system

Abstract

The system according to the embodiment aims to support effective self-branding in the operation of personal SNS. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a proposal unit, and an update unit. The acquisition unit acquires a user's goals and interests. The analysis unit analyzes past posting data and follower reactions based on the information acquired by the acquisition unit. The proposal unit proposes an SNS management method based on the analysis results obtained by the analysis unit. The update unit updates the management method proposed by the proposal unit according to the user's real-time situation.

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] With conventional technology, it was difficult to carry out effective self-branding when using personal social media.

[0005] The system according to the embodiment aims to support effective self-branding in the operation of personal SNS. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a proposal unit, and an update unit. The acquisition unit acquires the user's goals and interests. The analysis unit analyzes past posting data and follower reactions based on the information acquired by the acquisition unit. The proposal unit proposes an SNS operation method based on the analysis results obtained by the analysis unit. The update unit updates the operation method proposed by the proposal unit according to the user's real-time situation. [Effects of the Invention]

[0007] The system according to the embodiment can support effective self-branding in the operation of personal SNS. [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 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) A generation AI system according to an embodiment of the present invention provides advice to social media users to effectively build their own brand. In this generation AI system, users input their goals, interests, and current social media activity status, and the generation AI analyzes this information to suggest optimal social media management methods. For example, the system provides specific advice on what content to post, when to post, and what hashtags to use. Furthermore, the generation AI continuously monitors the user's social media activity and updates the advice as needed. This allows users to effectively build their brand and increase their influence on social media. First, the user inputs their goals, interests, and current social media activity status. The user inputs information such as their goals, interests, current number of followers, and posting frequency. For example, the user may input a goal such as "I want to increase my followers" or "I want to increase my influence on a specific topic." The generation AI then analyzes the input information. The generation AI analyzes the user's goals, interests, past posting data, and follower responses to suggest optimal social media management methods. For example, the system analyzes past posting data to determine what content was popular with followers and reflects this in future posts. Furthermore, the generation AI continuously monitors the user's social media activity and updates the advice as needed. For example, by providing advice tailored to specific events or trends, the system maximizes a user's influence. This mechanism allows users to effectively build their own brand and increase their influence on social media. For example, if a user wants to increase their influence on a specific topic, the generative AI will suggest posting content related to that topic and advise them to use appropriate hashtags. This will allow the user to increase their followers and influence. In this way, the generative AI system can support effective self-branding by suggesting the optimal social media usage method based on the user's goals and interests and updating it in real time.

[0029] A generative AI system according to an embodiment includes an acquisition unit, an analysis unit, a suggestion unit, and an update unit. The acquisition unit acquires a user's goals and interests and current SNS activity status. The user's goals and interests include, for example, increasing the number of followers and increasing influence on a specific topic. The acquisition unit can acquire information such as the user's entered goals and interests, the current number of followers, and posting frequency. The analysis unit analyzes past posting data and follower reactions based on the information acquired by the acquisition unit. The analysis unit can, for example, analyze what content was popular with followers from past posting data and reflect the results in future posts. The analysis unit analyzes the user's posting data and follower reactions using, for example, data mining technology or machine learning algorithms. The suggestion unit suggests an optimal SNS management method based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, suggest what kind of content should be posted, when to post, and what hashtags should be used. The suggestion unit can, for example, use the generative AI to suggest an optimal SNS management method to the user. The update unit updates the operation method proposed by the suggestion unit according to the user's real-time situation. The update unit can update the advice according to, for example, specific events or trends. The update unit, for example, continuously monitors the user's SNS activity using the generation AI and updates the advice as needed. As a result, the generation AI system according to the embodiment can support effective self-branding by proposing an optimal SNS operation method based on the user's goals and interests and updating it in real time.

[0030] The acquisition unit can acquire the user's goals and interests, and the current status of SNS activity. The acquisition unit acquires, for example, information such as the goals and interests, current number of followers, and posting frequency input by the user. The user's goals and interests include, for example, increasing the number of followers and increasing influence on a specific topic. For example, the user can input goals such as "I want to increase my followers" or "I want to increase my influence on a specific topic." The acquisition unit can also acquire the user's current SNS activity status. For example, the acquisition unit acquires information such as the user's current number of followers, posting frequency, and engagement rate. By acquiring the user's goals, interests, and current SNS activity status, the acquisition unit can provide more appropriate advice.

[0031] The analysis unit can analyze past posting data and follower reactions. For example, the analysis unit analyzes what content was popular with followers from past posting data and reflects this in future posts. The analysis unit can analyze user posting data and follower reactions, for example, using data mining technology or machine learning algorithms. For example, the analysis unit analyzes past posting data to identify what content was popular with followers. The analysis unit can also analyze follower reactions to identify what posts were effective for followers. In this way, the analysis unit can propose the most suitable SNS management method to the user by analyzing past posting data and follower reactions.

[0032] The suggestion unit can suggest whether appropriate content should be posted, whether the post should be made at an appropriate time, and whether appropriate hashtags should be used. The suggestion unit, for example, suggests what kind of content should be posted. The suggestion unit can suggest types of content, such as images, videos, and text. The suggestion unit can also suggest when to post. The suggestion unit can suggest, for example, days of the week, times of the day, or timing to coincide with a specific event. Furthermore, the suggestion unit can also suggest what hashtags should be used. The suggestion unit can suggest, for example, trending hashtags or highly relevant hashtags. In this way, the suggestion unit can maximize the effectiveness of SNS management by suggesting optimal content, posting timing, and hashtags for the user.

[0033] The update unit can update the advice in accordance with identified events or trends. The update unit can, for example, update the advice in accordance with specific events or trends. The update unit can, for example, update the advice in accordance with identified events such as seasonal events or industry events. The update unit can also update the advice in accordance with trends such as fashions on social media or news topics. In this way, the update unit can maximize the influence of the user by updating the advice in accordance with specific events or trends.

[0034] The suggestion unit can suggest an operation method according to the SNS platform used by the user. For example, the suggestion unit can suggest an operation method according to the SNS platform used by the user. For example, the suggestion unit can suggest an operation method according to SNS platforms such as X (formerly Twitter (registered trademark)), Instagram (registered trademark), and Facebook (registered trademark). The suggestion unit suggests the optimal operation method based on the characteristics and user demographics of each SNS platform. In this way, the suggestion unit can provide more effective advice by suggesting an operation method according to the SNS platform used by the user.

[0035] The acquisition unit can analyze the user's past SNS activity history and select the optimal acquisition method. The acquisition unit can, for example, analyze time periods in which the user frequently posted in the past and acquire goals and interests during those time periods. The acquisition unit can, for example, analyze the content of the user's past posts and prioritize acquisition of related goals and interests. The acquisition unit can also analyze the reactions of the user's past followers and acquire goals and interests related to content that received a good reaction. In this way, the acquisition unit can select a more effective method of acquiring goals and interests by analyzing the user's past SNS activity history.

[0036] When acquiring goals and interests, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is busy in their current living situation, the acquisition unit acquires goals and interests that can be achieved in a short period of time. For example, the acquisition unit can preferentially acquire related goals and interests based on the user's areas of interest. The acquisition unit can also acquire goals and interests that reduce stress according to the user's living situation. As a result, the acquisition unit can acquire more appropriate goals and interests by filtering based on the user's current living situation and areas of interest.

[0037] When acquiring goals and interests, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring goals and interests related to that area. For example, when the user is traveling, the acquisition unit can acquire goals and interests related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring goals and interests that can be carried out at home. In this way, the acquisition unit can acquire more relevant information by taking into account the user's geographical location information.

[0038] When acquiring goals and interests, the acquisition unit can analyze the user's social media activity and acquire related information. For example, if the user frequently posts about a specific topic, the acquisition unit can acquire goals and interests related to that topic. For example, the acquisition unit can acquire goals and interests related to topics in which the user's followers are interested. The acquisition unit can also acquire goals and interests based on the activity content of online communities in which the user participates. This allows the acquisition unit to acquire more relevant information by analyzing the user's social media activity.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted data. For example, the analysis unit performs a detailed analysis on important posted data. For example, the analysis unit can perform a concise analysis on general posted data. The analysis unit can also perform a detailed analysis on posted data that receives many responses from followers. In this way, the analysis unit can provide more effective analysis results by adjusting the level of detail of the analysis based on the importance of the posted data.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of follower reactions. For example, if there are a lot of positive reactions, the analysis unit can apply an analysis algorithm specialized for positive reactions. For example, if there are a lot of negative reactions, the analysis unit can apply an analysis algorithm specialized for negative reactions. Furthermore, if there are a lot of neutral reactions, the analysis unit can also apply an analysis algorithm specialized for neutral reactions. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of follower reactions.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the posted data. For example, the analysis unit can prioritize analysis of the most recently posted data. For example, the analysis unit can prioritize analysis of posted data related to a specific event. The analysis unit can also prioritize analysis of posted data within a period specified by the user. In this way, the analysis unit can provide more effective analysis results by determining the priority of analysis based on the time of submission of the posted data.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted data. For example, the analysis unit can prioritize analyzing posted data related to the user's goals. For example, the analysis unit can prioritize analyzing posted data that has received a large number of responses from followers. The analysis unit can also prioritize analyzing posted data related to a specific topic. In this way, the analysis unit can provide more effective analysis results by adjusting the order of analysis based on the relevance of the posted data.

[0043] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the content when making a suggestion. For example, the suggestion unit can make a detailed suggestion for important content. For example, the suggestion unit can make a concise suggestion for general content. Furthermore, the suggestion unit can also make a detailed suggestion for content that has received many responses from followers. In this way, the suggestion unit can provide more effective suggestions by adjusting the level of detail of the suggestion based on the importance of the content.

[0044] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the content. For example, the suggestion unit can apply a suggestion algorithm specialized for entertainment to entertainment content. For example, the suggestion unit can apply a suggestion algorithm specialized for education to educational content. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for business to business content. In this way, the suggestion unit can provide more appropriate suggestions by applying different suggestion algorithms depending on the category of the content.

[0045] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the submission time of the content. For example, the suggestion unit can preferentially suggest the latest content. For example, the suggestion unit can preferentially suggest content related to a specific event. The suggestion unit can also preferentially suggest content within a period specified by the user. In this way, the suggestion unit can provide more effective suggestions by determining the priority of the suggestion based on the submission time of the content.

[0046] The suggestion unit may adjust the order of suggestions based on the relevance of the content when making suggestions. For example, the suggestion unit may preferentially suggest content related to the user's goals. For example, the suggestion unit may preferentially suggest content that has received many responses from followers. The suggestion unit may also preferentially suggest content related to a specific topic. In this way, the suggestion unit may provide more effective suggestions by adjusting the order of suggestions based on the relevance of the content.

[0047] When updating, the update unit can analyze the user's past SNS activity and select the optimal update method. The update unit, for example, analyzes the time periods in which the user frequently posted in the past and updates the advice during those time periods. The update unit, for example, can analyze the content of the user's past posts and prioritize updating related advice. The update unit can also analyze the reactions of the user's past followers and update advice related to content that received a good reaction. In this way, the update unit can select a more effective advice update method by analyzing the user's past SNS activity.

[0048] The update unit can customize the advice update means based on the user's current living situation during updating. For example, if the user is busy in their current living situation, the update unit provides advice that can be implemented in a short time. For example, the update unit can provide advice to reduce stress according to the user's living situation. Furthermore, the update unit can also provide detailed advice when the user is relaxed. In this way, the update unit can provide more appropriate advice by customizing the advice update means based on the user's current living situation.

[0049] When updating, the update unit can select an optimal advice update method by taking into account the user's geographical location information. For example, when the user is in a specific area, the update unit can provide advice related to that area with priority. For example, when the user is traveling, the update unit can provide advice related to the travel destination. Furthermore, when the user is at home, the update unit can also provide advice that can be implemented at home with priority. In this way, the update unit can provide more appropriate advice by taking into account the user's geographical location information.

[0050] The update unit may analyze the user's social media activity at the time of updating and suggest a means for updating the advice. For example, if the user frequently posts about a particular topic, the update unit may provide advice related to that topic. For example, the update unit may provide advice related to topics in which the user's followers are interested. The update unit may also provide advice based on the activity of online communities in which the user participates. This allows the update unit to provide more appropriate advice by analyzing the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The acquisition unit can acquire the user's past successes and failures in addition to the user's goals and interests. For example, it can acquire the success and failure factors of the user's past campaigns and projects and suggest future SNS management methods based on that. This allows the user to utilize their past experience to manage SNS more effectively. The acquisition unit can also acquire the engagement rate and follower increase / decrease trends in the user's past SNS activities. This makes it possible to understand the user's SNS activity patterns and suggest optimal management methods.

[0053] The analysis unit can analyze data on competitors' social media activities. For example, it can analyze the content of competitors' posts and engagement rates, and suggest to the user points of differentiation from competitors. This allows the user to differentiate themselves from competitors and operate their social media more effectively. The analysis unit can also analyze trends in the user's social media activities. For example, it can analyze trending keywords and hashtags over a specific period of time, and suggest to the user content to post that matches the trends.

[0054] The suggestion unit can suggest content tailored to the target demographic based on attribute data of followers in the user's SNS activities. For example, it can suggest the optimal type of content and posting timing based on the followers' age group, gender, and interests. This allows the user to more effectively approach the target demographic. The suggestion unit can also suggest interactive content to improve the engagement rate in the user's SNS activities. For example, by suggesting interactive content such as surveys, quizzes, and live broadcasts, it is possible to increase engagement with followers.

[0055] The update unit can provide advice tailored to the seasons and events in the user's SNS activities. For example, by suggesting posting content and hashtags tailored to seasonal events such as Christmas and Halloween, the user's SNS activities can be stimulated. The update unit can also update advice based on feedback from followers regarding the user's SNS activities. For example, by analyzing comments and messages from followers and updating advice based on that, the user's SNS operations can be made more effective.

[0056] The suggestion unit can analyze the engagement patterns of followers in a user's social media activities and suggest the optimal posting timing based on that analysis. For example, it can identify the time of day when followers are most active and suggest posting during that time. It can also suggest posting timing that coincides with days of the week when followers are most engaged or specific events. This allows users to maximize engagement with their followers.

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

[0058] Step 1: The acquisition unit acquires the user's goals and interests, as well as their current social media activity status. Examples of the user's goals and interests include increasing the number of followers and increasing influence on a specific topic. The acquisition unit acquires information such as the goals and interests entered by the user, the current number of followers, and posting frequency. Step 2: The analysis unit analyzes past posting data and follower reactions based on the information acquired by the acquisition unit. The analysis unit analyzes what content was popular with followers from past posting data and can reflect this in future posts. The analysis unit uses data mining technology and machine learning algorithms to analyze user posting data and follower reactions. Step 3: The suggestion unit proposes the optimal SNS operation method based on the analysis results obtained by the analysis unit. The suggestion unit can suggest what kind of content should be posted, when it should be posted, and what hashtags should be used. The suggestion unit uses a generative AI to propose the optimal SNS operation method to the user. Step 4: The update unit updates the operation methods suggested by the suggestion unit according to the user's real-time situation. The update unit can update the advice to suit specific events or trends. The update unit uses the generation AI to continuously monitor the user's social media activity and update the advice as needed.

[0059] (Example 2) A generation AI system according to an embodiment of the present invention provides advice to social media users to effectively build their own brand. In this generation AI system, users input their goals, interests, and current social media activity status, and the generation AI analyzes this information to suggest optimal social media management methods. For example, the system provides specific advice on what content to post, when to post, and what hashtags to use. Furthermore, the generation AI continuously monitors the user's social media activity and updates the advice as needed. This allows users to effectively build their brand and increase their influence on social media. First, the user inputs their goals, interests, and current social media activity status. The user inputs information such as their goals, interests, current number of followers, and posting frequency. For example, the user may input a goal such as "I want to increase my followers" or "I want to increase my influence on a specific topic." The generation AI then analyzes the input information. The generation AI analyzes the user's goals, interests, past posting data, and follower responses to suggest optimal social media management methods. For example, the system analyzes past posting data to determine what content was popular with followers and reflects this in future posts. Furthermore, the generation AI continuously monitors the user's social media activity and updates the advice as needed. For example, by providing advice tailored to specific events or trends, the system maximizes a user's influence. This mechanism allows users to effectively build their own brand and increase their influence on social media. For example, if a user wants to increase their influence on a specific topic, the generative AI will suggest posting content related to that topic and advise them to use appropriate hashtags. This will allow the user to increase their followers and influence. In this way, the generative AI system can support effective self-branding by suggesting the optimal social media usage method based on the user's goals and interests and updating it in real time.

[0060] A generative AI system according to an embodiment includes an acquisition unit, an analysis unit, a suggestion unit, and an update unit. The acquisition unit acquires a user's goals and interests and current SNS activity status. The user's goals and interests include, for example, increasing the number of followers and increasing influence on a specific topic. The acquisition unit can acquire information such as the user's entered goals and interests, the current number of followers, and posting frequency. The analysis unit analyzes past posting data and follower reactions based on the information acquired by the acquisition unit. The analysis unit can, for example, analyze what content was popular with followers from past posting data and reflect the results in future posts. The analysis unit analyzes the user's posting data and follower reactions using, for example, data mining technology or machine learning algorithms. The suggestion unit suggests an optimal SNS management method based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, suggest what kind of content should be posted, when to post, and what hashtags should be used. The suggestion unit can, for example, use the generative AI to suggest an optimal SNS management method to the user. The update unit updates the operation method proposed by the suggestion unit according to the user's real-time situation. The update unit can update the advice according to, for example, specific events or trends. The update unit, for example, continuously monitors the user's SNS activity using the generation AI and updates the advice as needed. As a result, the generation AI system according to the embodiment can support effective self-branding by proposing an optimal SNS operation method based on the user's goals and interests and updating it in real time.

[0061] The acquisition unit can acquire the user's goals and interests, and the current status of SNS activity. The acquisition unit acquires, for example, information such as the goals and interests, current number of followers, and posting frequency input by the user. The user's goals and interests include, for example, increasing the number of followers and increasing influence on a specific topic. For example, the user can input goals such as "I want to increase my followers" or "I want to increase my influence on a specific topic." The acquisition unit can also acquire the user's current SNS activity status. For example, the acquisition unit acquires information such as the user's current number of followers, posting frequency, and engagement rate. By acquiring the user's goals, interests, and current SNS activity status, the acquisition unit can provide more appropriate advice.

[0062] The analysis unit can analyze past posting data and follower reactions. For example, the analysis unit analyzes what content was popular with followers from past posting data and reflects this in future posts. The analysis unit can analyze user posting data and follower reactions, for example, using data mining technology or machine learning algorithms. For example, the analysis unit analyzes past posting data to identify what content was popular with followers. The analysis unit can also analyze follower reactions to identify what posts were effective for followers. In this way, the analysis unit can propose the most suitable SNS management method to the user by analyzing past posting data and follower reactions.

[0063] The suggestion unit can suggest whether appropriate content should be posted, whether the post should be made at an appropriate time, and whether appropriate hashtags should be used. The suggestion unit, for example, suggests what kind of content should be posted. The suggestion unit can suggest types of content, such as images, videos, and text. The suggestion unit can also suggest when to post. The suggestion unit can suggest, for example, days of the week, times of the day, or timing to coincide with a specific event. Furthermore, the suggestion unit can also suggest what hashtags should be used. The suggestion unit can suggest, for example, trending hashtags or highly relevant hashtags. In this way, the suggestion unit can maximize the effectiveness of SNS management by suggesting optimal content, posting timing, and hashtags for the user.

[0064] The update unit can update the advice in accordance with identified events or trends. The update unit can, for example, update the advice in accordance with specific events or trends. The update unit can, for example, update the advice in accordance with identified events such as seasonal events or industry events. The update unit can also update the advice in accordance with trends such as fashions on social media or news topics. In this way, the update unit can maximize the influence of the user by updating the advice in accordance with specific events or trends.

[0065] The suggestion unit can suggest an operation method according to the SNS platform used by the user. For example, the suggestion unit can suggest an operation method according to the SNS platform used by the user. For example, the suggestion unit can suggest an operation method according to SNS platforms such as X (formerly Twitter), Instagram, and Facebook. The suggestion unit suggests the optimal operation method based on the characteristics and user demographics of each SNS platform. In this way, the suggestion unit can provide more effective advice by suggesting an operation method according to the SNS platform used by the user.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring goals and interests based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit adjusts the timing to acquire goals and interests when the user is relaxed. For example, if the user is excited, the acquisition unit can actively acquire goals and interests by utilizing the user's emotions. Furthermore, if the user is tired, the acquisition unit can adjust the timing to acquire goals and interests after the user has rested. In this way, the acquisition unit can acquire more appropriate information by adjusting the timing to acquire goals and interests according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0067] The acquisition unit can analyze the user's past SNS activity history and select the optimal acquisition method. The acquisition unit can, for example, analyze time periods in which the user frequently posted in the past and acquire goals and interests during those time periods. The acquisition unit can, for example, analyze the content of the user's past posts and prioritize acquisition of related goals and interests. The acquisition unit can also analyze the reactions of the user's past followers and acquire goals and interests related to content that received a good reaction. In this way, the acquisition unit can select a more effective method of acquiring goals and interests by analyzing the user's past SNS activity history.

[0068] When acquiring goals and interests, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is busy in their current living situation, the acquisition unit acquires goals and interests that can be achieved in a short period of time. For example, the acquisition unit can preferentially acquire related goals and interests based on the user's areas of interest. The acquisition unit can also acquire goals and interests that reduce stress according to the user's living situation. As a result, the acquisition unit can acquire more appropriate goals and interests by filtering based on the user's current living situation and areas of interest.

[0069] The acquisition unit can estimate the user's emotions and determine the priority of goals and interests to be acquired based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit can prioritize acquiring long-term goals. For example, when the user is excited, the acquisition unit can prioritize acquiring short-term goals. Furthermore, when the user is feeling stressed, the acquisition unit can also prioritize acquiring goals and interests that are useful for stress reduction. In this way, the acquisition unit can acquire more effective information by prioritizing goals and interests based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0070] When acquiring goals and interests, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring goals and interests related to that area. For example, when the user is traveling, the acquisition unit can acquire goals and interests related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring goals and interests that can be carried out at home. In this way, the acquisition unit can acquire more relevant information by taking into account the user's geographical location information.

[0071] When acquiring goals and interests, the acquisition unit can analyze the user's social media activity and acquire related information. For example, if the user frequently posts about a specific topic, the acquisition unit can acquire goals and interests related to that topic. For example, the acquisition unit can acquire goals and interests related to topics in which the user's followers are interested. The acquisition unit can also acquire goals and interests based on the activity content of online communities in which the user participates. This allows the acquisition unit to acquire more relevant information by analyzing the user's social media activity.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide visually stimulating analysis results. This allows the analysis unit to provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted data. For example, the analysis unit performs a detailed analysis on important posted data. For example, the analysis unit can perform a concise analysis on general posted data. The analysis unit can also perform a detailed analysis on posted data that receives many responses from followers. In this way, the analysis unit can provide more effective analysis results by adjusting the level of detail of the analysis based on the importance of the posted data.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of follower reactions. For example, if there are a lot of positive reactions, the analysis unit can apply an analysis algorithm specialized for positive reactions. For example, if there are a lot of negative reactions, the analysis unit can apply an analysis algorithm specialized for negative reactions. Furthermore, if there are a lot of neutral reactions, the analysis unit can also apply an analysis algorithm specialized for neutral reactions. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of follower reactions.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. This allows the analysis unit to adjust the length of the analysis based on the user's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the posted data. For example, the analysis unit can prioritize analysis of the most recently posted data. For example, the analysis unit can prioritize analysis of posted data related to a specific event. The analysis unit can also prioritize analysis of posted data within a period specified by the user. In this way, the analysis unit can provide more effective analysis results by determining the priority of analysis based on the time of submission of the posted data.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted data. For example, the analysis unit can prioritize analyzing posted data related to the user's goals. For example, the analysis unit can prioritize analyzing posted data that has received a large number of responses from followers. The analysis unit can also prioritize analyzing posted data related to a specific topic. In this way, the analysis unit can provide more effective analysis results by adjusting the order of analysis based on the relevance of the posted data.

[0078] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. For example, when the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. Furthermore, when the user is excited, the suggestion unit can also provide visually stimulating suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0079] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the content when making a suggestion. For example, the suggestion unit can make a detailed suggestion for important content. For example, the suggestion unit can make a concise suggestion for general content. Furthermore, the suggestion unit can also make a detailed suggestion for content that has received many responses from followers. In this way, the suggestion unit can provide more effective suggestions by adjusting the level of detail of the suggestion based on the importance of the content.

[0080] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the content. For example, the suggestion unit can apply a suggestion algorithm specialized for entertainment to entertainment content. For example, the suggestion unit can apply a suggestion algorithm specialized for education to educational content. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for business to business content. In this way, the suggestion unit can provide more appropriate suggestions by applying different suggestion algorithms depending on the category of the content.

[0081] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can also provide visually stimulating suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0082] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the submission time of the content. For example, the suggestion unit can preferentially suggest the latest content. For example, the suggestion unit can preferentially suggest content related to a specific event. The suggestion unit can also preferentially suggest content within a period specified by the user. In this way, the suggestion unit can provide more effective suggestions by determining the priority of the suggestion based on the submission time of the content.

[0083] The suggestion unit may adjust the order of suggestions based on the relevance of the content when making suggestions. For example, the suggestion unit may preferentially suggest content related to the user's goals. For example, the suggestion unit may preferentially suggest content that has received many responses from followers. The suggestion unit may also preferentially suggest content related to a specific topic. In this way, the suggestion unit may provide more effective suggestions by adjusting the order of suggestions based on the relevance of the content.

[0084] The update unit can estimate the user's emotions and adjust the advice update method based on the estimated user's emotions. For example, if the user is relaxed, the update unit can provide detailed advice. For example, if the user is in a hurry, the update unit can provide concise advice that focuses on the main points. Furthermore, if the user is excited, the update unit can also provide visually stimulating advice. This allows the update unit to provide more appropriate advice by adjusting the advice update method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0085] When updating, the update unit can analyze the user's past SNS activity and select the optimal update method. The update unit, for example, analyzes the time periods in which the user frequently posted in the past and updates the advice during those time periods. The update unit, for example, can analyze the content of the user's past posts and prioritize updating related advice. The update unit can also analyze the reactions of the user's past followers and update advice related to content that received a good reaction. In this way, the update unit can select a more effective advice update method by analyzing the user's past SNS activity.

[0086] The update unit can customize the advice update means based on the user's current living situation during updating. For example, if the user is busy in their current living situation, the update unit provides advice that can be implemented in a short time. For example, the update unit can provide advice to reduce stress according to the user's living situation. Furthermore, the update unit can also provide detailed advice when the user is relaxed. In this way, the update unit can provide more appropriate advice by customizing the advice update means based on the user's current living situation.

[0087] The update unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is relaxed, the update unit can prioritize providing long-term advice. For example, if the user is excited, the update unit can prioritize providing short-term advice. Furthermore, if the user is feeling stressed, the update unit can also prioritize providing advice that is useful for stress reduction. In this way, the update unit can provide more appropriate advice by determining the priority of advice based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, 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.

[0088] When updating, the update unit can select an optimal advice update method by taking into account the user's geographical location information. For example, when the user is in a specific area, the update unit can provide advice related to that area with priority. For example, when the user is traveling, the update unit can provide advice related to the travel destination. Furthermore, when the user is at home, the update unit can also provide advice that can be implemented at home with priority. In this way, the update unit can provide more appropriate advice by taking into account the user's geographical location information.

[0089] The update unit may analyze the user's social media activity at the time of updating and suggest a means for updating the advice. For example, if the user frequently posts about a particular topic, the update unit may provide advice related to that topic. For example, the update unit may provide advice related to topics in which the user's followers are interested. The update unit may also provide advice based on the activity of online communities in which the user participates. This allows the update unit to provide more appropriate advice by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, suggestion unit, and update 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 acquisition unit is realized by the control unit 46A of the smart device 14 and acquires information such as the goals and interests entered by the user, the current number of followers, and posting frequency. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past posting data and follower reactions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal SNS operation method. The update unit is realized, for example, by the control unit 46A of the smart device 14 and updates advice according to the user's real-time situation. === Hard Collateral 1-2 === Each of the multiple elements including the above-described acquisition unit, analysis unit, suggestion unit, and update unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires information such as the goals and interests entered by the user, the current number of followers, and posting frequency. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past posting data and follower reactions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal SNS operation method. The update unit is realized, for example, by the control unit 46A of the smart glasses 214 and updates advice according to the user's real-time situation. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, suggestion unit, and update unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset type terminal 314 and acquires information such as the goals and interests entered by the user, the current number of followers, and posting frequency. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past posting data and follower reactions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal SNS operation method. The update unit is realized, for example, by the control unit 46A of the headset type terminal 314 and updates advice according to the user's real-time situation. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, suggestion unit, and update unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 and acquires information such as the goals and interests entered by the user, the current number of followers, and posting frequency. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past posting data and follower reactions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal SNS operation method. The update unit is realized, for example, by the control unit 46A of the robot 414 and updates advice according to the user's real-time situation.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The acquisition unit can acquire the user's past successes and failures in addition to the user's goals and interests. For example, it can acquire the success and failure factors of the user's past campaigns and projects and suggest future SNS management methods based on that. This allows the user to utilize their past experience to manage SNS more effectively. The acquisition unit can also acquire the engagement rate and follower increase / decrease trends in the user's past SNS activities. This makes it possible to understand the user's SNS activity patterns and suggest optimal management methods.

[0092] The analysis unit can analyze data on competitors' social media activities. For example, it can analyze the content of competitors' posts and engagement rates, and suggest to the user points of differentiation from competitors. This allows the user to differentiate themselves from competitors and operate their social media more effectively. The analysis unit can also analyze trends in the user's social media activities. For example, it can analyze trending keywords and hashtags over a specific period of time, and suggest to the user content to post that matches the trends.

[0093] The suggestion unit can suggest content tailored to the target demographic based on attribute data of followers in the user's SNS activities. For example, it can suggest the optimal type of content and posting timing based on the followers' age group, gender, and interests. This allows the user to more effectively approach the target demographic. The suggestion unit can also suggest interactive content to improve the engagement rate in the user's SNS activities. For example, by suggesting interactive content such as surveys, quizzes, and live broadcasts, it is possible to increase engagement with followers.

[0094] The update unit can provide advice tailored to the seasons and events in the user's SNS activities. For example, by suggesting posting content and hashtags tailored to seasonal events such as Christmas and Halloween, the user's SNS activities can be stimulated. The update unit can also update advice based on feedback from followers regarding the user's SNS activities. For example, by analyzing comments and messages from followers and updating advice based on that, the user's SNS operations can be made more effective.

[0095] The acquisition unit can estimate the user's emotions and customize the method for acquiring goals and interests based on the estimated user emotions. For example, if the user has positive emotions, the acquisition unit can adjust the acquisition to acquire challenging goals and interests. Also, if the user has negative emotions, the acquisition unit can acquire relaxing goals and interests. In this way, the acquisition unit can provide more appropriate information by customizing the method for acquiring goals and interests according to the user's emotions.

[0096] The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can adjust the timing to provide analysis results at a time when the user is relaxed. Also, if the user is excited, the analysis unit can take advantage of the user's emotions and proactively provide analysis results. In this way, the analysis unit can provide more effective analysis results by adjusting the timing of analysis according to the user's emotions.

[0097] The suggestion unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user's emotions. 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, visually stimulating suggestions can be provided. In this way, the suggestion unit can provide more appropriate suggestions by customizing the content of the suggestions based on the user's emotions.

[0098] The update unit can estimate the user's emotions and adjust the advice update frequency based on the estimated user's emotions. For example, if the user is relaxed, the advice can be updated more frequently. Also, if the user is feeling stressed, the advice update frequency can be reduced. In this way, the update unit can provide more appropriate advice by adjusting the advice update frequency according to the user's emotions.

[0099] The acquisition unit can estimate the user's emotions and determine the priority of goals and interests to be acquired based on the estimated user's emotions. For example, if the user is relaxed, long-term goals can be prioritized for acquisition. Also, if the user is excited, short-term goals can be prioritized for acquisition. Furthermore, if the user is feeling stressed, goals and interests that are useful for stress reduction can be prioritized for acquisition. In this way, the acquisition unit can acquire more effective information by determining the priority of goals and interests based on the user's emotions.

[0100] The suggestion unit can analyze the engagement patterns of followers in a user's social media activities and suggest the optimal posting timing based on that analysis. For example, it can identify the time of day when followers are most active and suggest posting during that time. It can also suggest posting timing that coincides with days of the week when followers are most engaged or specific events. This allows users to maximize engagement with their followers.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The acquisition unit acquires the user's goals and interests, as well as their current social media activity status. Examples of the user's goals and interests include increasing the number of followers and increasing influence on a specific topic. The acquisition unit acquires information such as the goals and interests entered by the user, the current number of followers, and posting frequency. Step 2: The analysis unit analyzes past posting data and follower reactions based on the information acquired by the acquisition unit. The analysis unit analyzes what content was popular with followers from past posting data and can reflect this in future posts. The analysis unit uses data mining technology and machine learning algorithms to analyze user posting data and follower reactions. Step 3: The suggestion unit proposes the optimal SNS operation method based on the analysis results obtained by the analysis unit. The suggestion unit can suggest what kind of content should be posted, when it should be posted, and what hashtags should be used. The suggestion unit uses a generative AI to propose the optimal SNS operation method to the user. Step 4: The update unit updates the operation methods suggested by the suggestion unit according to the user's real-time situation. The update unit can update the advice to suit specific events or trends. The update unit uses the generation AI to continuously monitor the user's social media activity and update the advice as needed.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires a user's goals and interests; an analysis unit that analyzes past posting data and reactions of followers based on the information acquired by the acquisition unit; a proposal unit that proposes an SNS operation method based on the analysis result obtained by the analysis unit; an update unit that updates the operation method proposed by the proposal unit in accordance with a real-time situation of the user. A system characterized by:

2. The acquisition unit Obtaining users' goals, interests, and current social media activity 2. The system of claim 1.

3. The analysis unit Analyze past posting data and follower reactions 2. The system of claim 1.

4. The proposal unit Suggestions on the right content to post, the right time to post, and the right hashtags to use 2. The system of claim 1.

5. The update unit Update advice in line with identified events and trends 2. The system of claim 1.

6. The proposal unit Propose operation methods according to the SNS platform used by the user 2. The system of claim 1.

7. The acquisition unit Estimate the user's emotions and adjust the timing of acquiring goals and interests based on the estimated user emotions.

2. The system of claim 1.

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

Images