suggest skills that users should learn next based on their skill level. Furthermore, based on the user's skill assessment results, the skill assessment unit can provide appropriate training programs. This supports user skill improvement and promotes growth within the community.

[0069] The community management support system can also include a network analysis unit that analyzes user networks. This unit can analyze, for example, the connections and interaction history between users. It can also match users with shared interests. Furthermore, the network analysis unit can visualize user networks and propose measures to promote interaction within the community. This strengthens connections between users and improves the sense of community.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data acquisition unit acquires data from the community platform. For example, it acquires data such as user behavior history, feedback, and history of events attended to understand users' interests, opinions, requests, and participation trends. Step 2: The analysis unit analyzes the data acquired by the data acquisition unit. For example, it analyzes the user's behavior history to identify their interests and concerns, analyzes feedback to identify their opinions and requests, and analyzes the history of events they have participated in to identify their participation trends. Step 3: The proposal department presents policy proposals based on the analysis results obtained by the analysis department. For example, they may propose new events based on user interests, suggest content improvements based on opinions and requests, and propose measures to encourage participation based on participation trends. Step 4: The automation department automates administrative tasks. For example, it automates the creation of event forms, content creation, and banner creation.

[0072] (Example of form 2) The community management support system according to an embodiment of the present invention is a mechanism in which an AI agent performs administrative tasks such as creating event forms, producing content, and creating banners, which are implemented from the design and overall perspective of community management, allowing humans to dedicate time to interacting with users. Specifically, it consists of the following steps. First, the AI ​​agent analyzes data that can be obtained from the community platform and then presents proposed measures to be implemented and suggestions for enhancing the user experience. This frees community managers from administrative tasks and allows them to concentrate on direct interaction with users. First, the AI ​​agent obtains data from the community platform. This data includes user behavior history, feedback, and history of events attended. Next, the AI ​​agent analyzes this data to understand the current state of the community. For example, by analyzing user participation rates, event popularity, and feedback content, it identifies the strengths and weaknesses of the community. Next, the AI ​​agent presents proposed measures based on the analysis results. For example, if the user participation rate is low, it proposes new events to encourage participation or the creation of content tailored to the interests of participants. It also presents ideas for enhancing the user experience, such as providing personalized experiences based on the interests and needs of individual users. For example, the AI ​​agent can suggest discussions and workshops on topics of interest to specific users. Furthermore, the AI ​​agent automates administrative tasks such as creating event forms, developing content, and generating banners. This allows community managers to focus on direct interaction with users without having to spend time on these tasks. For instance, it reduces the burden of administrative work by automatically generating event announcement banners and managing participant lists. This system allows community managers to spend more time interacting with users and revitalizing the community. In addition, the suggested initiatives and experience enhancements provided by the AI ​​agent improve user satisfaction and promote community growth.For example, providing events and content tailored to users' interests can increase participation rates and strengthen community cohesion. This allows community management support systems to free community managers from administrative tasks, enabling them to focus on direct interaction with users.

[0073] The community management support system according to this embodiment comprises a data acquisition unit, an analysis unit, a proposal unit, and an automation unit. The data acquisition unit acquires data from the community platform. The data acquisition unit acquires data such as user behavior history, feedback, and participation history of events. The data acquisition unit collects user behavior history to understand user interests and concerns. The data acquisition unit can also collect feedback to understand user opinions and requests. Furthermore, the data acquisition unit can collect participation history of events to understand user participation trends. The analysis unit analyzes the data acquired by the data acquisition unit. The analysis unit analyzes user behavior history to identify user interests and concerns. The analysis unit can also analyze feedback to identify user opinions and requests. Furthermore, the analysis unit can analyze participation history of events to identify user participation trends. The proposal unit presents policy proposals based on the analysis results obtained by the analysis unit. The proposal unit proposes new events based on user interests and concerns. The proposal department can, for example, suggest improvements to content based on user feedback and requests. Furthermore, the proposal department can suggest measures to encourage user participation based on user participation trends. The automation department automates administrative tasks. For example, the automation department can automate the creation of event forms. For example, the automation department can also automate content creation. Furthermore, the automation department can also automate banner creation. As a result, the community management support system can efficiently acquire and analyze data, suggest measures, and automate administrative tasks.

[0074] The data acquisition unit acquires data from the community platform. Specifically, the data acquisition unit collects diverse data such as user behavior history, feedback, and event participation history. User behavior history includes login time, page viewing history, click history, posted content, and comment history. This allows for a detailed understanding of what kind of content users are interested in and what kind of behavior patterns they have. Feedback collection includes survey results, reviews, ratings, and opinion submissions. This allows for an understanding of user satisfaction, dissatisfaction, and requests for improvement. Event participation history includes which events users participated in, participation frequency, participation time, and activities at events. This allows for an understanding of users' event participation trends and the types of events they are interested in. The data acquisition unit collects this data in real time and stores it in a central database. Data collection is performed automatically via API and is designed to maintain data consistency and accuracy. This allows the data acquisition unit to build a foundation for comprehensively understanding user behavior and opinions on the community platform and providing this information to the analysis and proposal departments.

[0075] The analysis department analyzes the data acquired by the data acquisition department. Specifically, the analysis department analyzes user behavior history to identify user interests and preferences. For example, using machine learning algorithms, it can predict what kind of content users are interested in based on their browsing and click history. Furthermore, using natural language processing technology, it can analyze user posts and comments to extract user opinions and requests. In feedback analysis, text mining technology can be used to identify user satisfaction and dissatisfaction from survey results and reviews. When analyzing the history of events attended, clustering algorithms can be used to group user participation trends and identify which events are popular and which users are participating in which events. Based on these analysis results, the analysis department gains a detailed understanding of user behavior patterns, interests, opinions and requests, and participation trends, and provides this information to the proposal department. This allows the analysis department to provide important insights into community management and contribute to the planning and improvement of measures.

[0076] The Proposal Department presents policy proposals based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department proposes new events based on user interests and preferences. For example, it can suggest similar events or related content based on events users have previously participated in or content they have viewed. Furthermore, it can also propose content improvement proposals based on user opinions and requests. For example, it can propose improving the content of specific items or adding new features based on user feedback. It can also propose policy proposals to encourage participation based on user participation trends. For example, it can propose measures to provide benefits or incentives to encourage participation from specific user groups. The Proposal Department presents these policy proposals as concrete action plans and supports community managers in implementing them. In this way, the Proposal Department can contribute to the revitalization of the community and the improvement of user satisfaction.

[0077] The Automation Department automates administrative tasks. Specifically, it automates the creation of event forms. For example, it allows users to easily create forms using templates when creating events. It can also automate content creation. For example, it can use AI to automatically generate content based on users' interests. Furthermore, it can automate banner creation. For example, it can automatically generate event announcement banners and display them on the community platform. Through these automation functions, the Automation Department reduces the burden on community administrators and supports efficient operation. In addition, the Automation Department can continuously improve the automation process for administrative tasks based on user behavior history and feedback. For example, it can improve event form templates or optimize content generation algorithms based on user feedback. In this way, the Automation Department can contribute to the efficiency and quality improvement of community management.

[0078] The Experience Enhancement Department can propose experience enhancement suggestions for each user. For example, the Experience Enhancement Department can propose ideas for providing a personalized experience based on the user's interests and needs. For example, the Experience Enhancement Department can also suggest holding discussions or workshops on topics of interest to specific users. For example, the Experience Enhancement Department can also propose the most suitable experience enhancement suggestions for each individual user based on the user's behavior history and feedback. By providing experience enhancement suggestions for each user, user satisfaction is improved. Some or all of the above processes in the Experience Enhancement Department may be performed using AI, for example, or not using AI. For example, the Experience Enhancement Department can input the user's behavior history and feedback into a generating AI and have the generating AI generate experience enhancement suggestions.

[0079] The form creation unit can create event forms. For example, the form creation unit can automatically create event forms such as participation application forms and survey forms. For example, the form creation unit can automatically generate a participation application form simply by entering event details. For example, the form creation unit can also automatically generate a survey form simply by entering survey questions. This automates the creation of event forms, reducing the burden of administrative work. Some or all of the above-described processes in the form creation unit may be performed using AI, or not. For example, the form creation unit can input event details into a generation AI and have the generation AI execute the generation of the event form.

[0080] The data acquisition unit can acquire data such as the user's behavior history, feedback, and history of events attended. For example, the data acquisition unit can collect the user's behavior history to understand the user's interests and concerns. For example, the data acquisition unit can also collect feedback to understand the user's opinions and requests. Furthermore, the data acquisition unit can collect the history of events attended to understand the user's participation trends. By acquiring data such as the user's behavior history, feedback, and history of events attended, detailed data analysis becomes possible. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's behavior history and feedback into a generating AI and have the generating AI perform the data acquisition.

[0081] The analysis unit can analyze the acquired data and understand the current state of the community. For example, the analysis unit can analyze users' behavioral history to identify users' interests and concerns. For example, the analysis unit can analyze feedback to identify users' opinions and requests. Furthermore, the analysis unit can analyze the history of events attended to identify users' participation trends. In this way, the current state of the community can be understood by analyzing the acquired data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input users' behavioral history and feedback into a generating AI and have the generating AI perform the data analysis.

[0082] The proposal department can present policy proposals based on the analysis results. For example, the proposal department can propose new events based on user interests and preferences. For example, the proposal department can also present suggestions for improving content based on user opinions and requests. Furthermore, the proposal department can present policy proposals to encourage participation based on user participation trends. This allows for the implementation of effective policies by presenting policy proposals based on analysis results. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate policy proposals.

[0083] The automation unit can automate administrative tasks such as creating event forms, producing content, and creating banners. For example, the automation unit can automate the creation of event forms. The automation unit can also automate content production. Furthermore, the automation unit can also automate banner creation. This reduces the burden on community managers by automating administrative tasks. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the creation of event forms, content production, and banner creation into a generation AI, and have the generation AI perform the automation of administrative tasks.

[0084] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can reduce the frequency of data acquisition to alleviate the user's burden. For example, if the user is relaxed, the data acquisition unit can increase the frequency of data acquisition to collect more detailed data. Furthermore, if the user is excited, the data acquisition unit can acquire data in real time and reflect it immediately. This reduces the user's burden by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data acquisition.

[0085] The data acquisition unit can analyze the user's past behavior history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring data from pages that the user has frequently accessed in the past. The data acquisition unit can also analyze the user's past behavior patterns and set the optimal data acquisition timing. Furthermore, the data acquisition unit can acquire relevant data based on feedback provided by the user in the past. This allows the optimal data acquisition method to be selected by analyzing the user's past behavior history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past behavior history into a generating AI and have the generating AI select the data acquisition method.

[0086] The data acquisition unit can filter data based on the user's current areas of interest during data acquisition. For example, the data acquisition unit can prioritize acquiring data related to topics the user is currently interested in. The data acquisition unit can also filter out unnecessary data based on the user's current areas of interest. Furthermore, the data acquisition unit can acquire and provide data related to content that the user has shown interest in. This allows for the acquisition of highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's current areas of interest into a generating AI and have the generating AI perform data filtering.

[0087] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated user emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring data of high importance. For example, if the user is relaxed, the data acquisition unit may also prioritize acquiring detailed data. Furthermore, if the user is excited, the data acquisition unit may prioritize acquiring data that is needed in real time. In this way, important data can be prioritized by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI or not using AI. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0088] The data acquisition unit can prioritize acquiring highly relevant data by considering the user's geographical location information during data acquisition. For example, the data acquisition unit can prioritize acquiring event information related to the area where the user is currently located. The data acquisition unit can also acquire region-specific data based on the user's geographical location information. Furthermore, if the user is on the move, the data acquisition unit can acquire real-time data based on their current location. This allows for the collection of region-specific data by acquiring highly relevant data based on the user's geographical location information. Some or all of the above-described processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform data acquisition.

[0089] The data acquisition unit can analyze the user's social media activity and acquire relevant data during data acquisition. For example, the data acquisition unit can acquire data related to content shared by the user on social media. The data acquisition unit can also analyze the user's social media activity history and acquire data of interest. Furthermore, the data acquisition unit can acquire relevant data based on the content of posts from accounts that the user follows. This allows for the efficient acquisition of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity into a generating AI and have the generating AI perform the data acquisition.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide interactive analysis results. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a behavioral pattern analysis algorithm to user behavior history data. For example, the analysis unit can also apply an emotion analysis algorithm to user feedback data. Furthermore, the analysis unit can apply an event participation tendency analysis algorithm to user event participation history data. By applying different analysis algorithms depending on the data category, highly accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an interactive analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an appropriate result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the data acquisition timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the data acquisition timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition timing into a generating AI and have the generating AI determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and visually easy-to-understand suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide interactive suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0097] The proposal department can adjust the level of detail in a proposal based on the importance of the measure. For example, the proposal department can provide detailed proposals for measures of high importance, and simplified proposals for measures of low importance. Furthermore, the proposal department can adjust the depth of the proposal according to the importance of the measure. This allows for efficient proposals by adjusting the level of detail according to the importance of the measure. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the measure into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0098] The proposal unit can apply different proposal algorithms depending on the category of the measure when making a proposal. For example, the proposal unit can apply a behavioral pattern analysis algorithm to measures based on user behavior history. For example, the proposal unit can also apply an emotion analysis algorithm to measures based on user feedback. Furthermore, the proposal unit can apply an event participation trend analysis algorithm to measures based on user event participation history. By applying different proposal algorithms depending on the category of the measure, highly accurate proposals can be obtained. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the measure into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide interactive suggestions. By adjusting the length of suggestions according to the user's emotions, the system can provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0100] The proposal department can determine the priority of proposals based on the submission deadlines for each measure. For example, the proposal department can prioritize proposals for measures that are urgent. For example, the proposal department can postpone proposals that have ample time for submission. Furthermore, the proposal department can adjust the priority of proposals according to the submission deadlines. This allows for prioritizing proposals for urgent measures by determining the priority of proposals based on the submission deadlines. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission deadlines for measures into a generating AI and have the generating AI determine the priority of proposals.

[0101] The proposal department can adjust the order of proposals based on the relevance of the measures when making a proposal. For example, the proposal department can prioritize proposing measures that are highly relevant. For example, the proposal department can also postpone measures that are less relevant. Furthermore, the proposal department can adjust the order of proposals according to the relevance of the measures. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the relevance of the measures into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0102] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated user emotions. For example, if the user is stressed, the automation unit can provide a simple and intuitive automation method. For example, if the user is relaxed, the automation unit can also provide detailed automation options. Furthermore, if the user is excited, the automation unit can provide an interactive automation method. This allows for an easy-to-use automation method by adjusting the automation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not using AI. For example, the automation unit can input user emotion data into the generative AI and have the generative AI adjust the automation method.

[0103] The automation unit can analyze past administrative work history to select the optimal automation method during automation. For example, the automation unit can propose the optimal automation method based on the history of administrative work performed by the user in the past. The automation unit can also analyze past administrative work history to select an efficient automation method. Furthermore, the automation unit can automate frequently performed tasks based on the user's past administrative work history. This allows for the selection of the optimal automation method by analyzing past administrative work history. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input past administrative work history into a generating AI and have the generating AI select an automation method.

[0104] The automation unit can customize the automation methods based on the current status of the office work during automation. For example, the automation unit can grasp the progress of the current office work in real time and provide the optimal automation method. The automation unit can also adjust the automation methods according to the current status of the office work. Furthermore, the automation unit can analyze the current status of the office work and propose efficient automation methods. This enables efficient automation by customizing the automation methods based on the current status of the office work. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the current status of the office work into a generating AI and have the generating AI perform the customization of the automation methods.

[0105] The automation unit can estimate the user's emotions and determine automation priorities based on the estimated emotions. For example, if the user is stressed, the automation unit will prioritize automating high-priority administrative tasks. For example, if the user is relaxed, the automation unit may also prioritize automating detailed administrative tasks. Furthermore, if the user is excited, the automation unit may prioritize automating administrative tasks that are needed in real time. This allows for the prioritization of important administrative tasks by determining automation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input user emotion data into a generative AI and have the generative AI determine the automation priorities.

[0106] The automation unit can select the optimal automation method during automation, taking into account the geographical distribution of office work. For example, the automation unit proposes the optimal automation method based on data related to the region where the office work is performed. The automation unit can also analyze the geographical distribution of office work and select an efficient automation method. Furthermore, the automation unit can adjust the automation means for office work based on the geographical distribution. This allows for the efficient automation of region-specific office work by considering the geographical distribution of office work. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the geographical distribution of office work into a generating AI and have the generating AI select an automation method.

[0107] The automation unit can improve the accuracy of automation by referring to relevant literature on office work during the automation process. For example, the automation unit can refer to literature related to office work and propose the optimal automation method. The automation unit can also improve the accuracy of automating office work based on relevant literature. Furthermore, the automation unit can analyze relevant literature on office work and select efficient automation methods. This allows for improved automation accuracy by referring to relevant literature on office work. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input relevant literature on office work into a generating AI and have the generating AI perform the automation accuracy improvement.

[0108] The experience enhancement unit can estimate the user's emotions and adjust the experience enhancement method based on the estimated user emotions. For example, if the user is stressed, the experience enhancement unit can provide a relaxing experience enhancement method. For example, if the user is relaxed, the experience enhancement unit can also provide a more detailed experience enhancement method. Furthermore, if the user is excited, the experience enhancement unit can provide an interactive experience enhancement method. This allows the system to provide the optimal experience enhancement for the user by adjusting the experience enhancement method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the experience enhancement unit may be performed using AI, for example, or without AI. For example, the experience enhancement unit can input user emotion data into a generative AI and have the generative AI adjust the experience enhancement method.

[0109] The Experience Enhancement Unit can analyze a user's past experience history to select the optimal experience enhancement method during the experience enhancement process. For example, the Experience Enhancement Unit can propose the optimal experience enhancement method based on a user's past event participation history. The Experience Enhancement Unit can also analyze a user's past experience history to select an efficient experience enhancement method. Furthermore, the Experience Enhancement Unit can enhance frequently occurring experiences based on a user's past experience history. This allows the optimal experience enhancement method to be selected by analyzing a user's past experience history. Some or all of the above processes in the Experience Enhancement Unit may be performed using AI, for example, or without AI. For example, the Experience Enhancement Unit can input a user's past experience history into a generating AI and have the generating AI select an experience enhancement method.

[0110] The experience enhancement unit can customize the means of enhancing the experience based on the user's current interests and needs. For example, the experience enhancement unit can provide experience enhancement methods related to topics the user is currently interested in. The experience enhancement unit can also filter out unnecessary experience enhancement methods based on the user's current interests and needs. Furthermore, the experience enhancement unit can also provide experience enhancement methods related to content the user has shown interest in. This allows the system to provide the optimal experience enhancement for the user by customizing the means of enhancing the experience based on the user's current interests and needs. Some or all of the above processing in the experience enhancement unit may be performed using AI, for example, or without AI. For example, the experience enhancement unit can input the user's current interests and needs into a generating AI and have the generating AI perform the customization of the experience enhancement means.

[0111] The experience enhancement unit can estimate the user's emotions and determine the priority of experience enhancements based on the estimated emotions. For example, if the user is stressed, the experience enhancement unit will prioritize providing high-priority experience enhancement methods. For example, if the user is relaxed, the experience enhancement unit may also prioritize providing detailed experience enhancement methods. Furthermore, if the user is excited, the experience enhancement unit may prioritize providing necessary experience enhancement methods in real time. This allows for the priority of important experience enhancements by determining the priority of experience enhancements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the experience enhancement unit may be performed using AI or not using AI. For example, the experience enhancement unit can input user emotion data into a generative AI and have the generative AI determine the priority of experience enhancements.

[0112] The experience enhancement unit can select the optimal experience enhancement method when enhancing the user experience, taking into account the user's geographical location information. For example, the experience enhancement unit may prioritize providing experience enhancement methods related to the user's current location. The experience enhancement unit can also provide region-specific experience enhancement methods based on the user's geographical location information. Furthermore, if the user is on the move, the experience enhancement unit can provide real-time experience enhancement methods based on their current location. This allows for the provision of region-specific experience enhancements by selecting the optimal experience enhancement method based on the user's geographical location information. Some or all of the above processing in the experience enhancement unit may be performed using AI, for example, or without AI. For example, the experience enhancement unit can input the user's geographical location information into a generating AI and have the generating AI select the experience enhancement method.

[0113] The Experience Enhancement Unit can analyze a user's social media activity and propose ways to enhance the user experience during the enhancement process. For example, the Experience Enhancement Unit can provide methods for enhancing the user experience related to content shared by the user on social media. The Experience Enhancement Unit can also analyze a user's social media activity history and provide methods for enhancing the user experience that are of interest to them. Furthermore, the Experience Enhancement Unit can provide relevant methods for enhancing the user experience based on the content posted by accounts that the user follows. This allows the unit to propose relevant methods for enhancing the user experience by analyzing the user's social media activity. Some or all of the above-described processes in the Experience Enhancement Unit may be performed using AI, for example, or without AI. For example, the Experience Enhancement Unit can input the user's social media activity into a generating AI and have the generating AI execute suggestions for methods to enhance the user experience.

[0114] The form creation unit can estimate the user's emotions and adjust the form creation method based on the estimated emotions. For example, if the user is stressed, the form creation unit can provide a simple and intuitive form creation method. If the user is relaxed, for example, the form creation unit can also provide detailed form creation options. Furthermore, if the user is excited, the form creation unit can provide an interactive form creation method. This allows for a user-friendly form creation method by adjusting the form creation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the form creation unit may be performed using AI or not. For example, the form creation unit can input user emotion data into the generative AI and have the generative AI adjust the form creation method.

[0115] The form creation unit can analyze past form creation history to select the optimal form creation method when creating a form. For example, the form creation unit can suggest the optimal form creation method based on the user's past form creation history. The form creation unit can also analyze past form creation history to select an efficient form creation method. Furthermore, the form creation unit can automatically generate frequently used forms from the user's past form creation history. This allows for the selection of the optimal form creation method by analyzing past form creation history. Some or all of the above processes in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input past form creation history into a generation AI and have the generation AI select a form creation method.

[0116] The form creation unit can customize the form creation method based on the current event status when creating a form. For example, the form creation unit can grasp the progress of the current event in real time and provide the optimal form creation method. The form creation unit can also adjust the form creation method according to the current event status. Furthermore, the form creation unit can analyze the current event status and propose an efficient form creation method. This makes efficient form creation possible by customizing the form creation method based on the current event status. Some or all of the above processing in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input the current event status into a generating AI and have the generating AI perform the customization of the form creation method.

[0117] The form creation unit can estimate the user's emotions and determine the priority of form creation based on the estimated emotions. For example, if the user is stressed, the form creation unit will prioritize creating high-priority forms. For example, if the user is relaxed, the form creation unit may also prioritize creating detailed forms. Furthermore, if the user is excited, the form creation unit may prioritize creating forms that are needed in real time. In this way, by determining the priority of form creation according to the user's emotions, important forms can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the form creation unit may be performed using AI, or not using AI. For example, the form creation unit can input user emotion data into a generative AI and have the generative AI determine the priority of form creation.

[0118] The form creation unit can select the optimal form creation method when creating a form, taking into account the geographical distribution of events. For example, the form creation unit can propose the optimal form creation method based on data related to the region where the event takes place. For example, the form creation unit can also analyze the geographical distribution of events and select an efficient form creation method. Furthermore, the form creation unit can adjust the form creation method for events based on geographical distribution. This allows for efficient creation of region-specific forms by considering the geographical distribution of events. Some or all of the above-described processes in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input the geographical distribution of events into a generation AI and have the generation AI select a form creation method.

[0119] The form creation unit can improve the accuracy of form creation by referring to event-related literature during the form creation process. For example, the form creation unit can refer to event-related literature and propose the optimal form creation method. The form creation unit can also improve the accuracy of event form creation based on the relevant literature. Furthermore, the form creation unit can analyze event-related literature and select efficient form creation methods. In this way, the accuracy of form creation can be improved by referring to event-related literature. Some or all of the above processes in the form creation unit may be performed using AI, for example, or without AI. For example, the form creation unit can input event-related literature into a generation AI and have the generation AI perform the improvement of form creation accuracy.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The community management support system can also include a health management unit that monitors users' health status. This unit can, for example, monitor users' heart rate and sleep patterns to understand their health condition. It can also suggest relaxing content if a user is experiencing stress. Furthermore, based on the user's health status, the health management unit can send notifications encouraging appropriate rest. This enables community management that considers users' health, leading to increased user satisfaction.

[0122] The community management support system can also include a Hobbies & Skills section to understand users' hobbies and skills. For example, the Hobbies & Skills section can suggest relevant events and content based on the hobbies and skills registered by users. It can also suggest workshops to help users discover new hobbies. Furthermore, the Hobbies & Skills section can match users with shared hobbies to promote interaction among them. This allows for the provision of personalized experiences based on users' hobbies and skills.

[0123] The community management support system can also include a learning management unit to manage users' learning progress. For example, the learning management unit monitors the progress of online courses and workshops that users participate in. It can also provide support if users encounter difficulties in their learning. Furthermore, based on the user's learning progress, the learning management unit can suggest the next content they should learn. This can improve the user's learning experience and promote growth within the community.

[0124] The community management support system can also include a feedback collection unit that collects user feedback in real time. For example, the feedback collection unit can collect user opinions on events and content in real time. It can also record, for example, the emotions users felt during an event. Furthermore, the feedback collection unit can analyze the collected feedback and use it to improve future events and content. This allows for the rapid incorporation of user opinions and improves the quality of the community.

[0125] The community management support system can also include a purchase analysis unit that analyzes users' purchase history. For example, the purchase analysis unit analyzes the history of products and services that users have purchased in the past. It can also suggest relevant products and services based on users' purchasing trends. Furthermore, the purchase analysis unit can offer benefits and discounts based on users' purchase history. This can improve the user's purchasing experience and promote consumption activity within the community.

[0126] The community management support system may also include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on those emotions. For example, if the user is feeling stressed, the communication adjustment unit may send messages using gentle language. If the user is relaxed, the communication adjustment unit may also provide detailed information. Furthermore, if the user is excited, the communication adjustment unit may provide interactive communication. This enables appropriate communication tailored to the user's emotions and improves user satisfaction.

[0127] The community management support system can also include a skills assessment unit to evaluate users' skills. This unit can, for example, evaluate the results of workshops and training sessions that users have participated in. It can also suggest skills that users should learn next based on their skill level. Furthermore, based on the user's skill assessment results, the skill assessment unit can provide appropriate training programs. This supports user skill improvement and promotes growth within the community.

[0128] The community management support system can also include an event adjustment unit that estimates user emotions and adjusts event content based on those emotions. For example, if a user is feeling stressed, the event adjustment unit might add relaxing activities. If a user is relaxed, it might offer a more in-depth workshop. Furthermore, if a user is excited, it might offer interactive games or discussions. This allows for event content tailored to user emotions, thereby improving user satisfaction.

[0129] The community management support system can also include a network analysis unit that analyzes user networks. This unit can analyze, for example, the connections and interaction history between users. It can also match users with shared interests. Furthermore, the network analysis unit can visualize user networks and propose measures to promote interaction within the community. This strengthens connections between users and improves the sense of community.

[0130] The community management support system may also include a content display adjustment unit that estimates the user's emotions and adjusts how content is displayed based on those emotions. For example, if the user is feeling stressed, the content display adjustment unit can display simple and visually easy-to-understand content. If the user is relaxed, for example, the content display adjustment unit can also display detailed content. Furthermore, if the user is excited, the content display adjustment unit can display interactive content. This enables the display of appropriate content according to the user's emotions, thereby improving user satisfaction.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data acquisition unit acquires data from the community platform. For example, it acquires data such as user behavior history, feedback, and history of events attended to understand users' interests, opinions, requests, and participation trends. Step 2: The analysis unit analyzes the data acquired by the data acquisition unit. For example, it analyzes the user's behavior history to identify their interests and concerns, analyzes feedback to identify their opinions and requests, and analyzes the history of events they have participated in to identify their participation trends. Step 3: The proposal department presents policy proposals based on the analysis results obtained by the analysis department. For example, they may propose new events based on user interests, suggest content improvements based on opinions and requests, and propose measures to encourage participation based on participation trends. Step 4: The automation department automates administrative tasks. For example, it automates the creation of event forms, content creation, and banner creation.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, automation unit, experience enhancement unit, form creation unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data acquisition unit acquires the user's behavior history and feedback using the camera 42 and microphone 38B of the smart device 14, and collects the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the acquired data to identify the user's interests and concerns. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and presents policy proposals based on the analysis results. The automation unit is implemented in the specific processing unit 46A of the smart device 14, and automates the creation of event forms and content production. The experience enhancement unit is implemented in the specific processing unit 290 of the data processing unit 12, and presents experience enhancement proposals for each user. The form creation unit is implemented in the specific processing unit 46A of the smart device 14, and automatically generates event forms. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the timing of data acquisition. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, automation unit, experience enhancement unit, form creation unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data acquisition unit acquires the user's behavior history and feedback using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects the data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the acquired data to identify the user's interests and concerns. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and presents policy proposals based on the analysis results. The automation unit is implemented in the control unit 46A of the smart glasses 214, for example, and automates the creation of event forms and content production. The experience enhancement unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and presents experience enhancement proposals for each user. The form creation unit is implemented in the control unit 46A of the smart glasses 214, for example, and automatically generates event forms. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the timing of data acquisition. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, automation unit, experience enhancement unit, form creation unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data acquisition unit acquires the user's behavior history and feedback using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the acquired data to identify the user's interests and concerns. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and presents policy proposals based on the analysis results. The automation unit is implemented in the control unit 46A of the headset terminal 314, for example, and automates the creation of event forms and content creation. The experience enhancement unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and presents experience enhancement proposals for each user. The form creation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and automatically generates event forms. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the timing of data acquisition. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the data acquisition unit, analysis unit, proposal unit, automation unit, experience enhancement unit, form creation unit, and emotion estimation function, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data acquisition unit acquires the user's behavior history and feedback using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects the data. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the acquired data to identify the user's interests and concerns. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and presents policy proposals based on the analysis results. The automation unit is implemented by, for example, the control unit 46A of the robot 414, and automates the creation of event forms and content production. The experience enhancement unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and presents experience enhancement proposals for each user. The form creation unit is implemented by, for example, the control unit 46A of the robot 414, and automatically generates event forms. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the timing of data acquisition. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A data acquisition unit that acquires data, An analysis unit analyzes the data acquired by the data acquisition unit, A proposal unit presents policy proposals based on the analysis results obtained by the aforementioned analysis unit, It includes an automation unit that automates administrative tasks. A system characterized by the following features. (Note 2) It includes an Experience Enhancement Department that proposes user-specific experience enhancement plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a form creation section for creating event forms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The data acquisition unit, We collect data such as user behavior history, feedback, and history of events attended. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the acquired data to understand the current state of the community. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the analysis results, we will present policy proposals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned automation unit, Automate administrative tasks such as creating event forms, developing content, and generating banners. The system described in Appendix 1, characterized by the features described herein. (Note 8) The data acquisition unit, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The data acquisition unit, Analyze the user's past behavior history and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The data acquisition unit, When acquiring data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The data acquisition unit, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The data acquisition unit, When acquiring data, the system prioritizes the acquisition of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The data acquisition unit, When acquiring data, the system analyzes the user's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the measures being considered. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the measure. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the measures. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned automation unit, It estimates the user's emotions and adjusts the automation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned automation unit, When automating tasks, the system analyzes past administrative work history to select the most suitable automation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned automation unit, When automating, customize the automation methods based on the current state of office work. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned automation unit, When automating tasks, the optimal automation method should be selected considering the geographical distribution of administrative work. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned automation unit, When automating tasks, refer to relevant literature on office work to improve the accuracy of the automation process. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned experience enhancement unit is: It estimates the user's emotions and adjusts the way the experience is enhanced based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned experience enhancement unit is: When enhancing the user experience, we analyze the user's past experience history to select the most suitable method for enhancing the experience. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned experience enhancement unit is: When enhancing the user experience, customize the means of enhancing the experience based on the user's current interests and needs. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned experience enhancement unit is: It estimates user emotions and prioritizes experience enhancements based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned experience enhancement unit is: When enhancing the user experience, the optimal method for enhancing the experience is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned experience enhancement unit is: When enhancing the user experience, we analyze users' social media activity and propose ways to improve the experience. The system described in Appendix 2, characterized by the features described herein. (Note 38) The form creation unit described above is: It estimates the user's emotions and adjusts the form creation method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The form creation unit described above is: When creating a form, the system analyzes past form creation history to select the most suitable form creation method. The system described in Appendix 3, characterized by the features described herein. (Note 40) The form creation unit described above is: When creating a form, customize the form creation method based on the current event status. The system described in Appendix 3, characterized by the features described herein. (Note 41) The form creation unit described above is: The system estimates user sentiment and prioritizes form creation based on the estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 42) The form creation unit described above is: When creating a form, select the optimal form creation method considering the geographical distribution of the event. The system described in Appendix 3, characterized by the features described herein. (Note 43) The form creation unit described above is: When creating a form, refer to relevant literature related to the event to improve the accuracy of the form. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data acquisition unit that acquires data, An analysis unit analyzes the data acquired by the data acquisition unit, A proposal unit presents policy proposals based on the analysis results obtained by the aforementioned analysis unit, It includes an automation unit that automates administrative tasks. A system characterized by the following features.

2. It includes an Experience Enhancement Department that proposes user-specific experience enhancement plans. The system according to feature 1.

3. It includes a form creation section for creating event forms. The system according to feature 1.

4. The data acquisition unit, We collect data such as user behavior history, feedback, and history of events attended. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the acquired data to understand the current state of the community. The system according to feature 1.

6. The aforementioned proposal section is, Based on the analysis results, we will present policy proposals. The system according to feature 1.

7. The aforementioned automation unit, Automate administrative tasks such as creating event forms, developing content, and generating banners. The system according to feature 1.

8. The data acquisition unit, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system according to feature 1.

9. The data acquisition unit, Analyze the user's past behavior history and select the optimal data acquisition method. The system according to feature 1.

10. The data acquisition unit, When acquiring data, filtering is performed based on the user's current areas of interest. The system according to feature 1.

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