system

Abstract

The system according to the embodiment aims to automate the operation of SNS and to communicate with users efficiently. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an emotional response unit, a monitoring unit, and a language support unit. The collection unit collects user profile information. The analysis unit analyzes the information collected by the collection unit and generates individually optimized responses. The emotional response unit analyzes the user's tweets and tone and generates emotional responses tailored to the individual. The monitoring unit performs real-time monitoring and responds immediately to important inquiries. The language support unit supports multiple languages.

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 efficiently communicate with users when operating SNS, which was a major challenge especially for companies facing labor shortages.

[0005] The system according to the embodiment aims to automate the operation of SNS and to communicate with users efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an emotional response unit, a monitoring unit, and a language support unit. The collection unit collects user profile information. The analysis unit analyzes the information collected by the collection unit and generates individually optimized responses. The emotional response unit analyzes the user's tweets and tone and generates emotional responses tailored to the individual. The monitoring unit performs real-time monitoring and responds immediately to important inquiries. The language support unit supports multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment automates the operation of SNS and enables efficient communication with users. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An SNS management automation system according to an embodiment of the present invention reduces the man-hours required for SNS-related tasks. This SNS management automation system identifies users' profile information and generates individually optimized responses. Next, it analyzes the user's tweets and tone to generate emotional responses tailored to the individual. Furthermore, it performs real-time monitoring and immediately responds to important inquiries. Finally, by supporting multiple languages, it can accommodate global users. For example, the SNS management automation system collects and analyzes information such as the user's age, gender, and interests. This allows it to generate individually optimized responses. For example, it may respond in a casual tone to younger users and in a polite tone to older users. Next, the SNS management automation system analyzes the content and wording of the user's tweets and generates emotional responses tailored to the user. This makes communication with users more natural and friendly. Furthermore, the SNS management automation system monitors users' comments and inquiries on the SNS in real time and immediately responds to important inquiries. This improves user satisfaction. Finally, the SNS management automation system can generate responses in various languages, such as English, Japanese, and Chinese. This allows it to accommodate global users. This system allows SNS managers and management agencies to significantly reduce the amount of work required for SNS-related tasks. It also facilitates smooth communication with users, contributing to improving a company's brand image. As a result, the SNS management automation system reduces the amount of work required for SNS-related tasks and enables smooth communication with users.

[0029] An SNS operation automation system according to an embodiment includes a collection unit, an analysis unit, an emotional response unit, a monitoring unit, and a language support unit. The collection unit collects user profile information. For example, the collection unit can collect information such as the user's age, gender, and interests. The collection unit can collect information for generating an appropriate response based on the user's age, for example. The collection unit can also collect information for adjusting the tone of the response based on the user's gender. Furthermore, the collection unit can collect related information based on the user's interests. The analysis unit analyzes the information collected by the collection unit and generates an individually optimized response. For example, the analysis unit can respond to younger users in a casual tone and older users in a polite tone based on the collected information. For example, the analysis unit can generate a response in a tone appropriate to the user's age group based on the collected information. The analysis unit can also generate a response in a tone appropriate to the user's gender based on the collected information. Furthermore, the analysis unit can generate a response in a tone appropriate to the user's interests based on the collected information. The emotional response unit analyzes a user's tweets and tone and generates an emotional response tailored to the user. For example, the emotional response unit analyzes the content and wording of a user's tweets and generates an emotional response tailored to the user. The emotional response unit generates an emotional response based on, for example, the content of a user's tweets. The emotional response unit can also generate an emotional response based on the user's wording. Furthermore, the emotional response unit can also generate an emotional response based on the user's tone. The monitoring unit performs monitoring in real time and responds immediately to important inquiries. For example, the monitoring unit monitors user comments and inquiries on the SNS in real time and responds immediately to important inquiries. For example, the monitoring unit monitors user comments on the SNS in real time. The monitoring unit can also monitor user inquiries on the SNS in real time. Furthermore, the monitoring unit can respond immediately to important inquiries on the SNS. The language support unit supports multiple languages. For example, the language support unit generates responses in various languages, such as English, Japanese, and Chinese. The language support unit generates a response in English, for example.The language support unit can also generate responses in Japanese. Furthermore, the language support unit can also generate responses in Chinese. As a result, the SNS operation automation system according to the embodiment can reduce the number of steps required for SNS-related work and facilitate smooth communication with users.

[0030] The collection unit can collect information such as the user's age, gender, and interests. The collection unit, for example, collects information for generating an appropriate response based on the user's age. For example, the collection unit receives the user's age as input and collects information corresponding to the age. The collection unit can also collect information for adjusting the tone of the response based on the user's gender. For example, the collection unit receives the user's gender as input and collects information corresponding to the gender. Furthermore, the collection unit can collect related information based on the user's interests. For example, the collection unit receives the user's interests as input and collects information corresponding to the interests. In this way, by collecting information such as the user's age, gender, and interests, an individually optimized response can be generated. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information such as the user's age, gender, and interests into AI, and the AI ​​can analyze the information to determine the information to collect.

[0031] Based on the collected information, the analysis unit can respond in a casual tone to younger users and in a polite tone to older users. The analysis unit, for example, generates a response in a tone appropriate to the user's age group based on the collected information. For example, the analysis unit responds in a casual tone to younger users. For example, the analysis unit generates a response using friendly language to younger users. The analysis unit can also respond in a polite tone to older users. For example, the analysis unit generates a response using honorific language to older users. Furthermore, the analysis unit can also generate a response in a tone appropriate to the user's gender based on the collected information. For example, the analysis unit responds in a casual tone to male users. The analysis unit can also respond in a polite tone to female users. This enables more appropriate communication by responding in a tone appropriate to the user's age group. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into an AI, which can then analyze the information to determine the tone of the response.

[0032] The emotional response unit can analyze the content of a user's tweets and wording, and generate an emotional response tailored to the user. The emotional response unit generates an emotional response based on, for example, the content of a user's tweets. For example, the emotional response unit analyzes the content of a user's tweets and generates an emotional response based on the content. For example, the emotional response unit receives the content of a user's tweets as input, and generates an emotional response based on the content. The emotional response unit can also generate an emotional response based on the user's wording. For example, the emotional response unit analyzes the user's wording and generates an emotional response based on the wording. For example, the emotional response unit receives the user's wording as input, and generates an emotional response based on the wording. Furthermore, the emotional response unit can also generate an emotional response based on the user's tone. For example, the emotional response unit analyzes the user's tone and generates an emotional response based on the tone. For example, the emotional response unit receives the user's tone as input, and generates an emotional response based on the tone. This allows for more natural and friendly communication by generating emotional responses based on the content and wording of a user's tweets. Some or all of the above-described processing in the emotional response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotional response unit can input the content and wording of a user's tweets into AI, which can then analyze the content and generate an emotional response.

[0033] The monitoring unit monitors user comments and inquiries on the SNS in real time and can respond immediately to important inquiries. The monitoring unit, for example, monitors user comments on the SNS in real time. For example, the monitoring unit monitors user comments on the SNS in real time and responds immediately based on the comments. For example, the monitoring unit receives user comments on the SNS as input and responds immediately based on the comments. The monitoring unit can also monitor user inquiries on the SNS in real time. For example, the monitoring unit monitors user inquiries on the SNS in real time and responds immediately based on the inquiries. For example, the monitoring unit receives user inquiries on the SNS as input and responds immediately based on the inquiries. The monitoring unit can also respond immediately to important inquiries on the SNS. For example, the monitoring unit monitors important inquiries on the SNS in real time and responds immediately based on the inquiries. For example, the monitoring unit receives important inquiries on the SNS as input and responds immediately based on the inquiries. This makes it possible to improve user satisfaction through real-time monitoring and immediate responses. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input user comments and inquiries on SNS into AI, which may analyze the content and respond immediately.

[0034] The language support unit can generate responses in various languages, such as English, Japanese, and Chinese. The language support unit generates a response in English, for example. For example, the language support unit translates a user's input into English and generates a response in English. For example, the language support unit translates a user's input into English and generates a response based on the translation result. The language support unit can also generate a response in Japanese. For example, the language support unit translates a user's input into Japanese and generates a response in Japanese. For example, the language support unit translates a user's input into Japanese and generates a response based on the translation result. The language support unit can also generate a response in Chinese. For example, the language support unit translates a user's input into Chinese and generates a response in Chinese. For example, the language support unit translates a user's input into Chinese and generates a response based on the translation result. This allows support for multiple languages, making it possible to support users globally. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit can input user input into the AI, which can then translate the content and generate a response.

[0035] The collection unit can analyze the user's past SNS activity history and select the optimal information collection method. For example, the collection unit prioritizes collection of related information based on content frequently viewed by the user in the past. For example, the collection unit analyzes the user's past SNS activity history and collects information based on the frequently viewed content. The collection unit can also analyze posts that the user has previously responded to and collect similar information. For example, the collection unit analyzes the user's past SNS activity history and collects information based on posts that have received many responses. Furthermore, the collection unit can analyze the content of the user's past posts and collect information that matches the user's interests. For example, the collection unit analyzes the user's past SNS activity history and collects information based on the content of the posts. This enables more effective information collection by analyzing the user's past SNS activity history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past SNS activity history into AI, which can then analyze the history and select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user is currently traveling, the collection unit prioritizes collecting information related to the travel destination. For example, the collection unit analyzes the user's current living situation and collects information related to the travel destination if the user is traveling. Furthermore, if the user has started a new hobby, the collection unit can collect information related to the hobby. For example, the collection unit analyzes the user's current living situation and collects information related to the new hobby. Furthermore, if the user is participating in a specific event, the collection unit can collect information related to the event. For example, the collection unit analyzes the user's current living situation and collects information related to the specific event. This allows more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's current living situation and areas of interest into AI, which can then analyze the information and perform filtering.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the collection unit collects event information related to the city. For example, the collection unit analyzes the user's geographical location information and, if the user is in a specific city, collects event information related to the city. Furthermore, if the user is in a specific country, the collection unit can collect news and trend information for that country. For example, the collection unit analyzes the user's geographical location information and, if the user is in a specific country, collects news and trend information for that country. Furthermore, if the user is in a specific region, the collection unit can collect weather information and traffic information for that region. For example, the collection unit analyzes the user's geographical location information and, if the user is in a specific region, collects weather information and traffic information for that region. In this way, by taking the user's geographical location information into consideration, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the information to collect highly relevant information.

[0038] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, if the user frequently uses a specific hashtag, the collection unit can collect information related to the hashtag. For example, the collection unit can analyze the user's social media activity and collect information based on the frequently used hashtag. Furthermore, if the user follows a specific account, the collection unit can also collect information related to the account. For example, the collection unit can analyze the user's social media activity and collect information based on the accounts the user follows. Furthermore, if the user posts frequently on a specific topic, the collection unit can also collect information related to the topic. For example, the collection unit can analyze the user's social media activity and collect information based on the topics to which posts are frequently made. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity into AI, which can then analyze the activity and collect related information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, provides detailed analysis results for information of high importance. For example, the analysis unit evaluates the importance of the collected information and performs a detailed analysis on the highly important information. The analysis unit can also provide concise analysis results for information of low importance. For example, the analysis unit evaluates the importance of the collected information and performs a concise analysis on the low important information. Furthermore, the analysis unit can also provide analysis results with a moderate level of detail for information of medium importance. For example, the analysis unit evaluates the importance of the collected information and performs an analysis with a moderate level of detail on the medium important information. In this way, by adjusting the level of detail of the analysis based on the importance of the collected information, more effective analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the collected information to AI, and the AI ​​can evaluate the importance and determine the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the user category. For example, the analysis unit applies a trend analysis algorithm to young users. For example, the analysis unit evaluates the user category and applies a trend analysis algorithm to young users. The analysis unit can also apply an analysis algorithm that emphasizes reliable information to elderly users. For example, the analysis unit evaluates the user category and applies an analysis algorithm that emphasizes reliable information to elderly users. The analysis unit can also apply an industry-specific analysis algorithm to business users. For example, the analysis unit evaluates the user category and applies an industry-specific analysis algorithm to business users. This makes it possible to provide more appropriate analysis results by applying an analysis algorithm according to the user category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user category into AI, and the AI ​​can evaluate the category and determine the analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit evaluates the time when the information was collected and prioritizes analysis of the most recent information. The analysis unit can also analyze older information as needed. For example, the analysis unit evaluates the time when the information was collected and analyzes older information as needed. Furthermore, the analysis unit can also analyze information of moderate recency with a moderate priority. For example, the analysis unit evaluates the time when the information was collected and analyzes information of moderate recency with a moderate priority. In this way, by determining the priority of analysis based on the time when the information was collected, more effective analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the information was collected into AI, and the AI ​​can evaluate the time and determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of information with high relevance. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit evaluates the relevance of the information and postpones analysis of information with low relevance. Furthermore, the analysis unit can analyze information with medium relevance in a moderate order. For example, the analysis unit evaluates the relevance of the information and analyzes information with medium relevance in a moderate order. In this way, by adjusting the order of analysis based on the relevance of the information, more effective analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the relevance of the information into AI, and the AI ​​can evaluate the relevance and determine the order of analysis.

[0043] When generating an emotional response, the emotional response unit can select an optimal response method by referring to the user's past response history. For example, the emotional response unit preferentially uses response methods that have previously elicited favorable responses from the user. For example, the emotional response unit can select response methods that have previously elicited favorable responses from the user by referring to the user's past response history. The emotional response unit can also avoid response methods that have previously elicited negative responses from the user. For example, the emotional response unit can avoid response methods that have previously elicited negative responses from the user by referring to the user's past response history. Furthermore, the emotional response unit can analyze the user's past response history and select the most effective response method. For example, the emotional response unit can select an effective response method by referring to the user's past response history. In this way, a more effective response method can be selected by referring to the user's past response history. Some or all of the above-described processing in the emotional response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotional response unit can input the user's past response history into AI, which can then analyze the history to select the optimal response method.

[0044] When generating an emotional response, the emotional response unit can select an optimal response method by taking into account the user's geographical location information. For example, if the user is in a specific city, the emotional response unit generates a response including information related to the city. For example, the emotional response unit analyzes the user's geographical location information and generates a response including information related to the city when the user is in a specific city. Furthermore, if the user is in a specific country, the emotional response unit can generate a response that takes into account the culture of the country when the user is in that country. For example, the emotional response unit analyzes the user's geographical location information and generates a response that takes into account the culture of the country when the user is in that country. Furthermore, if the user is in a specific region, the emotional response unit can generate a response that takes into account the dialect and vocabulary of the region when the user is in that region. For example, the emotional response unit analyzes the user's geographical location information and generates a response that takes into account the dialect and vocabulary of the region when the user is in that region. This makes it possible to provide a more appropriate response by taking into account the user's geographical location information. Some or all of the above-described processing in the emotional response unit may be performed, for example, using AI or without AI. For example, the emotional response unit can input the user's geographical location information into the AI, which can then analyze the information and select the optimal response method.

[0045] When generating an emotional response, the emotional response unit can analyze the user's social media activity and suggest a means of response. The emotional response unit can suggest an optimal means of response based on, for example, the social media platform frequently used by the user. For example, the emotional response unit can analyze the user's social media activity and suggest a means of response based on the frequently used social media platform. Furthermore, if the user frequently uses a specific hashtag, the emotional response unit can generate a response including the hashtag. For example, the emotional response unit can analyze the user's social media activity and generate a response based on the frequently used hashtag. Furthermore, if the user follows a specific account, the emotional response unit can generate a response including information related to the account. For example, the emotional response unit can analyze the user's social media activity and generate a response based on the followed account. In this way, by analyzing the user's social media activity, more appropriate means of response can be suggested. Some or all of the above-described processing in the emotional response unit can be performed using, for example, AI, or without AI. For example, the emotional response unit can input the user's social media activity into AI, which can then analyze the activity and suggest a means of response.

[0046] During monitoring, the monitoring unit can select the optimal monitoring method by referring to past monitoring data. The monitoring unit adjusts the monitoring frequency, for example, based on time periods when there were many inquiries in the past. For example, the monitoring unit refers to past monitoring data and adjusts the monitoring frequency based on time periods when there were many inquiries. The monitoring unit can also prioritize monitoring inquiries about a specific topic from the past monitoring data. For example, the monitoring unit refers to past monitoring data and prioritizes monitoring inquiries about a specific topic. Furthermore, the monitoring unit can analyze past monitoring data and select the most effective monitoring method. For example, the monitoring unit selects an effective monitoring method by referring to past monitoring data. In this way, a more effective monitoring method can be selected by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into AI, and the AI ​​can analyze the data to select the optimal monitoring method.

[0047] During monitoring, the monitoring unit can customize the monitoring means based on the user's current activity status. For example, if the user is participating in an event, the monitoring unit prioritizes monitoring comments related to the event. For example, the monitoring unit analyzes the user's current activity status and, if the user is participating in an event, monitors comments related to the event. Furthermore, if the user is traveling, the monitoring unit can prioritize monitoring comments related to the travel destination. For example, the monitoring unit analyzes the user's current activity status and, if the user is traveling, monitors comments related to the travel destination. Furthermore, if the user has started a new hobby, the monitoring unit can prioritize monitoring comments related to the hobby. For example, the monitoring unit analyzes the user's current activity status and monitors comments related to the new hobby. This allows for more appropriate monitoring by customizing the monitoring means based on the user's current activity status. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's current activity status into AI, which can analyze the status and determine the monitoring means.

[0048] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific city, the monitoring unit prioritizes monitoring of comments related to that city. For example, the monitoring unit analyzes the user's geographical location information and, if the user is in a specific city, monitors comments related to that city. Furthermore, if the user is in a specific country, the monitoring unit can perform monitoring that takes into account the culture of that country. For example, the monitoring unit analyzes the user's geographical location information and, if the user is in a specific country, performs monitoring that takes into account the culture of that country. Furthermore, if the user is in a specific region, the monitoring unit can perform monitoring that takes into account the dialect and vocabulary of that region. For example, the monitoring unit analyzes the user's geographical location information and, if the user is in a specific region, performs monitoring that takes into account the dialect and vocabulary of that region. This allows for more appropriate monitoring by taking into account the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's geographical location information into AI, and the AI ​​can analyze the information to select the optimal monitoring method.

[0049] During monitoring, the monitoring unit can analyze the user's social media activities and suggest monitoring measures. The monitoring unit can suggest optimal monitoring measures, for example, based on the social media platforms frequently used by the user. For example, the monitoring unit can analyze the user's social media activities and suggest monitoring measures based on the frequently used social media platforms. Furthermore, if a user frequently uses a specific hashtag, the monitoring unit can prioritize monitoring posts related to that hashtag. For example, the monitoring unit can analyze the user's social media activities and perform monitoring based on the frequently used hashtag. Furthermore, if the user follows a specific account, the monitoring unit can prioritize monitoring posts related to that account. For example, the monitoring unit can analyze the user's social media activities and perform monitoring based on the accounts followed. In this way, by analyzing the user's social media activities, more appropriate monitoring measures can be suggested. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI, or can be performed without AI. For example, the monitoring unit can input the user's social media activities into AI, which can then analyze the activities and suggest monitoring measures.

[0050] The language support unit can select an optimal language support method by referring to the user's past language usage history during language support. For example, the language support unit prioritizes the use of languages ​​previously used by the user. For example, the language support unit references the user's past language usage history and performs language support based on the used languages. The language support unit can also prioritize the use of language support methods to which the user has previously responded favorably. For example, the language support unit references the user's past language usage history and selects a language support method to which the user has previously responded favorably. Furthermore, the language support unit can analyze the user's past language usage history and select the most effective language support method. For example, the language support unit references the user's past language usage history and selects an effective language support method. In this way, by referring to the user's past language usage history, a more effective language support method can be selected. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without AI. For example, the language support unit can input the user's past language usage history into AI, which can analyze the history to select the optimal language support method.

[0051] During language support, the language support unit can customize language support means based on the user's current language usage situation. The language support unit, for example, prioritizes the use of the language currently used by the user. For example, the language support unit analyzes the user's current language usage situation and performs language support based on the currently used language. Furthermore, if the user uses multiple languages, the language support unit can prioritize the use of the most frequently used language. For example, the language support unit analyzes the user's current language usage situation and performs language support based on the most frequently used language. Furthermore, if the user uses a specific language, the language support unit can perform language support optimized for that language. For example, the language support unit analyzes the user's current language usage situation and performs language support based on the specific language. This allows for more appropriate language support by customizing the language support means based on the user's current language usage situation. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without AI. For example, the language support unit can input the user's current language usage situation into AI, which can then analyze the situation and determine the language support means.

[0052] The language support unit can select the optimal language support method by taking into account the user's geographical location information when providing language support. For example, if the user is in a specific city, the language support unit performs language support that takes into account the dialect and vocabulary of the city. For example, the language support unit analyzes the user's geographical location information, and if the user is in a specific city, performs language support that takes into account the dialect and vocabulary of the city. Furthermore, if the user is in a specific country, the language support unit can also perform language support that takes into account the culture of the country. For example, the language support unit analyzes the user's geographical location information, and if the user is in a specific country, performs language support that takes into account the culture of the country. Furthermore, if the user is in a specific region, the language support unit can also perform language support that takes into account the vocabulary of the region. For example, the language support unit analyzes the user's geographical location information, and if the user is in a specific region, performs language support that takes into account the vocabulary of the region. This allows for more appropriate language support by taking into account the user's geographical location information. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit can input the user's geographical location information into the AI, which can then analyze the information and select the optimal language support method.

[0053] During language support, the language support unit can analyze the user's social media activities and suggest language support measures. The language support unit can, for example, suggest optimal language support measures based on the social media platforms frequently used by the user. For example, the language support unit can analyze the user's social media activities and suggest language support measures based on the frequently used social media platforms. Furthermore, if a user frequently uses a specific hashtag, the language support unit can provide language support that includes the hashtag. For example, the language support unit can analyze the user's social media activities and provide language support based on the frequently used hashtag. Furthermore, if the user follows a specific account, the language support unit can provide language support that includes information related to the account. For example, the language support unit can analyze the user's social media activities and provide language support based on the accounts followed. This allows for the analysis of the user's social media activities to suggest more appropriate language support measures. Some or all of the above-described processing in the language support unit can be performed using, for example, AI, or without AI. For example, the language support unit can input the user's social media activities into AI, which can then analyze the activities and suggest language support measures.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The SNS operation automation system can further include a learning unit that learns user behavior patterns. The learning unit collects users' past behavior data and analyzes the behavior patterns. For example, the learning unit analyzes how frequently a user uses SNS during a specific time period and generates optimal responses for that time period. The learning unit can also learn how users react to specific topics and optimize responses related to those topics. Furthermore, the learning unit can predict future behavior based on the user's behavior patterns and prepare responses in advance based on the predictions. This enables more effective communication by optimizing responses based on the user's behavior patterns.

[0056] The collection unit can also collect information about the user's health condition. For example, if the user uses a fitness tracker or a health app, the collection unit collects that data to understand the user's health condition. In addition, if the user posts health-related information on a social networking site, the collection unit can analyze the content of the post and collect information about the user's health condition. Furthermore, if the user asks a health-related question, the collection unit can also collect information about the user's health condition based on the content of the question. This allows for the provision of more personalized services by optimizing responses based on the user's health condition.

[0057] The analysis unit can analyze a user's purchasing history and generate an optimal response. For example, the analysis unit can suggest related products and services based on products and services the user has purchased in the past. The analysis unit can also analyze a user's purchasing history to determine preferences for specific brands or categories and generate a response based on those preferences. Furthermore, the analysis unit can analyze a user's purchasing history and generate a response at the optimal time based on the frequency and timing of purchases. This enables more effective marketing by optimizing responses based on a user's purchasing history.

[0058] The SNS operation automation system may further include a feedback unit that collects user feedback. The feedback unit collects feedback provided by users and transmits it to the analysis unit. For example, if a user rates a response, the feedback unit collects the rating. The feedback unit may also collect the content of suggestions or opinions provided by users. Furthermore, if a user expresses dissatisfaction, the feedback unit may collect the content of the dissatisfaction. This allows the system to be improved based on user feedback and provide better services.

[0059] The collection unit can also collect device information about the user. For example, the collection unit collects the type of device and OS version used by the user and sends the information to the analysis unit. The collection unit can also collect device usage information about the user and provide information for generating an optimal response. Furthermore, the collection unit can collect setting information about the user's device and optimize responses based on the settings. This makes it possible to provide a more personalized service by optimizing responses based on the user's device information.

[0060] The analysis unit can analyze the user's social network and generate an optimal response. For example, the analysis unit can analyze the accounts and friend relationships the user follows and generate a response based on that information. The analysis unit can also analyze the user's influence within the social network and optimize the response based on that influence. Furthermore, the analysis unit can analyze the user's activity history within the social network and generate a response based on that history. This enables more effective communication by optimizing the response based on the user's social network.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects user profile information. For example, the collection unit may collect information such as the user's age, gender, and interests. For example, the collection unit may collect information for generating an appropriate response based on the user's age. The collection unit may also collect information for adjusting the tone of the response based on the user's gender. Furthermore, the collection unit may collect related information based on the user's interests. Step 2: The analysis unit analyzes the information collected by the collection unit and generates an individually optimized response. For example, the analysis unit may respond in a casual tone to younger users and in a polite tone to older users based on the collected information. For example, the analysis unit may generate a response in a tone that corresponds to the user's age group based on the collected information. The analysis unit may also generate a response in a tone that corresponds to the user's gender based on the collected information. Furthermore, the analysis unit may generate a response in a tone that corresponds to the user's interests based on the collected information. Step 3: The emotional response unit analyzes the user's tweets and tone and generates an emotional response tailored to the user. For example, the emotional response unit analyzes the content and wording of the user's tweets and generates an emotional response tailored to the user. The emotional response unit generates an emotional response based on, for example, the content of the user's tweets. The emotional response unit can also generate an emotional response based on the user's wording. Furthermore, the emotional response unit can also generate an emotional response based on the user's tone. Step 4: The monitoring unit performs monitoring in real time and responds immediately to important inquiries. For example, the monitoring unit monitors user comments and inquiries on the SNS in real time and responds immediately to important inquiries. The monitoring unit, for example, monitors user comments on the SNS in real time. The monitoring unit can also monitor user inquiries on the SNS in real time. Furthermore, the monitoring unit can also respond immediately to important inquiries on the SNS. Step 5: The language support unit supports multiple languages. For example, the language support unit generates responses in various languages, such as English, Japanese, and Chinese. For example, the language support unit generates a response in English. The language support unit can also generate a response in Japanese. The language support unit can also generate a response in Chinese.

[0063] (Example 2) An SNS management automation system according to an embodiment of the present invention reduces the man-hours required for SNS-related tasks. This SNS management automation system identifies users' profile information and generates individually optimized responses. Next, it analyzes the user's tweets and tone to generate emotional responses tailored to the individual. Furthermore, it performs real-time monitoring and immediately responds to important inquiries. Finally, by supporting multiple languages, it can accommodate global users. For example, the SNS management automation system collects and analyzes information such as the user's age, gender, and interests. This allows it to generate individually optimized responses. For example, it may respond in a casual tone to younger users and in a polite tone to older users. Next, the SNS management automation system analyzes the content and wording of the user's tweets and generates emotional responses tailored to the user. This makes communication with users more natural and friendly. Furthermore, the SNS management automation system monitors users' comments and inquiries on the SNS in real time and immediately responds to important inquiries. This improves user satisfaction. Finally, the SNS management automation system can generate responses in various languages, such as English, Japanese, and Chinese. This allows it to accommodate global users. This system allows SNS managers and management agencies to significantly reduce the amount of work required for SNS-related tasks. It also facilitates smooth communication with users, contributing to improving a company's brand image. As a result, the SNS management automation system reduces the amount of work required for SNS-related tasks and enables smooth communication with users.

[0064] An SNS operation automation system according to an embodiment includes a collection unit, an analysis unit, an emotional response unit, a monitoring unit, and a language support unit. The collection unit collects user profile information. For example, the collection unit can collect information such as the user's age, gender, and interests. The collection unit can collect information for generating an appropriate response based on the user's age, for example. The collection unit can also collect information for adjusting the tone of the response based on the user's gender. Furthermore, the collection unit can collect related information based on the user's interests. The analysis unit analyzes the information collected by the collection unit and generates an individually optimized response. For example, the analysis unit can respond to younger users in a casual tone and older users in a polite tone based on the collected information. For example, the analysis unit can generate a response in a tone appropriate to the user's age group based on the collected information. The analysis unit can also generate a response in a tone appropriate to the user's gender based on the collected information. Furthermore, the analysis unit can generate a response in a tone appropriate to the user's interests based on the collected information. The emotional response unit analyzes a user's tweets and tone and generates an emotional response tailored to the user. For example, the emotional response unit analyzes the content and wording of a user's tweets and generates an emotional response tailored to the user. The emotional response unit generates an emotional response based on, for example, the content of a user's tweets. The emotional response unit can also generate an emotional response based on the user's wording. Furthermore, the emotional response unit can also generate an emotional response based on the user's tone. The monitoring unit performs monitoring in real time and responds immediately to important inquiries. For example, the monitoring unit monitors user comments and inquiries on the SNS in real time and responds immediately to important inquiries. For example, the monitoring unit monitors user comments on the SNS in real time. The monitoring unit can also monitor user inquiries on the SNS in real time. Furthermore, the monitoring unit can respond immediately to important inquiries on the SNS. The language support unit supports multiple languages. For example, the language support unit generates responses in various languages, such as English, Japanese, and Chinese. The language support unit generates a response in English, for example.The language support unit can also generate responses in Japanese. Furthermore, the language support unit can also generate responses in Chinese. As a result, the SNS operation automation system according to the embodiment can reduce the number of steps required for SNS-related work and facilitate smooth communication with users.

[0065] The collection unit can collect information such as the user's age, gender, and interests. The collection unit, for example, collects information for generating an appropriate response based on the user's age. For example, the collection unit receives the user's age as input and collects information corresponding to the age. The collection unit can also collect information for adjusting the tone of the response based on the user's gender. For example, the collection unit receives the user's gender as input and collects information corresponding to the gender. Furthermore, the collection unit can collect related information based on the user's interests. For example, the collection unit receives the user's interests as input and collects information corresponding to the interests. In this way, by collecting information such as the user's age, gender, and interests, an individually optimized response can be generated. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information such as the user's age, gender, and interests into AI, and the AI ​​can analyze the information to determine the information to collect.

[0066] Based on the collected information, the analysis unit can respond in a casual tone to younger users and in a polite tone to older users. The analysis unit, for example, generates a response in a tone appropriate to the user's age group based on the collected information. For example, the analysis unit responds in a casual tone to younger users. For example, the analysis unit generates a response using friendly language to younger users. The analysis unit can also respond in a polite tone to older users. For example, the analysis unit generates a response using honorific language to older users. Furthermore, the analysis unit can also generate a response in a tone appropriate to the user's gender based on the collected information. For example, the analysis unit responds in a casual tone to male users. The analysis unit can also respond in a polite tone to female users. This enables more appropriate communication by responding in a tone appropriate to the user's age group. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into an AI, which can then analyze the information to determine the tone of the response.

[0067] The emotional response unit can analyze the content of a user's tweets and wording, and generate an emotional response tailored to the user. The emotional response unit generates an emotional response based on, for example, the content of a user's tweets. For example, the emotional response unit analyzes the content of a user's tweets and generates an emotional response based on the content. For example, the emotional response unit receives the content of a user's tweets as input, and generates an emotional response based on the content. The emotional response unit can also generate an emotional response based on the user's wording. For example, the emotional response unit analyzes the user's wording and generates an emotional response based on the wording. For example, the emotional response unit receives the user's wording as input, and generates an emotional response based on the wording. Furthermore, the emotional response unit can also generate an emotional response based on the user's tone. For example, the emotional response unit analyzes the user's tone and generates an emotional response based on the tone. For example, the emotional response unit receives the user's tone as input, and generates an emotional response based on the tone. This allows for more natural and friendly communication by generating emotional responses based on the content and wording of a user's tweets. Some or all of the above-described processing in the emotional response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotional response unit can input the content and wording of a user's tweets into AI, which can then analyze the content and generate an emotional response.

[0068] The monitoring unit monitors user comments and inquiries on the SNS in real time and can respond immediately to important inquiries. The monitoring unit, for example, monitors user comments on the SNS in real time. For example, the monitoring unit monitors user comments on the SNS in real time and responds immediately based on the comments. For example, the monitoring unit receives user comments on the SNS as input and responds immediately based on the comments. The monitoring unit can also monitor user inquiries on the SNS in real time. For example, the monitoring unit monitors user inquiries on the SNS in real time and responds immediately based on the inquiries. For example, the monitoring unit receives user inquiries on the SNS as input and responds immediately based on the inquiries. The monitoring unit can also respond immediately to important inquiries on the SNS. For example, the monitoring unit monitors important inquiries on the SNS in real time and responds immediately based on the inquiries. For example, the monitoring unit receives important inquiries on the SNS as input and responds immediately based on the inquiries. This makes it possible to improve user satisfaction through real-time monitoring and immediate responses. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input user comments and inquiries on SNS into AI, which may analyze the content and respond immediately.

[0069] The language support unit can generate responses in various languages, such as English, Japanese, and Chinese. The language support unit generates a response in English, for example. For example, the language support unit translates a user's input into English and generates a response in English. For example, the language support unit translates a user's input into English and generates a response based on the translation result. The language support unit can also generate a response in Japanese. For example, the language support unit translates a user's input into Japanese and generates a response in Japanese. For example, the language support unit translates a user's input into Japanese and generates a response based on the translation result. The language support unit can also generate a response in Chinese. For example, the language support unit translates a user's input into Chinese and generates a response in Chinese. For example, the language support unit translates a user's input into Chinese and generates a response based on the translation result. This allows support for multiple languages, making it possible to support users globally. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit can input user input into the AI, which can then translate the content and generate a response.

[0070] The collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user emotions. For example, when the user is expressing positive emotions, the collection unit prioritizes collecting positive information related to the user's interests. For example, the collection unit estimates the user's emotions and, when the user is expressing positive emotions, collects positive information based on the emotions. The collection unit can also collect information for reducing stress when the user is expressing negative emotions. For example, the collection unit estimates the user's emotions and, when the user is expressing negative emotions, collects information for reducing stress based on the emotions. Furthermore, the collection unit can collect a wide range of information in a balanced manner when the user is expressing neutral emotions. For example, the collection unit estimates the user's emotions and, when the user is expressing neutral emotions, collects a wide range of information based on the emotions. This allows the type of information to be adjusted based on the user's emotions, thereby collecting more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotions into AI, which may analyze the emotions and determine the type of information to collect.

[0071] The collection unit can analyze the user's past SNS activity history and select the optimal information collection method. For example, the collection unit prioritizes collection of related information based on content frequently viewed by the user in the past. For example, the collection unit analyzes the user's past SNS activity history and collects information based on the frequently viewed content. The collection unit can also analyze posts that the user has previously responded to and collect similar information. For example, the collection unit analyzes the user's past SNS activity history and collects information based on posts that have received many responses. Furthermore, the collection unit can analyze the content of the user's past posts and collect information that matches the user's interests. For example, the collection unit analyzes the user's past SNS activity history and collects information based on the content of the posts. This enables more effective information collection by analyzing the user's past SNS activity history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past SNS activity history into AI, which can then analyze the history and select the optimal information collection method.

[0072] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user is currently traveling, the collection unit prioritizes collecting information related to the travel destination. For example, the collection unit analyzes the user's current living situation and collects information related to the travel destination if the user is traveling. Furthermore, if the user has started a new hobby, the collection unit can collect information related to the hobby. For example, the collection unit analyzes the user's current living situation and collects information related to the new hobby. Furthermore, if the user is participating in a specific event, the collection unit can collect information related to the event. For example, the collection unit analyzes the user's current living situation and collects information related to the specific event. This allows more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's current living situation and areas of interest into AI, which can then analyze the information and perform filtering.

[0073] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest trend information. For example, the collection unit estimates the user's emotions and, if the user is excited, collects the latest trend information based on the user's emotions. Furthermore, if the user is depressed, the collection unit can prioritize collecting encouraging and comforting information. For example, the collection unit estimates the user's emotions and, if the user is depressed, collects encouraging and comforting information based on the user's emotions. Furthermore, the collection unit can prioritize collecting relaxing content if the user is relaxed. For example, the collection unit estimates the user's emotions and, if the user is relaxed, collects relaxing content based on the user's emotions. In this way, by determining the priority of information based on the user's emotions, more appropriate information can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotions into AI, which may analyze the emotions and determine the priority of the information to be collected.

[0074] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the collection unit collects event information related to the city. For example, the collection unit analyzes the user's geographical location information and, if the user is in a specific city, collects event information related to the city. Furthermore, if the user is in a specific country, the collection unit can collect news and trend information for that country. For example, the collection unit analyzes the user's geographical location information and, if the user is in a specific country, collects news and trend information for that country. Furthermore, if the user is in a specific region, the collection unit can collect weather information and traffic information for that region. For example, the collection unit analyzes the user's geographical location information and, if the user is in a specific region, collects weather information and traffic information for that region. In this way, by taking the user's geographical location information into consideration, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the information to collect highly relevant information.

[0075] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, if the user frequently uses a specific hashtag, the collection unit can collect information related to the hashtag. For example, the collection unit can analyze the user's social media activity and collect information based on the frequently used hashtag. Furthermore, if the user follows a specific account, the collection unit can also collect information related to the account. For example, the collection unit can analyze the user's social media activity and collect information based on the accounts the user follows. Furthermore, if the user posts frequently on a specific topic, the collection unit can also collect information related to the topic. For example, the collection unit can analyze the user's social media activity and collect information based on the topics to which posts are frequently made. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity into AI, which can then analyze the activity and collect related information.

[0076] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, if the user is expressing a positive emotion, the analysis unit displays the analysis result in a bright tone. For example, if the user is expressing a positive emotion, the analysis unit displays the analysis result in a bright tone based on the emotion. The analysis unit can also display the analysis result in a subdued tone if the user is expressing a negative emotion. For example, if the user is expressing a negative emotion, the analysis unit displays the analysis result in a subdued tone based on the emotion. The analysis unit can also display the analysis result in a standard tone if the user is expressing a neutral emotion. For example, if the user is expressing a neutral emotion, the analysis unit displays the analysis result in a standard tone based on the emotion. This allows for adjusting the presentation method of the analysis based on the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotions into AI, which then analyzes the emotions and determines how to express the analysis.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, provides detailed analysis results for information of high importance. For example, the analysis unit evaluates the importance of the collected information and performs a detailed analysis on the highly important information. The analysis unit can also provide concise analysis results for information of low importance. For example, the analysis unit evaluates the importance of the collected information and performs a concise analysis on the low important information. Furthermore, the analysis unit can also provide analysis results with a moderate level of detail for information of medium importance. For example, the analysis unit evaluates the importance of the collected information and performs an analysis with a moderate level of detail on the medium important information. In this way, by adjusting the level of detail of the analysis based on the importance of the collected information, more effective analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the collected information to AI, and the AI ​​can evaluate the importance and determine the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the user category. For example, the analysis unit applies a trend analysis algorithm to young users. For example, the analysis unit evaluates the user category and applies a trend analysis algorithm to young users. The analysis unit can also apply an analysis algorithm that emphasizes reliable information to elderly users. For example, the analysis unit evaluates the user category and applies an analysis algorithm that emphasizes reliable information to elderly users. The analysis unit can also apply an industry-specific analysis algorithm to business users. For example, the analysis unit evaluates the user category and applies an industry-specific analysis algorithm to business users. This makes it possible to provide more appropriate analysis results by applying an analysis algorithm according to the user category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user category into AI, and the AI ​​can evaluate the category and determine the analysis algorithm.

[0079] 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 provides a short and concise analysis result based on the user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result based on the user's emotions. The analysis unit can also provide a detailed analysis result based on the user's emotions if the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide a detailed analysis result based on the user's emotions if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can estimate the user's emotions and provide a visually stimulating analysis result based on the user's emotions if the user is excited. This allows the length of the analysis to be adjusted based on the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotions into AI, which then analyzes the emotions and determines the length of the analysis.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit evaluates the time when the information was collected and prioritizes analysis of the most recent information. The analysis unit can also analyze older information as needed. For example, the analysis unit evaluates the time when the information was collected and analyzes older information as needed. Furthermore, the analysis unit can also analyze information of moderate recency with a moderate priority. For example, the analysis unit evaluates the time when the information was collected and analyzes information of moderate recency with a moderate priority. In this way, by determining the priority of analysis based on the time when the information was collected, more effective analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the information was collected into AI, and the AI ​​can evaluate the time and determine the priority of analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of information with high relevance. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit evaluates the relevance of the information and postpones analysis of information with low relevance. Furthermore, the analysis unit can analyze information with medium relevance in a moderate order. For example, the analysis unit evaluates the relevance of the information and analyzes information with medium relevance in a moderate order. In this way, by adjusting the order of analysis based on the relevance of the information, more effective analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the relevance of the information into AI, and the AI ​​can evaluate the relevance and determine the order of analysis.

[0082] The emotion response unit can estimate the user's emotion and adjust the way a response is expressed based on the estimated user's emotion. For example, if the user is expressing a positive emotion, the emotion response unit responds in a bright tone. For example, if the user is expressing a positive emotion, the emotion response unit responds in a bright tone based on the emotion. The emotion response unit can also respond in a calm tone if the user is expressing a negative emotion. For example, the emotion response unit can estimate the user's emotion and respond in a calm tone based on the emotion if the user is expressing a negative emotion. Furthermore, the emotion response unit can also respond in a standard tone if the user is expressing a neutral emotion. For example, the emotion response unit can estimate the user's emotion and respond in a standard tone based on the emotion if the user is expressing a neutral emotion. This allows the system to provide a more appropriate response by adjusting the way a response is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotion response unit may input the user's emotion into AI, which may analyze the emotion and determine how to express the response.

[0083] When generating an emotional response, the emotional response unit can select an optimal response method by referring to the user's past response history. For example, the emotional response unit preferentially uses response methods that have previously elicited favorable responses from the user. For example, the emotional response unit can select response methods that have previously elicited favorable responses from the user by referring to the user's past response history. The emotional response unit can also avoid response methods that have previously elicited negative responses from the user. For example, the emotional response unit can avoid response methods that have previously elicited negative responses from the user by referring to the user's past response history. Furthermore, the emotional response unit can analyze the user's past response history and select the most effective response method. For example, the emotional response unit can select an effective response method by referring to the user's past response history. In this way, a more effective response method can be selected by referring to the user's past response history. Some or all of the above-described processing in the emotional response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotional response unit can input the user's past response history into AI, which can then analyze the history to select the optimal response method.

[0084] When generating an emotional response, the emotional response unit can customize the tone of the response based on the user's current emotional state. For example, if the user is excited, the emotional response unit responds with an energetic tone. For example, the emotional response unit estimates the user's emotion and, if the user is excited, responds with an energetic tone based on the emotion. The emotional response unit can also respond with a calm tone if the user is calm. For example, the emotional response unit estimates the user's emotion and, if the user is calm, responds with a calm tone based on the emotion. Furthermore, the emotional response unit can also respond with a comforting tone if the user is sad. For example, the emotional response unit estimates the user's emotion and, if the user is sad, responds with a comforting tone based on the emotion. This allows for customizing the tone of the response based on the user's current emotional state to provide a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotion response unit may input the user's emotion into AI, which may analyze the emotion and determine the tone of the response.

[0085] The emotion response unit can estimate the user's emotion and determine the priority of responses based on the estimated user's emotion. For example, the emotion response unit responds immediately when the user is expressing an urgent emotion. For example, the emotion response unit estimates the user's emotion and responds immediately based on the urgent emotion. The emotion response unit can also respond with a standard response time when the user is expressing a normal emotion. For example, the emotion response unit estimates the user's emotion and responds with a standard response time based on the normal emotion. Furthermore, the emotion response unit can respond with a slight delay when the user is relaxed. For example, the emotion response unit estimates the user's emotion and responds with a slight delay based on the relaxed emotion. This allows for response prioritization based on the user's emotion, thereby providing a response at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion response unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotion response unit may input the user's emotions into AI, which may analyze the emotions and determine the priority of responses.

[0086] When generating an emotional response, the emotional response unit can select an optimal response method by taking into account the user's geographical location information. For example, if the user is in a specific city, the emotional response unit generates a response including information related to the city. For example, the emotional response unit analyzes the user's geographical location information and generates a response including information related to the city when the user is in a specific city. Furthermore, if the user is in a specific country, the emotional response unit can generate a response that takes into account the culture of the country when the user is in that country. For example, the emotional response unit analyzes the user's geographical location information and generates a response that takes into account the culture of the country when the user is in that country. Furthermore, if the user is in a specific region, the emotional response unit can generate a response that takes into account the dialect and vocabulary of the region when the user is in that region. For example, the emotional response unit analyzes the user's geographical location information and generates a response that takes into account the dialect and vocabulary of the region when the user is in that region. This makes it possible to provide a more appropriate response by taking into account the user's geographical location information. Some or all of the above-described processing in the emotional response unit may be performed, for example, using AI or without AI. For example, the emotional response unit can input the user's geographical location information into the AI, which can then analyze the information and select the optimal response method.

[0087] When generating an emotional response, the emotional response unit can analyze the user's social media activity and suggest a means of response. The emotional response unit can suggest an optimal means of response based on, for example, the social media platform frequently used by the user. For example, the emotional response unit can analyze the user's social media activity and suggest a means of response based on the frequently used social media platform. Furthermore, if the user frequently uses a specific hashtag, the emotional response unit can generate a response including the hashtag. For example, the emotional response unit can analyze the user's social media activity and generate a response based on the frequently used hashtag. Furthermore, if the user follows a specific account, the emotional response unit can generate a response including information related to the account. For example, the emotional response unit can analyze the user's social media activity and generate a response based on the followed account. In this way, by analyzing the user's social media activity, more appropriate means of response can be suggested. Some or all of the above-described processing in the emotional response unit can be performed using, for example, AI, or without AI. For example, the emotional response unit can input the user's social media activity into AI, which can then analyze the activity and suggest a means of response.

[0088] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, if the user is nervous, the monitoring unit performs frequent monitoring and takes immediate action. For example, if the user is nervous, the monitoring unit can estimate the user's emotions and perform frequent monitoring and take immediate action based on the user's emotions. The monitoring unit can also monitor at a normal frequency if the user is relaxed. For example, the monitoring unit can estimate the user's emotions and, if the user is relaxed, can take a normal monitoring frequency based on the user's emotions. Furthermore, if the user is excited, the monitoring unit can prioritize monitoring important comments and inquiries. For example, the monitoring unit can estimate the user's emotions and, if the user is excited, prioritize monitoring important comments and inquiries based on the user's emotions. This allows for more appropriate monitoring by adjusting the monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's emotions into AI, which may analyze the emotions and determine the monitoring method.

[0089] During monitoring, the monitoring unit can select the optimal monitoring method by referring to past monitoring data. The monitoring unit adjusts the monitoring frequency, for example, based on time periods when there were many inquiries in the past. For example, the monitoring unit refers to past monitoring data and adjusts the monitoring frequency based on time periods when there were many inquiries. The monitoring unit can also prioritize monitoring inquiries about a specific topic from the past monitoring data. For example, the monitoring unit refers to past monitoring data and prioritizes monitoring inquiries about a specific topic. Furthermore, the monitoring unit can analyze past monitoring data and select the most effective monitoring method. For example, the monitoring unit selects an effective monitoring method by referring to past monitoring data. In this way, a more effective monitoring method can be selected by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into AI, and the AI ​​can analyze the data to select the optimal monitoring method.

[0090] During monitoring, the monitoring unit can customize the monitoring means based on the user's current activity status. For example, if the user is participating in an event, the monitoring unit prioritizes monitoring comments related to the event. For example, the monitoring unit analyzes the user's current activity status and, if the user is participating in an event, monitors comments related to the event. Furthermore, if the user is traveling, the monitoring unit can prioritize monitoring comments related to the travel destination. For example, the monitoring unit analyzes the user's current activity status and, if the user is traveling, monitors comments related to the travel destination. Furthermore, if the user has started a new hobby, the monitoring unit can prioritize monitoring comments related to the hobby. For example, the monitoring unit analyzes the user's current activity status and monitors comments related to the new hobby. This allows for more appropriate monitoring by customizing the monitoring means based on the user's current activity status. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's current activity status into AI, which can analyze the status and determine the monitoring means.

[0091] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, if the user is expressing an urgent emotion, the monitoring unit performs monitoring immediately. For example, if the user is expressing an urgent emotion, the monitoring unit performs monitoring immediately based on the emotion. The monitoring unit can also respond at a standard monitoring frequency if the user is expressing a normal emotion. For example, the monitoring unit can estimate the user's emotions and respond at a standard monitoring frequency based on the emotion if the user is expressing a normal emotion. Furthermore, the monitoring unit can perform monitoring with a slight delay if the user is relaxed. For example, if the user is relaxed, the monitoring unit can perform monitoring with a slight delay based on the emotion. This allows monitoring to be performed at a more appropriate time by determining the monitoring priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's emotions into AI, which may analyze the emotions and determine monitoring priorities.

[0092] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific city, the monitoring unit prioritizes monitoring of comments related to that city. For example, the monitoring unit analyzes the user's geographical location information and, if the user is in a specific city, monitors comments related to that city. Furthermore, if the user is in a specific country, the monitoring unit can perform monitoring that takes into account the culture of that country. For example, the monitoring unit analyzes the user's geographical location information and, if the user is in a specific country, performs monitoring that takes into account the culture of that country. Furthermore, if the user is in a specific region, the monitoring unit can perform monitoring that takes into account the dialect and vocabulary of that region. For example, the monitoring unit analyzes the user's geographical location information and, if the user is in a specific region, performs monitoring that takes into account the dialect and vocabulary of that region. This allows for more appropriate monitoring by taking into account the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's geographical location information into AI, and the AI ​​can analyze the information to select the optimal monitoring method.

[0093] During monitoring, the monitoring unit can analyze the user's social media activities and suggest monitoring measures. The monitoring unit can suggest optimal monitoring measures, for example, based on the social media platforms frequently used by the user. For example, the monitoring unit can analyze the user's social media activities and suggest monitoring measures based on the frequently used social media platforms. Furthermore, if a user frequently uses a specific hashtag, the monitoring unit can prioritize monitoring posts related to that hashtag. For example, the monitoring unit can analyze the user's social media activities and perform monitoring based on the frequently used hashtag. Furthermore, if the user follows a specific account, the monitoring unit can prioritize monitoring posts related to that account. For example, the monitoring unit can analyze the user's social media activities and perform monitoring based on the accounts followed. In this way, by analyzing the user's social media activities, more appropriate monitoring measures can be suggested. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI, or can be performed without AI. For example, the monitoring unit can input the user's social media activities into AI, which can then analyze the activities and suggest monitoring measures.

[0094] The language support unit can estimate the user's emotions and adjust the language support method based on the estimated user's emotions. For example, if the user is expressing a positive emotion, the language support unit uses a bright tone to support the user's emotions. For example, if the user is expressing a positive emotion, the language support unit uses a bright tone to support the user's emotions. The language support unit can also use a calm tone to support the user's emotions. For example, if the user is expressing a negative emotion, the language support unit uses a calm tone to support the user's emotions. The language support unit can also use a standard tone to support the user's emotions. For example, if the user is expressing a neutral emotion, the language support unit uses a standard tone to support the user's emotions. This allows for more appropriate language support by adjusting the language support method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the language support unit may be performed using AI, or may be performed without using AI. For example, the language support unit may input the user's emotions into the AI, which may analyze the emotions and determine the language support method.

[0095] The language support unit can select an optimal language support method by referring to the user's past language usage history during language support. For example, the language support unit prioritizes the use of languages ​​previously used by the user. For example, the language support unit references the user's past language usage history and performs language support based on the used languages. The language support unit can also prioritize the use of language support methods to which the user has previously responded favorably. For example, the language support unit references the user's past language usage history and selects a language support method to which the user has previously responded favorably. Furthermore, the language support unit can analyze the user's past language usage history and select the most effective language support method. For example, the language support unit references the user's past language usage history and selects an effective language support method. In this way, by referring to the user's past language usage history, a more effective language support method can be selected. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without AI. For example, the language support unit can input the user's past language usage history into AI, which can analyze the history to select the optimal language support method.

[0096] During language support, the language support unit can customize language support means based on the user's current language usage situation. The language support unit, for example, prioritizes the use of the language currently used by the user. For example, the language support unit analyzes the user's current language usage situation and performs language support based on the currently used language. Furthermore, if the user uses multiple languages, the language support unit can prioritize the use of the most frequently used language. For example, the language support unit analyzes the user's current language usage situation and performs language support based on the most frequently used language. Furthermore, if the user uses a specific language, the language support unit can perform language support optimized for that language. For example, the language support unit analyzes the user's current language usage situation and performs language support based on the specific language. This allows for more appropriate language support by customizing the language support means based on the user's current language usage situation. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without AI. For example, the language support unit can input the user's current language usage situation into AI, which can analyze the situation and determine the language support means.

[0097] The language support unit can estimate the user's emotion and determine the priority of language support based on the estimated user's emotion. For example, if the user is expressing an urgent emotion, the language support unit immediately performs language support based on the emotion. For example, if the user is expressing an urgent emotion, the language support unit immediately performs language support based on the emotion. The language support unit can also perform language support within a standard response time if the user is expressing a normal emotion. For example, the language support unit can estimate the user's emotion and, if the user is expressing a normal emotion, perform language support within a standard response time based on the emotion. Furthermore, the language support unit can perform language support with a slight delay if the user is relaxed. For example, if the user is relaxed, the language support unit can perform language support with a slight delay based on the emotion. This allows language support to be performed at a more appropriate time by determining the priority of language support based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit may input the user's emotions into AI, which may then analyze the emotions and determine the priority of language support.

[0098] The language support unit can select the optimal language support method by taking into account the user's geographical location information when providing language support. For example, if the user is in a specific city, the language support unit performs language support that takes into account the dialect and vocabulary of the city. For example, the language support unit analyzes the user's geographical location information, and if the user is in a specific city, performs language support that takes into account the dialect and vocabulary of the city. Furthermore, if the user is in a specific country, the language support unit can also perform language support that takes into account the culture of the country. For example, the language support unit analyzes the user's geographical location information, and if the user is in a specific country, performs language support that takes into account the culture of the country. Furthermore, if the user is in a specific region, the language support unit can also perform language support that takes into account the vocabulary of the region. For example, the language support unit analyzes the user's geographical location information, and if the user is in a specific region, performs language support that takes into account the vocabulary of the region. This allows for more appropriate language support by taking into account the user's geographical location information. Some or all of the above-described processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit can input the user's geographical location information into the AI, which can then analyze the information and select the optimal language support method.

[0099] During language support, the language support unit can analyze the user's social media activities and suggest language support measures. The language support unit can, for example, suggest optimal language support measures based on the social media platforms frequently used by the user. For example, the language support unit can analyze the user's social media activities and suggest language support measures based on the frequently used social media platforms. Furthermore, if a user frequently uses a specific hashtag, the language support unit can provide language support that includes the hashtag. For example, the language support unit can analyze the user's social media activities and provide language support based on the frequently used hashtag. Furthermore, if the user follows a specific account, the language support unit can provide language support that includes information related to the account. For example, the language support unit can analyze the user's social media activities and provide language support based on the accounts followed. This allows for the analysis of the user's social media activities to suggest more appropriate language support measures. Some or all of the above-described processing in the language support unit can be performed using, for example, AI, or without AI. For example, the language support unit can input the user's social media activities into AI, which can then analyze the activities and suggest language support measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, emotional response unit, monitoring unit, and language support unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 38B of the smart device 14 and analyzes the information using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized response based on the collected information. The emotional response unit is implemented, for example, by the control unit 46A of the smart device 14 and analyzes the user's tweets and tone to generate an emotional response. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and monitors user comments and inquiries on the SNS in real time and immediately responds to important inquiries. The language support unit is implemented, for example, by the control unit 46A of the smart device 14 and generates responses in multiple languages. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, emotional response unit, monitoring unit, and language support unit, described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the information using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized response based on the collected information. The emotional response unit is implemented, for example, by the control unit 46A of the smart glasses 214 and analyzes the user's tweets and tone to generate an emotional response. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and monitors user comments and inquiries on the SNS in real time and immediately responds to important inquiries. The language support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and generates responses in multiple languages. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, emotional response unit, monitoring unit, and language support unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes the information using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized response based on the collected information. The emotional response unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and analyzes the user's tweets and tone to generate an emotional response. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and monitors user comments and inquiries on the SNS in real time and immediately responds to important inquiries. The language support unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and generates responses in multiple languages. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, emotional response unit, monitoring unit, and language support unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the robot 414 and analyzes the information using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized response based on the collected information. The emotional response unit is implemented, for example, by the control unit 46A of the robot 414 and analyzes the user's tweets and tone to generate an emotional response. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and monitors user comments and inquiries on the SNS in real time and immediately responds to important inquiries. The language support unit is implemented, for example, by the control unit 46A of the robot 414 and generates responses in multiple languages.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The SNS operation automation system can further include a learning unit that learns user behavior patterns. The learning unit collects users' past behavior data and analyzes the behavior patterns. For example, the learning unit analyzes how frequently a user uses SNS during a specific time period and generates optimal responses for that time period. The learning unit can also learn how users react to specific topics and optimize responses related to those topics. Furthermore, the learning unit can predict future behavior based on the user's behavior patterns and prepare responses in advance based on the predictions. This enables more effective communication by optimizing responses based on the user's behavior patterns.

[0102] The collection unit can also collect information about the user's health condition. For example, if the user uses a fitness tracker or a health app, the collection unit collects that data to understand the user's health condition. In addition, if the user posts health-related information on a social networking site, the collection unit can analyze the content of the post and collect information about the user's health condition. Furthermore, if the user asks a health-related question, the collection unit can also collect information about the user's health condition based on the content of the question. This allows for the provision of more personalized services by optimizing responses based on the user's health condition.

[0103] The analysis unit can analyze a user's purchasing history and generate an optimal response. For example, the analysis unit can suggest related products and services based on products and services the user has purchased in the past. The analysis unit can also analyze a user's purchasing history to determine preferences for specific brands or categories and generate a response based on those preferences. Furthermore, the analysis unit can analyze a user's purchasing history and generate a response at the optimal time based on the frequency and timing of purchases. This enables more effective marketing by optimizing responses based on a user's purchasing history.

[0104] The emotion response unit can estimate the user's emotion and adjust the content of the response based on the estimated user's emotion. For example, if the user is happy, the emotion response unit can generate a congratulatory message based on the user's emotion. If the user is sad, the emotion response unit can also generate a comforting message based on the user's emotion. Furthermore, if the user is angry, the emotion response unit can also generate an apology message based on the user's emotion. This allows for more appropriate communication by adjusting the content of the response based on the user's emotion.

[0105] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can increase the monitoring frequency based on the user's emotions and take immediate action. Also, if the user is relaxed, the monitoring unit can decrease the monitoring frequency based on the user's emotions. Furthermore, if the user is excited, the monitoring unit can prioritize monitoring of important comments and inquiries based on the user's emotions. In this way, more appropriate monitoring can be performed by adjusting the monitoring frequency based on the user's emotions.

[0106] The language support unit can estimate the user's emotion and adjust the tone of the language support based on the estimated user's emotion. For example, if the user is expressing a positive emotion, the language support unit can provide language support in a bright tone based on the emotion. Also, if the user is expressing a negative emotion, the language support unit can provide language support in a calm tone based on the emotion. Furthermore, if the user is expressing a neutral emotion, the language support unit can provide language support in a standard tone based on the emotion. In this way, by adjusting the tone of the language support based on the user's emotion, more appropriate language support can be provided.

[0107] The SNS operation automation system may further include a feedback unit that collects user feedback. The feedback unit collects feedback provided by users and transmits it to the analysis unit. For example, if a user rates a response, the feedback unit collects the rating. The feedback unit may also collect the content of suggestions or opinions provided by users. Furthermore, if a user expresses dissatisfaction, the feedback unit may collect the content of the dissatisfaction. This allows the system to be improved based on user feedback and provide better services.

[0108] The collection unit can also collect device information about the user. For example, the collection unit collects the type of device and OS version used by the user and sends the information to the analysis unit. The collection unit can also collect device usage information about the user and provide information for generating an optimal response. Furthermore, the collection unit can collect setting information about the user's device and optimize responses based on the settings. This makes it possible to provide a more personalized service by optimizing responses based on the user's device information.

[0109] The analysis unit can analyze the user's social network and generate an optimal response. For example, the analysis unit can analyze the accounts and friend relationships the user follows and generate a response based on that information. The analysis unit can also analyze the user's influence within the social network and optimize the response based on that influence. Furthermore, the analysis unit can analyze the user's activity history within the social network and generate a response based on that history. This enables more effective communication by optimizing the response based on the user's social network.

[0110] The emotion response unit can estimate the user's emotion and adjust the format of the response based on the estimated user's emotion. For example, if the user is expressing a positive emotion, the emotion response unit can generate a response using emojis or stamps based on the emotion. Also, if the user is expressing a negative emotion, the emotion response unit can generate a text-only response based on the emotion. Furthermore, if the user is expressing a neutral emotion, the emotion response unit can generate a standard format response based on the emotion. This allows for more appropriate communication by adjusting the format of the response based on the user's emotion.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects user profile information. For example, the collection unit may collect information such as the user's age, gender, and interests. For example, the collection unit may collect information for generating an appropriate response based on the user's age. The collection unit may also collect information for adjusting the tone of the response based on the user's gender. Furthermore, the collection unit may collect related information based on the user's interests. Step 2: The analysis unit analyzes the information collected by the collection unit and generates an individually optimized response. For example, the analysis unit may respond in a casual tone to younger users and in a polite tone to older users based on the collected information. For example, the analysis unit may generate a response in a tone that corresponds to the user's age group based on the collected information. The analysis unit may also generate a response in a tone that corresponds to the user's gender based on the collected information. Furthermore, the analysis unit may generate a response in a tone that corresponds to the user's interests based on the collected information. Step 3: The emotional response unit analyzes the user's tweets and tone and generates an emotional response tailored to the user. For example, the emotional response unit analyzes the content and wording of the user's tweets and generates an emotional response tailored to the user. The emotional response unit generates an emotional response based on, for example, the content of the user's tweets. The emotional response unit can also generate an emotional response based on the user's wording. Furthermore, the emotional response unit can also generate an emotional response based on the user's tone. Step 4: The monitoring unit performs monitoring in real time and responds immediately to important inquiries. For example, the monitoring unit monitors user comments and inquiries on the SNS in real time and responds immediately to important inquiries. The monitoring unit, for example, monitors user comments on the SNS in real time. The monitoring unit can also monitor user inquiries on the SNS in real time. Furthermore, the monitoring unit can also respond immediately to important inquiries on the SNS. Step 5: The language support unit supports multiple languages. For example, the language support unit generates responses in various languages, such as English, Japanese, and Chinese. For example, the language support unit generates a response in English. The language support unit can also generate a response in Japanese. The language support unit can also generate a response in Chinese.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user profile information; an analysis unit that analyzes the information collected by the collection unit and generates an individually optimized response; An emotional response unit that analyzes the user's tweets and tone and generates emotional responses tailored to the individual; The monitoring department performs real-time monitoring and responds immediately to important inquiries. A language support unit that supports multiple languages. A system characterized by:

2. The collecting unit Collect information such as the user's age, gender, and interests 2. The system of claim 1.

3. The analysis unit Based on the information collected, respond to younger users in a more casual tone and older users in a more polite tone.

2. The system of claim 1.

4. The emotion response unit Analyzes the content and language of a user's tweets and generates emotional responses tailored to that user.

2. The system of claim 1.

5. The monitoring unit Monitor user comments and inquiries on social media in real time and respond immediately to important inquiries.

2. The system of claim 1.

6. The language support unit Generate responses in various languages, including English, Japanese, and Chinese 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the type of information collected based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past social media activity history and select the most appropriate method of collecting information 2. The system of claim 1.

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