system

Abstract

The system according to the embodiment aims to centrally manage information about live events and new releases and to propose effective support activities. [Solution] The system according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a communication unit. The information collection unit collects live event schedules and new release information. The analysis unit analyzes user support activity data based on the information collected by the information collection unit. The proposal unit proposes appropriate support methods based on the analysis results obtained by the analysis unit. The communication unit promotes communication between fans based on the support methods proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to centrally manage scattered live event and new release information and propose effective support activities.

[0005] The system according to the embodiment aims to centrally manage information about live events and new releases and to propose effective support activities. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a communication unit. The information collection unit collects live event schedules and new release information. The analysis unit analyzes user support activity data based on the information collected by the information collection unit. The proposal unit proposes appropriate support methods based on the analysis results obtained by the analysis unit. The communication unit promotes communication between fans based on the support methods proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage information about live events and new releases, and can propose effective support activities. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support system for supporting idols according to an embodiment of the present invention utilizes a generation AI to support users' fan activities. The support system collects information on live event schedules and new releases, analyzes users' fan activities, proposes optimal fan methods, and promotes interaction between fans. For example, the support system collects and centrally manages information from the idol's official website and social media. The support system then uses a generation AI to analyze users' past fan activities and propose optimal fan methods. Furthermore, the support system promotes interaction between fans by planning fan meetings and sharing information on social media. This allows users to centrally monitor their idol's activities, find creative ways to support their idols, and deepen their bonds with other fans. The support system effectively supports users' fan activities, ensuring they never miss a single update on their idol's activities. Furthermore, users can find creative ways to support their idols and deepen their bonds with other fans through interactions with them.

[0029] The support system for supporting idols according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a communication unit. The information collection unit collects information such as live event schedules and new release information. For example, the information collection unit collects information from the idol's official website and social media accounts and centrally manages it. The information collection unit can also collect information from related news sites and forums. The analysis unit uses a generation AI to analyze the user's support activity data based on the information collected by the information collection unit. For example, the analysis unit analyzes data on events the user has attended and merchandise purchased, and proposes optimal support methods. The proposal unit uses the generation AI to propose optimal support methods based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes specific support methods, such as sending support messages and creating fan art, based on the user's support activity data. The communication unit promotes interaction between fans based on the support methods proposed by the proposal unit. For example, the communication unit promotes interaction between fans by planning fan meetings and sharing information on social media. As a result, the support system for supporting idols according to the embodiment effectively supports users' support activities and allows them to keep track of their idol's activities. Users can also find creative ways to show their support and deepen their bonds with other fans through interactions.

[0030] The information gathering unit can collect information about live event schedules and new releases from the idol's official website and social media. For example, the information gathering unit gathers information about live event schedules and new releases from the idol's official website and social media. Official websites include the artist's official website and the label's official website. Social media includes LINE (registered trademark), Twitter (registered trademark), Facebook (registered trademark), Instagram (registered trademark), and the like. This allows users to keep up with the latest information about their idol. Some or all of the above-described processing by the information gathering unit may be performed using, or without, AI, for example. For example, the information gathering unit may input information obtained from the idol's official website and social media into the generation AI, causing the generation AI to collect and organize information.

[0031] The analysis unit can analyze data on events the user attended and merchandise purchased and suggest appropriate cheering methods. The analysis unit, for example, analyzes data on events the user attended and merchandise purchased. Events include live concerts, fan meetings, autograph sessions, etc. Merchandise includes CDs, T-shirts, posters, etc. The analysis unit uses a generation AI to analyze this data and suggest optimal cheering methods. For example, the analysis unit suggests specific cheering methods, such as sending cheering messages or creating fan art, based on the frequency of events the user attended and the types of merchandise purchased. This allows the user to know the optimal cheering method based on their own cheering activities. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's cheering activity data into the generation AI and have the generation AI suggest optimal cheering methods.

[0032] The communication unit may plan fan meetings and share information on social media. For example, the communication unit may plan fan meetings and share information on social media. Fan meetings include information such as the venue, participation conditions, and program content. Social media includes Twitter, Facebook, and Instagram. The communication unit uses these platforms to promote interaction between fans. For example, the communication unit may plan fan meetings and provide a forum for participants to interact with each other. The communication unit may also encourage fans to share information and engage in joint support activities through social media. This allows users to interact with other fans, exchange information, and engage in joint support activities. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit may input information shared on social media into a generation AI and have the generation AI execute suggestions to promote interaction between fans.

[0033] The suggestion unit can analyze the user's cheering activity data using the generation AI and suggest an appropriate cheering method. The suggestion unit, for example, uses the generation AI to analyze the user's cheering activity data. The generation AI includes technologies such as natural language generation, image generation, and voice generation. The suggestion unit uses these technologies to analyze the user's cheering activity data and suggest the optimal cheering method. For example, the suggestion unit suggests specific cheering methods, such as sending a cheering message or creating fan art, based on data on events the user attended and merchandise purchased by the user. This improves the accuracy of cheering method suggestions by using the generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's cheering activity data into the generation AI and have the generation AI suggest the optimal cheering method.

[0034] The information collection unit can collect information from related news sites and forums in addition to the official website and social media of the favorite idol. For example, the information collection unit collects information from related news sites and forums in addition to the official website and social media of the favorite idol. News sites include music news sites and entertainment news sites. Forums include fan community forums and music discussion forums. The information collection unit automatically collects news articles and forum posts related to the favorite idol from these sources and notifies the user. For example, the information collection unit automatically collects news articles related to the favorite idol and notifies the user. The information collection unit can also regularly check forum posts related to the favorite idol to collect important information. Furthermore, the information collection unit can also collect information from blogs and fan sites related to the favorite idol and provide it to the user. This allows the user to obtain information about the favorite idol from a wider range of sources. Some or all of the above-mentioned processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input information obtained from news sites and forums into the generation AI and have the generation AI collect and organize the information.

[0035] The information collection unit can analyze the user's past browsing history and select the optimal information collection method when collecting information. For example, the information collection unit analyzes the user's past browsing history and selects the optimal information collection method when collecting information. The browsing history includes previously viewed pages and search history. The information collection unit uses AI to analyze the user's past browsing history and prioritize collection of highly relevant information. For example, the information collection unit prioritizes collection of information from sites that the user has frequently visited in the past. The information collection unit can also prioritize collection of highly relevant information based on the user's past browsing history. Furthermore, the information collection unit can prioritize collection of information related to topics that the user has shown interest in in the past. This enables optimal information collection based on the user's past browsing history. Some or all of the above-described processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's past browsing history into a generation AI and cause the generation AI to select the optimal information collection method.

[0036] The information collecting unit can filter information based on the user's areas of interest when collecting information. For example, the information collecting unit filters information based on the user's areas of interest when collecting information. Areas of interest include music genres, artists, events, etc. The information collecting unit uses AI to identify the user's areas of interest and prioritize collecting highly relevant information. For example, if the user is interested in a particular genre, the information collecting unit can prioritize collecting information about that genre. Also, if the user is interested in a particular artist, the information collecting unit can prioritize collecting information about that artist. Furthermore, if the user is interested in a particular event, the information collecting unit can prioritize collecting information about that event. This makes it possible to collect highly relevant information based on the user's areas of interest. Some or all of the above-described processing in the information collecting unit may be performed using AI, or may be performed without using AI. For example, the information collecting unit can input the user's areas of interest to a generation AI and have the generation AI perform filtering.

[0037] When collecting information, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the information collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, etc. The information collection unit uses AI to identify the user's geographical location information and prioritize collecting information related to that area. For example, if the user is in a specific area, the information collection unit can prioritize collecting information related to that area. Also, if the user is traveling, the information collection unit can prioritize collecting information related to the travel destination. Furthermore, if the user is participating in a specific event, the information collection unit can prioritize collecting information related to the event. This makes it possible to collect highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input the user's geographical location information to a generation AI and cause the generation AI to collect information.

[0038] The information collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the information collection unit can analyze the user's social media activity and collect related information when collecting information. Social media activity includes the content of posts, the number of likes, the number of followers, etc. The information collection unit uses AI to analyze the user's social media activity and prioritize the collection of highly relevant information. For example, the information collection unit can collect information from accounts the user follows on social media. The information collection unit can also collect information related to posts the user has "liked" on social media. Furthermore, the information collection unit can collect information related to posts the user has shared on social media. This makes it possible to collect highly relevant information based on the user's social media activity. Some or all of the above-described processing in the information collection unit can be performed using AI, or can be performed without using AI. For example, the information collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect information.

[0039] The information collection unit can customize the information collection method by reflecting the user's past feedback when collecting information. For example, the information collection unit customizes the information collection method by reflecting the user's past feedback when collecting information. The feedback includes survey results, comments, ratings, etc. The information collection unit uses AI to analyze the user's past feedback and adjust the information collection method. For example, the information collection unit adjusts the information collection method based on feedback provided by the user in the past. The information collection unit can also prioritize information collection from information sources that the user has previously rated. Furthermore, the information collection unit can collect information by avoiding information sources that the user has previously expressed dissatisfaction with. This allows the information collection method to be optimized based on the user's past feedback. Some or all of the above-mentioned processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] The analysis unit can improve the accuracy of the analysis by taking into account the frequency and patterns of the user's cheering activities during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the frequency and patterns of the user's cheering activities during analysis. The frequency of cheering activities includes the number of events participated in per month, the number of purchases made, etc. The patterns include temporal patterns, behavioral patterns, etc. The analysis unit uses AI to analyze the frequency and patterns of the user's cheering activities and propose an optimal cheering method. For example, the analysis unit may focus on analyzing data on events that the user frequently participates in. The analysis unit can also propose an optimal cheering method based on the user's cheering activity patterns. Furthermore, the analysis unit can propose an effective cheering method by taking into account the frequency of the user's cheering activities. This improves the accuracy of the analysis based on the frequency and patterns of the user's cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's cheering activity data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0041] During analysis, the analysis unit can apply an optimal analysis algorithm by referring to the user's past cheering activity data. For example, during analysis, the analysis unit can apply an optimal analysis algorithm by referring to the user's past cheering activity data. The cheering activity data includes event participation history, purchase history, and social media activity. The analysis unit uses AI to analyze the user's past cheering activity data and select an optimal analysis algorithm. For example, the analysis unit selects an optimal analysis algorithm based on data on events the user has previously participated in. The analysis unit can also apply an optimal analysis algorithm based on data on merchandise the user has previously purchased. Furthermore, the analysis unit can select an effective analysis algorithm by referring to the user's past cheering activity data. This allows the optimal analysis algorithm to be applied based on the user's past cheering activity data. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past cheering activity data into the generation AI and have the generation AI apply the optimal analysis algorithm.

[0042] The analysis unit can apply different analysis methods depending on the category of the user's cheering activities during analysis. For example, the analysis unit applies different analysis methods depending on the category of the user's cheering activities during analysis. Categories include music genre, event type, cheering method, etc. The analysis unit uses AI to identify the category of the user's cheering activities and apply the optimal analysis method. For example, the analysis unit applies an analysis method based on participation frequency and number of participations to event participation data. The analysis unit can also apply an analysis method based on purchase amount and number of purchases to merchandise purchase data. Furthermore, the analysis unit can apply an analysis method based on the number of posts and the number of "likes" to support activity data on social media. This allows the optimal analysis method to be applied depending on the category of the user's cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's cheering activity data into the generation AI and have the generation AI apply the optimal analysis method.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the cheering activities. For example, during analysis, the analysis unit determines the priority of analysis based on the time of submission of the cheering activities. The submission time includes the submission date, submission time, etc. The analysis unit uses AI to identify the time of submission of the cheering activities and prioritize analysis. For example, the analysis unit prioritizes analysis of recent cheering activity data. The analysis unit can also prioritize analysis of cheering activity data during a specific event period. Furthermore, the analysis unit can prioritize analysis of cheering activity data performed by a user during a specific period. This makes it possible to determine the priority of analysis based on the time of submission of the cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the cheering activities into the generation AI and have the generation AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the cheering activities during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the cheering activities during analysis. Relevance includes common themes, related keywords, etc. The analysis unit uses AI to evaluate the relevance of the cheering activities and prioritize analysis. For example, the analysis unit prioritizes analysis of cheering activities that the user frequently performs. The analysis unit can also prioritize analysis of cheering activities that the user performs for a specific artist. Furthermore, the analysis unit can prioritize analysis of cheering activities that the user performs for a specific event. This makes it possible to optimize the order of analysis based on the relevance of the cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input cheering activity relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. Expertise levels include beginner, intermediate, and advanced. The analysis unit uses AI to evaluate the user's level of expertise and provide analysis results in appropriate language. For example, if the user is a beginner, the analysis unit can provide analysis results in easy-to-understand language, avoiding technical terms. If the user is an intermediate expert, the analysis unit can provide analysis results using appropriate technical terms. Furthermore, if the user is an advanced expert, the analysis unit can provide detailed analysis results using a lot of technical terms. This allows analysis results to be provided in optimal language according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input the user's expertise level data into the generation AI and have the generation AI use technical terms.

[0046] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the support method when making the suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the support method when making the suggestion. The importance includes influence, priority, etc. The suggestion unit uses AI to evaluate the importance of the support method and provide the suggestion with an appropriate level of detail. For example, the suggestion unit provides detailed suggestions for important support methods. The suggestion unit can also provide concise suggestions for general support methods. Furthermore, the suggestion unit can provide detailed suggestions for support methods in which the user is particularly interested. This allows the suggestion to be provided with an optimal level of detail based on the importance of the support method. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input importance data of the support methods to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of the cheering method when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the cheering method when making a suggestion. Categories include music genre, event type, cheering method, etc. The suggestion unit uses AI to identify the category of the cheering method and apply the optimal suggestion algorithm. For example, the suggestion unit applies a suggestion algorithm including participation methods and points to note to suggestions regarding event participation. The suggestion unit can also apply a suggestion algorithm including purchasing methods and recommended products to suggestions regarding merchandise purchases. Furthermore, the suggestion unit can apply a suggestion algorithm including post content and timing to suggestions regarding cheering activities on social media. This allows the optimal suggestion algorithm to be applied depending on the category of the cheering method. Some or all of the above-mentioned processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input category data of the cheering method into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0048] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion results include the adoption rate of the suggestion, the user's satisfaction level, etc. The suggestion unit uses AI to analyze the user's past suggestion results and provide the optimal suggestion. For example, the suggestion unit can provide similar suggestions based on suggestions that the user has previously accepted. The suggestion unit can also provide new suggestions by avoiding suggestions that the user has previously rejected. Furthermore, the suggestion unit can analyze the user's past suggestion results and provide the optimal suggestion. This can improve the accuracy of the suggestion based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0049] The suggestion unit can determine the priority of the proposals based on the submission dates of the cheering methods when making the suggestions. For example, the suggestion unit determines the priority of the proposals based on the submission dates of the cheering methods when making the suggestions. The submission dates include the submission dates and submission times. The suggestion unit uses AI to identify the submission dates of the cheering methods and prioritize the suggestions. For example, the suggestion unit prioritizes the most recent cheering methods. The suggestion unit can also prioritize the proposals of cheering methods performed during a specific event period. Furthermore, the suggestion unit can prioritize the proposals of cheering methods performed by a user during a specific period. This allows the priority of the proposals to be determined based on the submission dates of the cheering methods. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input data on the submission dates of the cheering methods into the generation AI and have the generation AI determine the priority of the proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the cheering methods when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the cheering methods when making suggestions. Relevance includes common themes, related keywords, and the like. The suggestion unit uses AI to evaluate the relevance of the cheering methods and prioritize suggestions. For example, the suggestion unit can prioritize suggesting cheering methods that the user frequently uses. The suggestion unit can also prioritize suggesting cheering methods that the user uses for a specific artist. Furthermore, the suggestion unit can prioritize suggesting cheering methods that the user uses for a specific event. This makes it possible to optimize the order of suggestions based on the relevance of the cheering methods. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input cheering method relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] The suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. Expertise levels include beginner, intermediate, and advanced. The suggestion unit uses AI to evaluate the user's level of expertise and provide the proposal using appropriate language. For example, if the user is a beginner, the suggestion unit may provide the proposal in easy-to-understand language, avoiding technical terminology. If the user is an intermediate user, the suggestion unit may provide the proposal using appropriate technical terminology. Furthermore, if the user is an advanced user, the suggestion unit may provide a detailed proposal using a lot of technical terminology. This allows the suggestion to be provided using optimal language according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0052] The communication unit can select an optimal communication method by referring to the user's past communication history when interacting with the user. For example, the communication unit can select an optimal communication method by referring to the user's past communication history when interacting with the user. The communication history includes past communication events, participant lists, etc. The communication unit uses AI to analyze the user's past communication history and provide an optimal communication method. For example, the communication unit can suggest an optimal communication method based on communication methods that the user has preferred in the past. The communication unit can also suggest a new communication method by avoiding communication methods that the user has avoided in the past. Furthermore, the communication unit can analyze the user's past communication history and provide an optimal communication method. This allows the optimal communication method to be provided based on the user's past communication history. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's past communication history data into a generation AI and cause the generation AI to select an optimal communication method.

[0053] The communication unit can customize the communication content based on the user's current areas of interest when interacting with the user. For example, the communication unit customizes the communication content based on the user's current areas of interest when interacting with the user. Areas of interest include music genres, artists, events, etc. The communication unit uses AI to identify the user's current areas of interest and provide related communication content. For example, the communication unit provides communication content based on topics in which the user is currently interested. The communication unit can also provide communication content related to events the user recently attended. Furthermore, the communication unit can provide communication content related to goods the user recently purchased. This makes it possible to provide optimal communication content based on the user's current areas of interest. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's area of ​​interest data into a generation AI and have the generation AI customize the communication content.

[0054] The communication unit can improve the communication method by reflecting user feedback during communication. For example, the communication unit improves the communication method by reflecting user feedback during communication. Feedback includes survey results, comments, ratings, etc. The communication unit uses AI to analyze the user's feedback and adjust the communication method. For example, the communication unit adjusts the communication method based on feedback previously provided by the user. The communication unit can also prioritize communication methods that the user has previously evaluated. Furthermore, the communication unit can provide new communication methods by avoiding communication methods that the user has previously expressed dissatisfaction with. This allows the communication method to be optimized based on user feedback. Some or all of the above-mentioned processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and cause the generation AI to improve the communication method.

[0055] The communication unit can select the optimal communication method by taking into account the user's geographical location information when communicating. For example, the communication unit selects the optimal communication method by taking into account the user's geographical location information when communicating. Geographical location information includes GPS data, IP address, etc. The communication unit uses AI to identify the user's geographical location information and provide communication methods related to the area. For example, if the user is in a specific area, the communication unit can provide communication methods related to the area. Also, if the user is traveling, the communication unit can provide communication methods related to the travel destination. Furthermore, if the user is participating in a specific event, the communication unit can provide communication methods related to the event. This allows the optimal communication method to be provided based on the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal communication method.

[0056] The communication unit can analyze the user's social media activity and suggest ways to communicate when interacting with the user. For example, the communication unit analyzes the user's social media activity and suggests ways to communicate when interacting with the user. Social media activity includes the content of posts, the number of likes, the number of followers, etc. The communication unit uses AI to analyze the user's social media activity and provide the optimal means of communication. For example, the communication unit can suggest ways to communicate based on the accounts the user follows on social media. The communication unit can also suggest ways to communicate based on posts the user has "liked" on social media. Furthermore, the communication unit can suggest ways to communicate based on posts the user has shared on social media. This makes it possible to provide the optimal means of communication based on the user's social media activity. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's social media activity data into a generation AI and have the generation AI suggest ways to communicate.

[0057] The communication unit can customize the communication method by reflecting the user's past feedback during communication. For example, the communication unit customizes the communication method by reflecting the user's past feedback during communication. The feedback includes survey results, comments, ratings, etc. The communication unit uses AI to analyze the user's feedback and adjust the communication method. For example, the communication unit adjusts the communication method based on feedback provided by the user in the past. The communication unit can also prioritize communication methods that the user has previously evaluated. Furthermore, the communication unit can provide new communication methods by avoiding communication methods that the user has previously expressed dissatisfaction with. This allows the communication method to be optimized based on the user's feedback. Some or all of the above-mentioned processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and have the generation AI customize the communication method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The support system for supporting idols can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's past activity data and social media posts to suggest support methods that are likely to interest the user. For example, if the user is interested in a particular music genre, it can suggest support methods related to that genre. Also, if the user is interested in a particular artist, it can suggest support methods related to that artist. Furthermore, the hobby analysis unit can suggest new support methods based on the user's interests. This allows users to enjoy support activities that match their hobbies and interests.

[0060] The support system for supporting a fan can further include a lifestyle rhythm analysis unit that takes into account the user's lifestyle rhythm. The lifestyle rhythm analysis unit analyzes the user's sleep patterns and daily activity schedule, and suggests the optimal timing for supporting activities. For example, if the user has a nocturnal lifestyle, it can suggest a support method that can be done at night. Also, if the user is busy and finds it difficult to support activities during the day, it can suggest a support method that can be done in a short amount of time and is effective. Furthermore, the lifestyle rhythm analysis unit can adjust the schedule of the support activities based on the user's lifestyle rhythm. This allows the user to continue supporting activities without straining themselves.

[0061] The support system for supporting idols can further include a location information analysis unit that utilizes the user's geographical location information. The location information analysis unit identifies the user's current location and suggests support activities related to that area. For example, if the user is in a particular city, it can provide information about events held in that city. Also, if the user is traveling, it can suggest support activities at the user's travel destination. Furthermore, the location information analysis unit can provide local support goods and event information based on the user's geographical location. This allows users to enjoy support activities tailored to their current location.

[0062] The support system for supporting idols can further include a history analysis unit that utilizes the user's past support activity data. The history analysis unit analyzes the user's past support activity data and suggests the optimal support method. For example, it can suggest similar support methods based on data on events the user has previously attended or merchandise purchased. It can also suggest new support methods based on support methods the user has previously preferred. Furthermore, the history analysis unit can suggest effective support methods based on the user's past support activity data. This allows the user to find the optimal support method based on their past support activities.

[0063] The support system for supporting artists can further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes the user's social media activity and suggests optimal ways to support the artist. For example, if the user frequently "likes" posts by a particular artist, it can suggest ways to support the artist related to that artist. Also, if the user frequently uses a particular hashtag, it can suggest ways to support the artist related to that hashtag. Furthermore, the social media analysis unit can analyze the activity of the user's followers and suggest ways to support the artist together. This allows the user to find the optimal way to support the artist based on their social media activity.

[0064] The support system for supporting idols can further include a feedback reflection unit that reflects user feedback. The feedback reflection unit collects feedback from users and uses it to improve the system. For example, the system's functions can be updated based on improvements suggested by users. New support methods can also be developed based on feedback provided by users. Furthermore, the feedback reflection unit can periodically collect feedback and reflect it in system improvements in order to improve user satisfaction. This makes it possible to provide a system that reflects user opinions.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The information gathering department collects information about live event schedules and new releases. For example, the information gathering department collects information from the idol's official website and social media, and manages it centrally. Information can also be collected from related news sites and forums. Step 2: The analysis unit uses the generation AI to analyze the user's cheering activity data based on the information collected by the information collection unit. For example, it analyzes data on events the user attended and merchandise they purchased, and suggests optimal ways to cheer. Step 3: The suggestion unit uses the generation AI to propose optimal cheering methods based on the analysis results obtained by the analysis unit. For example, based on the user's cheering activity data, the suggestion unit proposes specific cheering methods, such as sending cheering messages or creating fan art. Step 4: The Communication Department promotes interaction between fans based on the support methods proposed by the Proposal Department. For example, they plan fan meetings and share information on social media to promote interaction between fans.

[0067] (Example 2) The support system for supporting idols according to an embodiment of the present invention utilizes a generation AI to support users' fan activities. The support system collects information on live event schedules and new releases, analyzes users' fan activities, proposes optimal fan methods, and promotes interaction between fans. For example, the support system collects and centrally manages information from the idol's official website and social media. The support system then uses a generation AI to analyze users' past fan activities and propose optimal fan methods. Furthermore, the support system promotes interaction between fans by planning fan meetings and sharing information on social media. This allows users to centrally monitor their idol's activities, find creative ways to support their idols, and deepen their bonds with other fans. The support system effectively supports users' fan activities, ensuring they never miss a single update on their idol's activities. Furthermore, users can find creative ways to support their idols and deepen their bonds with other fans through interactions with them.

[0068] The support system for supporting idols according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a communication unit. The information collection unit collects information such as live event schedules and new release information. For example, the information collection unit collects information from the idol's official website and social media accounts and centrally manages it. The information collection unit can also collect information from related news sites and forums. The analysis unit uses a generation AI to analyze the user's support activity data based on the information collected by the information collection unit. For example, the analysis unit analyzes data on events the user has attended and merchandise purchased, and proposes optimal support methods. The proposal unit uses the generation AI to propose optimal support methods based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes specific support methods, such as sending support messages and creating fan art, based on the user's support activity data. The communication unit promotes interaction between fans based on the support methods proposed by the proposal unit. For example, the communication unit promotes interaction between fans by planning fan meetings and sharing information on social media. As a result, the support system for supporting idols according to the embodiment effectively supports users' support activities and allows them to keep track of their idol's activities. Users can also find creative ways to show their support and deepen their bonds with other fans through interactions.

[0069] The information gathering unit can collect information about live event schedules and new releases from the idol's official website and social media. For example, the information gathering unit gathers information about live event schedules and new releases from the idol's official website and social media. Official websites include the artist's official website and the label's official website. Social media includes Twitter, Facebook, Instagram, and the like. This allows users to keep up with the latest information about their idol. Some or all of the above-mentioned processing by the information gathering unit may be performed using AI, for example, or may be performed without using AI. For example, the information gathering unit may input information obtained from the idol's official website and social media into the generation AI, causing the generation AI to collect and organize information.

[0070] The analysis unit can analyze data on events the user attended and merchandise purchased and suggest appropriate cheering methods. The analysis unit, for example, analyzes data on events the user attended and merchandise purchased. Events include live concerts, fan meetings, autograph sessions, etc. Merchandise includes CDs, T-shirts, posters, etc. The analysis unit uses a generation AI to analyze this data and suggest optimal cheering methods. For example, the analysis unit suggests specific cheering methods, such as sending cheering messages or creating fan art, based on the frequency of events the user attended and the types of merchandise purchased. This allows the user to know the optimal cheering method based on their own cheering activities. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's cheering activity data into the generation AI and have the generation AI suggest optimal cheering methods.

[0071] The communication unit may plan fan meetings and share information on social media. For example, the communication unit may plan fan meetings and share information on social media. Fan meetings include information such as the venue, participation conditions, and program content. Social media includes Twitter, Facebook, and Instagram. The communication unit uses these platforms to promote interaction between fans. For example, the communication unit may plan fan meetings and provide a forum for participants to interact with each other. The communication unit may also encourage fans to share information and engage in joint support activities through social media. This allows users to interact with other fans, exchange information, and engage in joint support activities. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit may input information shared on social media into a generation AI and have the generation AI execute suggestions to promote interaction between fans.

[0072] The suggestion unit can analyze the user's cheering activity data using the generation AI and suggest an appropriate cheering method. The suggestion unit, for example, uses the generation AI to analyze the user's cheering activity data. The generation AI includes technologies such as natural language generation, image generation, and voice generation. The suggestion unit uses these technologies to analyze the user's cheering activity data and suggest the optimal cheering method. For example, the suggestion unit suggests specific cheering methods, such as sending a cheering message or creating fan art, based on data on events the user attended and merchandise purchased by the user. This improves the accuracy of cheering method suggestions by using the generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's cheering activity data into the generation AI and have the generation AI suggest the optimal cheering method.

[0073] The information collection unit can analyze the user's emotions and adjust the timing of information collection based on the analyzed user's emotions. The information collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user's emotions. Emotions include joy, sadness, anger, etc. The information collection unit estimates the user's emotions using an emotion analysis algorithm. For example, if the user is excited, the latest information can be collected in real time and notified immediately. Alternatively, if the user is relaxed, information can be collected periodically and notified all at once. Furthermore, if the user is stressed, the frequency of information collection can be reduced and only important information can be notified. This allows information to be collected at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the information collection unit can be performed using AI or without AI. For example, the information collection unit can input the user's emotional data into the generation AI and have the generation AI adjust the timing of information collection.

[0074] The information collection unit can collect information from related news sites and forums in addition to the official website and social media of the favorite idol. For example, the information collection unit collects information from related news sites and forums in addition to the official website and social media of the favorite idol. News sites include music news sites and entertainment news sites. Forums include fan community forums and music discussion forums. The information collection unit automatically collects news articles and forum posts related to the favorite idol from these sources and notifies the user. For example, the information collection unit automatically collects news articles related to the favorite idol and notifies the user. The information collection unit can also regularly check forum posts related to the favorite idol to collect important information. Furthermore, the information collection unit can also collect information from blogs and fan sites related to the favorite idol and provide it to the user. This allows the user to obtain information about the favorite idol from a wider range of sources. Some or all of the above-mentioned processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input information obtained from news sites and forums into the generation AI and have the generation AI collect and organize the information.

[0075] The information collection unit can analyze the user's past browsing history and select the optimal information collection method when collecting information. For example, the information collection unit analyzes the user's past browsing history and selects the optimal information collection method when collecting information. The browsing history includes previously viewed pages and search history. The information collection unit uses AI to analyze the user's past browsing history and prioritize collection of highly relevant information. For example, the information collection unit prioritizes collection of information from sites that the user has frequently visited in the past. The information collection unit can also prioritize collection of highly relevant information based on the user's past browsing history. Furthermore, the information collection unit can prioritize collection of information related to topics that the user has shown interest in in the past. This enables optimal information collection based on the user's past browsing history. Some or all of the above-described processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's past browsing history into a generation AI and cause the generation AI to select the optimal information collection method.

[0076] The information collecting unit can filter information based on the user's areas of interest when collecting information. For example, the information collecting unit filters information based on the user's areas of interest when collecting information. Areas of interest include music genres, artists, events, etc. The information collecting unit uses AI to identify the user's areas of interest and prioritize collecting highly relevant information. For example, if the user is interested in a particular genre, the information collecting unit can prioritize collecting information about that genre. Also, if the user is interested in a particular artist, the information collecting unit can prioritize collecting information about that artist. Furthermore, if the user is interested in a particular event, the information collecting unit can prioritize collecting information about that event. This makes it possible to collect highly relevant information based on the user's areas of interest. Some or all of the above-described processing in the information collecting unit may be performed using AI, or may be performed without using AI. For example, the information collecting unit can input the user's areas of interest to a generation AI and have the generation AI perform filtering.

[0077] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The information collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. Emotions include joy, sadness, anger, etc. The information collection unit estimates the user's emotions using an emotion analysis algorithm. For example, if the user is excited, the latest information can be collected preferentially. Also, if the user is relaxed, important information can be collected preferentially. Furthermore, if the user is stressed, only important information can be collected preferentially. This allows important information to be collected preferentially according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0078] When collecting information, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the information collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, etc. The information collection unit uses AI to identify the user's geographical location information and prioritize collecting information related to that area. For example, if the user is in a specific area, the information collection unit can prioritize collecting information related to that area. Also, if the user is traveling, the information collection unit can prioritize collecting information related to the travel destination. Furthermore, if the user is participating in a specific event, the information collection unit can prioritize collecting information related to the event. This makes it possible to collect highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input the user's geographical location information to a generation AI and cause the generation AI to collect information.

[0079] The information collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the information collection unit can analyze the user's social media activity and collect related information when collecting information. Social media activity includes the content of posts, the number of likes, the number of followers, etc. The information collection unit uses AI to analyze the user's social media activity and prioritize the collection of highly relevant information. For example, the information collection unit can collect information from accounts the user follows on social media. The information collection unit can also collect information related to posts the user has "liked" on social media. Furthermore, the information collection unit can collect information related to posts the user has shared on social media. This makes it possible to collect highly relevant information based on the user's social media activity. Some or all of the above-described processing in the information collection unit can be performed using AI, or can be performed without using AI. For example, the information collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect information.

[0080] The information collection unit can customize the information collection method by reflecting the user's past feedback when collecting information. For example, the information collection unit customizes the information collection method by reflecting the user's past feedback when collecting information. The feedback includes survey results, comments, ratings, etc. The information collection unit uses AI to analyze the user's past feedback and adjust the information collection method. For example, the information collection unit adjusts the information collection method based on feedback provided by the user in the past. The information collection unit can also prioritize information collection from information sources that the user has previously rated. Furthermore, the information collection unit can collect information by avoiding information sources that the user has previously expressed dissatisfaction with. This allows the information collection method to be optimized based on the user's past feedback. Some or all of the above-mentioned processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated user emotions. Emotions include joy, sadness, anger, and the like. The analysis unit estimates the user's emotions using an emotion analysis algorithm. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results can be provided using visually appealing graphs or charts. This allows the analysis results to be presented in an optimal presentation format depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using or without the generative AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0082] The analysis unit can improve the accuracy of the analysis by taking into account the frequency and patterns of the user's cheering activities during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the frequency and patterns of the user's cheering activities during analysis. The frequency of cheering activities includes the number of events participated in per month, the number of purchases made, etc. The patterns include temporal patterns, behavioral patterns, etc. The analysis unit uses AI to analyze the frequency and patterns of the user's cheering activities and propose an optimal cheering method. For example, the analysis unit may focus on analyzing data on events that the user frequently participates in. The analysis unit can also propose an optimal cheering method based on the user's cheering activity patterns. Furthermore, the analysis unit can propose an effective cheering method by taking into account the frequency of the user's cheering activities. This improves the accuracy of the analysis based on the frequency and patterns of the user's cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's cheering activity data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0083] During analysis, the analysis unit can apply an optimal analysis algorithm by referring to the user's past cheering activity data. For example, during analysis, the analysis unit can apply an optimal analysis algorithm by referring to the user's past cheering activity data. The cheering activity data includes event participation history, purchase history, and social media activity. The analysis unit uses AI to analyze the user's past cheering activity data and select an optimal analysis algorithm. For example, the analysis unit selects an optimal analysis algorithm based on data on events the user has previously participated in. The analysis unit can also apply an optimal analysis algorithm based on data on merchandise the user has previously purchased. Furthermore, the analysis unit can select an effective analysis algorithm by referring to the user's past cheering activity data. This allows the optimal analysis algorithm to be applied based on the user's past cheering activity data. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past cheering activity data into the generation AI and have the generation AI apply the optimal analysis algorithm.

[0084] The analysis unit can apply different analysis methods depending on the category of the user's cheering activities during analysis. For example, the analysis unit applies different analysis methods depending on the category of the user's cheering activities during analysis. Categories include music genre, event type, cheering method, etc. The analysis unit uses AI to identify the category of the user's cheering activities and apply the optimal analysis method. For example, the analysis unit applies an analysis method based on participation frequency and number of participations to event participation data. The analysis unit can also apply an analysis method based on purchase amount and number of purchases to merchandise purchase data. Furthermore, the analysis unit can apply an analysis method based on the number of posts and the number of "likes" to support activity data on social media. This allows the optimal analysis method to be applied depending on the category of the user's cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's cheering activity data into the generation AI and have the generation AI apply the optimal analysis method.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Emotions include joy, sadness, anger, etc. The analysis unit uses an emotion analysis algorithm to estimate the user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis result can be provided using visually appealing graphs or charts. This allows the analysis result to be provided at an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the cheering activities. For example, during analysis, the analysis unit determines the priority of analysis based on the time of submission of the cheering activities. The submission time includes the submission date, submission time, etc. The analysis unit uses AI to identify the time of submission of the cheering activities and prioritize analysis. For example, the analysis unit prioritizes analysis of recent cheering activity data. The analysis unit can also prioritize analysis of cheering activity data during a specific event period. Furthermore, the analysis unit can prioritize analysis of cheering activity data performed by a user during a specific period. This makes it possible to determine the priority of analysis based on the time of submission of the cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the cheering activities into the generation AI and have the generation AI determine the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the cheering activities during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the cheering activities during analysis. Relevance includes common themes, related keywords, etc. The analysis unit uses AI to evaluate the relevance of the cheering activities and prioritize analysis. For example, the analysis unit prioritizes analysis of cheering activities that the user frequently performs. The analysis unit can also prioritize analysis of cheering activities that the user performs for a specific artist. Furthermore, the analysis unit can prioritize analysis of cheering activities that the user performs for a specific event. This makes it possible to optimize the order of analysis based on the relevance of the cheering activities. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input cheering activity relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0088] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. Expertise levels include beginner, intermediate, and advanced. The analysis unit uses AI to evaluate the user's level of expertise and provide analysis results in appropriate language. For example, if the user is a beginner, the analysis unit can provide analysis results in easy-to-understand language, avoiding technical terms. If the user is an intermediate expert, the analysis unit can provide analysis results using appropriate technical terms. Furthermore, if the user is an advanced expert, the analysis unit can provide detailed analysis results using a lot of technical terms. This allows analysis results to be provided in optimal language according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input the user's expertise level data into the generation AI and have the generation AI use technical terms.

[0089] The suggestion unit can estimate the user's emotion and adjust the way suggestions are expressed based on the estimated user emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way suggestions are expressed based on the estimated user emotion. Emotions include joy, sadness, anger, etc. The suggestion unit estimates the user's emotion using an emotion analysis algorithm. For example, if the user is relaxed, detailed suggestions can be provided. If the user is in a hurry, concise suggestions that focus on the main points can be provided. Furthermore, if the user is excited, suggestions can be provided using visually appealing graphs or charts. This allows suggestions to be provided in the most appropriate way depending on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or can be performed without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0090] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the support method when making the suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the support method when making the suggestion. The importance includes influence, priority, etc. The suggestion unit uses AI to evaluate the importance of the support method and provide the suggestion with an appropriate level of detail. For example, the suggestion unit provides detailed suggestions for important support methods. The suggestion unit can also provide concise suggestions for general support methods. Furthermore, the suggestion unit can provide detailed suggestions for support methods in which the user is particularly interested. This allows the suggestion to be provided with an optimal level of detail based on the importance of the support method. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input importance data of the support methods to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0091] The suggestion unit can apply different suggestion algorithms depending on the category of the cheering method when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the cheering method when making a suggestion. Categories include music genre, event type, cheering method, etc. The suggestion unit uses AI to identify the category of the cheering method and apply the optimal suggestion algorithm. For example, the suggestion unit applies a suggestion algorithm including participation methods and points to note to suggestions regarding event participation. The suggestion unit can also apply a suggestion algorithm including purchasing methods and recommended products to suggestions regarding merchandise purchases. Furthermore, the suggestion unit can apply a suggestion algorithm including post content and timing to suggestions regarding cheering activities on social media. This allows the optimal suggestion algorithm to be applied depending on the category of the cheering method. Some or all of the above-mentioned processing by the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input category data of the cheering method into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0092] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion results include the adoption rate of the suggestion, the user's satisfaction level, etc. The suggestion unit uses AI to analyze the user's past suggestion results and provide the optimal suggestion. For example, the suggestion unit can provide similar suggestions based on suggestions that the user has previously accepted. The suggestion unit can also provide new suggestions by avoiding suggestions that the user has previously rejected. Furthermore, the suggestion unit can analyze the user's past suggestion results and provide the optimal suggestion. This can improve the accuracy of the suggestion based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0093] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user emotion. For example, the suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user emotion. Emotions include joy, sadness, anger, etc. The suggestion unit uses an emotion analysis algorithm to estimate the user's emotion. For example, if the user is in a hurry, 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 suggestions using visually appealing graphs or charts. This allows the suggestion unit to provide suggestions of optimal length depending on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or can be performed without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0094] The suggestion unit can determine the priority of the proposals based on the submission dates of the cheering methods when making the suggestions. For example, the suggestion unit determines the priority of the proposals based on the submission dates of the cheering methods when making the suggestions. The submission dates include the submission dates and submission times. The suggestion unit uses AI to identify the submission dates of the cheering methods and prioritize the suggestions. For example, the suggestion unit prioritizes the most recent cheering methods. The suggestion unit can also prioritize the proposals of cheering methods performed during a specific event period. Furthermore, the suggestion unit can prioritize the proposals of cheering methods performed by a user during a specific period. This allows the priority of the proposals to be determined based on the submission dates of the cheering methods. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input data on the submission dates of the cheering methods into the generation AI and have the generation AI determine the priority of the proposals.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of the cheering methods when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the cheering methods when making suggestions. Relevance includes common themes, related keywords, and the like. The suggestion unit uses AI to evaluate the relevance of the cheering methods and prioritize suggestions. For example, the suggestion unit can prioritize suggesting cheering methods that the user frequently uses. The suggestion unit can also prioritize suggesting cheering methods that the user uses for a specific artist. Furthermore, the suggestion unit can prioritize suggesting cheering methods that the user uses for a specific event. This makes it possible to optimize the order of suggestions based on the relevance of the cheering methods. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input cheering method relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0096] The suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. Expertise levels include beginner, intermediate, and advanced. The suggestion unit uses AI to evaluate the user's level of expertise and provide the proposal using appropriate language. For example, if the user is a beginner, the suggestion unit may provide the proposal in easy-to-understand language, avoiding technical terminology. If the user is an intermediate user, the suggestion unit may provide the proposal using appropriate technical terminology. Furthermore, if the user is an advanced user, the suggestion unit may provide a detailed proposal using a lot of technical terminology. This allows the suggestion to be provided using optimal language according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0097] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated user emotions. For example, the interaction unit estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. Emotions include joy, sadness, anger, etc. The interaction unit estimates the user's emotions using an emotion analysis algorithm. For example, if the user is relaxed, a casual interaction method can be provided. If the user is nervous, a formal interaction method can be provided. Furthermore, if the user is excited, a lively interaction method can be provided. This allows the optimal interaction method to be provided depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interaction unit can be performed using AI or without AI. For example, the interaction unit can input the user's emotion data into the generation AI and have the generation AI adjust the interaction method.

[0098] The communication unit can select an optimal communication method by referring to the user's past communication history when interacting with the user. For example, the communication unit can select an optimal communication method by referring to the user's past communication history when interacting with the user. The communication history includes past communication events, participant lists, etc. The communication unit uses AI to analyze the user's past communication history and provide an optimal communication method. For example, the communication unit can suggest an optimal communication method based on communication methods that the user has preferred in the past. The communication unit can also suggest a new communication method by avoiding communication methods that the user has avoided in the past. Furthermore, the communication unit can analyze the user's past communication history and provide an optimal communication method. This allows the optimal communication method to be provided based on the user's past communication history. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's past communication history data into a generation AI and cause the generation AI to select an optimal communication method.

[0099] The communication unit can customize the communication content based on the user's current areas of interest when interacting with the user. For example, the communication unit customizes the communication content based on the user's current areas of interest when interacting with the user. Areas of interest include music genres, artists, events, etc. The communication unit uses AI to identify the user's current areas of interest and provide related communication content. For example, the communication unit provides communication content based on topics in which the user is currently interested. The communication unit can also provide communication content related to events the user recently attended. Furthermore, the communication unit can provide communication content related to goods the user recently purchased. This makes it possible to provide optimal communication content based on the user's current areas of interest. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's area of ​​interest data into a generation AI and have the generation AI customize the communication content.

[0100] The communication unit can improve the communication method by reflecting user feedback during communication. For example, the communication unit improves the communication method by reflecting user feedback during communication. Feedback includes survey results, comments, ratings, etc. The communication unit uses AI to analyze the user's feedback and adjust the communication method. For example, the communication unit adjusts the communication method based on feedback previously provided by the user. The communication unit can also prioritize communication methods that the user has previously evaluated. Furthermore, the communication unit can provide new communication methods by avoiding communication methods that the user has previously expressed dissatisfaction with. This allows the communication method to be optimized based on user feedback. Some or all of the above-mentioned processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and cause the generation AI to improve the communication method.

[0101] The communication unit can estimate the user's emotions and prioritize interactions based on the estimated user emotions. The communication unit, for example, estimates the user's emotions and prioritizes interactions based on the estimated user emotions. Emotions include joy, sadness, anger, and the like. The communication unit estimates the user's emotions using an emotion analysis algorithm. For example, if the user is excited, the communication unit can immediately start interactions. Also, if the user is relaxed, the communication unit can periodically engage in interactions. Furthermore, if the user is stressed, the frequency of interactions can be reduced and only important interactions can be prioritized. This allows interactions to be provided with optimal priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the communication unit can be performed using AI, or without AI. For example, the communication unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of interactions.

[0102] The communication unit can select the optimal communication method by taking into account the user's geographical location information when communicating. For example, the communication unit selects the optimal communication method by taking into account the user's geographical location information when communicating. Geographical location information includes GPS data, IP address, etc. The communication unit uses AI to identify the user's geographical location information and provide communication methods related to the area. For example, if the user is in a specific area, the communication unit can provide communication methods related to the area. Also, if the user is traveling, the communication unit can provide communication methods related to the travel destination. Furthermore, if the user is participating in a specific event, the communication unit can provide communication methods related to the event. This allows the optimal communication method to be provided based on the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal communication method.

[0103] The communication unit can analyze the user's social media activity and suggest ways to communicate when interacting with the user. For example, the communication unit analyzes the user's social media activity and suggests ways to communicate when interacting with the user. Social media activity includes the content of posts, the number of likes, the number of followers, etc. The communication unit uses AI to analyze the user's social media activity and provide the optimal means of communication. For example, the communication unit can suggest ways to communicate based on the accounts the user follows on social media. The communication unit can also suggest ways to communicate based on posts the user has "liked" on social media. Furthermore, the communication unit can suggest ways to communicate based on posts the user has shared on social media. This makes it possible to provide the optimal means of communication based on the user's social media activity. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input the user's social media activity data into a generation AI and have the generation AI suggest ways to communicate.

[0104] The communication unit can customize the communication method by reflecting the user's past feedback during communication. For example, the communication unit customizes the communication method by reflecting the user's past feedback during communication. The feedback includes survey results, comments, ratings, etc. The communication unit uses AI to analyze the user's feedback and adjust the communication method. For example, the communication unit adjusts the communication method based on feedback provided by the user in the past. The communication unit can also prioritize communication methods that the user has previously evaluated. Furthermore, the communication unit can provide new communication methods by avoiding communication methods that the user has previously expressed dissatisfaction with. This allows the communication method to be optimized based on the user's feedback. Some or all of the above-mentioned processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and have the generation AI customize the communication method. === Hard Collateral 1-1 === Each of the multiple elements, including the information collection unit, analysis unit, suggestion unit, and communication unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information collection unit uses the communication I / F 44 of the smart device 14 to collect information from the official website and social media of the favorite idol and centrally manages the information in the database 24 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user support activity data based on the collected information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal support method based on the analysis results. The communication unit is realized, for example, by the control unit 46A of the smart device 14 and promotes interaction between fans based on the suggested support methods. === Hard Collateral 1-2 === Each of the multiple elements, including the information collection unit, analysis unit, suggestion unit, and communication unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information collection unit uses the communication I / F 44 of the smart glasses 214 to collect information from the official website and social media of the favorite idol and centrally manages the information in the database 24 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user support activity data based on the collected information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal support method based on the analysis results. The communication unit is realized, for example, by the control unit 46A of the smart glasses 214 and promotes interaction between fans based on the suggested support methods. === Hard Collateral 1-3 === Each of the multiple elements, including the information collection unit, analysis unit, suggestion unit, and communication unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the information collection unit uses the communication I / F 44 of the headset-type terminal 314 to collect information from the official website and social media of the favorite idol and centrally manages the information in the database 24 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user support activity data based on the collected information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal support method based on the analysis results. The communication unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and promotes interaction between fans based on the suggested support methods. === Hard Collateral 1-4 === Each of the multiple elements, including the information collection unit, analysis unit, suggestion unit, and communication unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit uses the communication I / F 44 of the robot 414 to collect information from the official website and social media of the favorite idol and centrally manages the information in the database 24 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user support activity data based on the collected information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal support method based on the analysis results. The communication unit is realized, for example, by the control unit 46A of the robot 414 and promotes interaction between fans based on the suggested support method.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The support system for supporting idols can further include a health management unit that monitors the user's health condition. The health management unit measures the user's heart rate and stress level and adjusts the load of the cheering activities. For example, if the user's heart rate is high, the health management unit can suggest reducing the frequency of cheering activities. Also, if the stress level is high, the health management unit can suggest a cheering method that will help the user relax. Furthermore, the health management unit can suggest appropriate rest periods based on the user's health condition. This allows the user to continue cheering activities while maintaining their health.

[0107] The support system for supporting idols can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's past activity data and social media posts to suggest support methods that are likely to interest the user. For example, if the user is interested in a particular music genre, it can suggest support methods related to that genre. Also, if the user is interested in a particular artist, it can suggest support methods related to that artist. Furthermore, the hobby analysis unit can suggest new support methods based on the user's interests. This allows users to enjoy support activities that match their hobbies and interests.

[0108] The support system for supporting a fan can further include a lifestyle rhythm analysis unit that takes into account the user's lifestyle rhythm. The lifestyle rhythm analysis unit analyzes the user's sleep patterns and daily activity schedule, and suggests the optimal timing for supporting activities. For example, if the user has a nocturnal lifestyle, it can suggest a support method that can be done at night. Also, if the user is busy and finds it difficult to support activities during the day, it can suggest a support method that can be done in a short amount of time and is effective. Furthermore, the lifestyle rhythm analysis unit can adjust the schedule of the support activities based on the user's lifestyle rhythm. This allows the user to continue supporting activities without straining themselves.

[0109] The support system for supporting idols can further include a message customization unit that estimates the user's emotions and customizes the content of the cheering message based on the estimated emotions. The message customization unit analyzes the user's emotions and generates a cheering message according to the emotions. For example, if the user is happy, a positive cheering message can be sent. Also, if the user is sad, an encouraging message can be sent. Furthermore, if the user is angry, a message to calm the user can be sent. This makes it possible to provide a cheering message that is in line with the user's emotions.

[0110] The support system for supporting idols can further include a location information analysis unit that utilizes the user's geographical location information. The location information analysis unit identifies the user's current location and suggests support activities related to that area. For example, if the user is in a particular city, it can provide information about events held in that city. Also, if the user is traveling, it can suggest support activities at the user's travel destination. Furthermore, the location information analysis unit can provide local support goods and event information based on the user's geographical location. This allows users to enjoy support activities tailored to their current location.

[0111] The support system for supporting idols can further include a history analysis unit that utilizes the user's past support activity data. The history analysis unit analyzes the user's past support activity data and suggests the optimal support method. For example, it can suggest similar support methods based on data on events the user has previously attended or merchandise purchased. It can also suggest new support methods based on support methods the user has previously preferred. Furthermore, the history analysis unit can suggest effective support methods based on the user's past support activity data. This allows the user to find the optimal support method based on their past support activities.

[0112] The support system for supporting idols can further include a goods suggestion unit that estimates the user's emotions and suggests cheering goods based on the estimated emotions. The goods suggestion unit analyzes the user's emotions and suggests cheering goods according to the emotions. For example, if the user is happy, it can suggest goods with positive messages. Also, if the user is sad, it can suggest goods with an uplifting design. Furthermore, if the user is angry, it can suggest goods that will help the user relax. This makes it possible to provide cheering goods that are in line with the user's emotions.

[0113] The support system for supporting artists can further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes the user's social media activity and suggests optimal ways to support the artist. For example, if the user frequently "likes" posts by a particular artist, it can suggest ways to support the artist related to that artist. Also, if the user frequently uses a particular hashtag, it can suggest ways to support the artist related to that hashtag. Furthermore, the social media analysis unit can analyze the activity of the user's followers and suggest ways to support the artist together. This allows the user to find the optimal way to support the artist based on their social media activity.

[0114] The support system for supporting idols can further include an event planning unit that estimates the user's emotions and plans a support event based on the estimated emotions. The event planning unit analyzes the user's emotions and plans a support event according to the emotions. For example, if the user is happy, it can plan an event with a fun atmosphere. If the user is sad, it can plan an event that includes many encouraging messages. Furthermore, if the user is angry, it can plan an event that helps the user relax. This makes it possible to provide a support event that is in tune with the user's emotions.

[0115] The support system for supporting idols can further include a feedback reflection unit that reflects user feedback. The feedback reflection unit collects feedback from users and uses it to improve the system. For example, the system's functions can be updated based on improvements suggested by users. New support methods can also be developed based on feedback provided by users. Furthermore, the feedback reflection unit can periodically collect feedback and reflect it in system improvements in order to improve user satisfaction. This makes it possible to provide a system that reflects user opinions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The information gathering department collects information about live event schedules and new releases. For example, the information gathering department collects information from the idol's official website and social media, and manages it centrally. Information can also be collected from related news sites and forums. Step 2: The analysis unit uses the generation AI to analyze the user's cheering activity data based on the information collected by the information collection unit. For example, it analyzes data on events the user attended and merchandise they purchased, and suggests optimal ways to cheer. Step 3: The suggestion unit uses the generation AI to propose optimal cheering methods based on the analysis results obtained by the analysis unit. For example, based on the user's cheering activity data, the suggestion unit proposes specific cheering methods, such as sending cheering messages or creating fan art. Step 4: The Communication Department promotes interaction between fans based on the support methods proposed by the Proposal Department. For example, they plan fan meetings and share information on social media to promote interaction between fans.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The information gathering department collects live event schedules and new release information, an analysis unit that analyzes the supporting activity data of the user based on the information collected by the information collection unit; a suggestion unit that suggests an appropriate support method based on the analysis result obtained by the analysis unit; an exchange unit that promotes exchanges between fans based on the support method proposed by the suggestion unit; A system characterized by:

2. The information collecting unit Collect information about live event schedules and new releases from the target's official website and social media.

2. The system of claim 1.

3. The analysis unit Analyzing data on events users have attended and merchandise they have purchased, and suggesting appropriate ways to support the cause 2. The system of claim 1.

4. The AC section is Organizing fan meetings and sharing information on social media 2. The system of claim 1.

5. The proposal unit Generative AI analyzes user cheering activity data and suggests appropriate cheering methods 2. The system of claim 1.

6. The information collecting unit Analyze user emotions and adjust the timing of information collection based on the analyzed user emotions.

2. The system of claim 1.

7. The information collecting unit In addition to the official website and social media of your favorite idol, gather information from related news sites and forums.

2. The system of claim 1.

8. The information collecting unit When collecting information, analyze the user's past browsing history and select the most appropriate information collection method.

2. The system of claim 1.

Images