system

Abstract

An object of a system according to an embodiment is to realize smooth communication with a user even when an SNS operator has a shortage of manpower.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, a monitoring unit, an association unit, and a language association unit. The collection unit collects activity information of a user. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a response based on the information analyzed by the analysis unit. The providing unit provides the response generated by the generating unit. The monitoring unit performs monitoring in real time. The responder immediately responds to important inquiries or urgent requests. The language corresponding unit corresponds to a plurality of languages.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem that SNS operators were short-staffed and were unable to communicate smoothly with users.

[0005] The system according to the embodiment aims to enable SNS operators to achieve smooth communication with users even when they are short-staffed. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, a monitoring unit, a response unit, and a language support unit. The collection unit collects user activity information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a response based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit. The monitoring unit performs monitoring in real time. The response unit immediately responds to important inquiries or emergency requests. The language support unit supports multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment allows an SNS operator to achieve smooth communication with users even when the operator is short-staffed. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An SNS auto-response system according to an embodiment of the present invention collects and analyzes user activity information, and generates and provides optimal responses. The SNS auto-response system collects and analyzes user activity information and generates optimal responses, allowing SNS operators to efficiently communicate with users even when staffing is limited. For example, the SNS auto-response system collects user activity information. For example, the SNS auto-response system collects users' past posts and profile information. Then, the SNS auto-response system analyzes the collected information and generates individually optimized responses. For example, the SNS auto-response system generates responses that provide relevant information based on topics the user has previously shown interest in. Next, the SNS auto-response system analyzes the emotions and tone of the user's posts and messages to generate emotional responses tailored to the individual. For example, if the user is angry, the system generates a calming response, and if the user is happy, the system generates an empathetic response. Next, the SNS auto-response system performs real-time monitoring and immediately responds to important inquiries and emergency requests. For example, if a user makes an urgent inquiry, the system responds immediately, improving user satisfaction. Next, the SNS auto-response system supports multiple languages ​​and provides appropriate responses to users who speak different languages. For example, by supporting various languages ​​such as English, Spanish, and French, SNS auto-response systems can accommodate global users. This allows SNS operators to resolve staff shortages and communicate with users efficiently. For example, SNS auto-response systems can quickly and accurately collect and analyze user activity information to generate optimal responses. SNS auto-response systems can also improve user satisfaction by analyzing users' emotions and tone and generating emotional responses tailored to each individual. Furthermore, SNS auto-response systems can improve user satisfaction by monitoring in real time and responding immediately to important inquiries and emergency requests. Furthermore, by supporting multiple languages, SNS auto-response systems can provide appropriate responses to global users.

[0029] An SNS automatic response system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, a monitoring unit, a response unit, and a language response unit. The collection unit collects user activity information. The user activity information includes, but is not limited to, browsing history, click history, and purchase history. The collection unit collects, for example, the user's past posts and profile information. The collection unit can also collect information based on the user's current activity status and areas of interest. For example, the collection unit collects information during the user's currently active time period and responds in real time. The analysis unit analyzes the collected information and generates an individually optimized response. For example, the analysis unit analyzes the collected information and generates an optimal response for the user. For example, the analysis unit generates a response that provides relevant information based on topics in which the user has previously shown interest. The generation unit analyzes the user's emotions and tone and generates an emotional response tailored to the user. For example, the generation unit analyzes the user's emotions and tone and generates an emotional response. For example, the generation unit generates a calming response when the user is angry, and generates a sympathetic response when the user is happy. The provision unit provides the response generated by the generation unit. The provision unit provides the generated response, for example, in real time. The provision unit can also provide the generated responses in batches. The monitoring unit performs monitoring in real time and immediately responds to important inquiries and emergency requests. The monitoring unit performs monitoring in real time and immediately responds to important inquiries and emergency requests. For example, the monitoring unit immediately responds when an emergency inquiry is made by a user. The response unit immediately responds to important inquiries and emergency requests. The response unit immediately responds to important inquiries and emergency requests. For example, the response unit immediately responds when an emergency inquiry is made by a user. The language support unit supports multiple languages ​​and provides appropriate responses to users who speak different languages. The language support unit supports multiple languages ​​and provides appropriate responses to users who speak different languages. For example, the language support unit supports various languages ​​such as English, Spanish, and French.As a result, the SNS automatic response system according to the embodiment can collect and analyze user activity information, generate and provide optimal responses, and, for example, enable SNS operators to communicate with users efficiently even when they are short-staffed.

[0030] The collection unit can collect the user's past posts or profile information. The collection unit, for example, collects the user's past posts. For example, the collection unit collects social media posts, blog articles, etc. The collection unit can also collect the user's profile information. For example, the collection unit collects profile information such as the user's name, age, and interests. By collecting the user's past posts and profile information, information for generating individually optimized responses can be obtained. Some or all of the above-mentioned 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 the user's past posts and profile information into the generation AI and cause the generation AI to collect the information.

[0031] The analysis unit can analyze the collected information and generate an individually optimized response. The analysis unit, for example, analyzes the collected information and generates an individually optimized response. For example, the analysis unit generates a response that provides relevant information based on topics in which the user has previously shown interest. The analysis unit can also analyze the collected information and generate an optimal response for the user. For example, the analysis unit analyzes the user's past posts and profile information and generates an individually optimized response. In this way, the optimal response for the user can be generated by analyzing the collected information. 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 collected information to a generation AI and have the generation AI analyze the information.

[0032] The monitoring unit performs monitoring in real time and can respond immediately to important inquiries or emergency requests. The monitoring unit, for example, performs monitoring in real time and responds immediately to important inquiries or emergency requests. For example, the monitoring unit responds immediately when an emergency inquiry is received from a user. The monitoring unit can also perform monitoring in real time and respond immediately to important inquiries or emergency requests. For example, the monitoring unit monitors user activity information in real time and responds immediately to important inquiries or emergency requests. In this way, real-time monitoring can respond immediately to important inquiries or emergency requests. 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 user activity information to a generation AI and cause the generation AI to perform real-time monitoring.

[0033] The language support unit supports multiple languages ​​and can provide appropriate responses to users who speak different languages. The language support unit, for example, supports multiple languages ​​and can provide appropriate responses to users who speak different languages. For example, the language support unit supports various languages ​​such as English, Spanish, and French. The language support unit can also support multiple languages ​​and provide appropriate responses to users who speak different languages. For example, the language support unit provides a response in the most appropriate language based on the user's language setting. By supporting multiple languages, appropriate responses can be provided to 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 the user's language setting into a generation AI and cause the generation AI to execute a response in the most appropriate language.

[0034] The collection unit can analyze a user's past posts and profile information and select the optimal collection method. The collection unit, for example, analyzes a user's past posts and profile information and selects the optimal collection method. For example, the collection unit analyzes time periods during which the user frequently posted in the past and collects information during those time periods. The collection unit can also identify the user's interests from the user's profile information and collect information based on those interests. The collection unit can also analyze the content of a user's past posts and prioritize collecting related information. In this way, the optimal collection method can be selected by analyzing the user's past posts and profile information. 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 the user's past posts and profile information into a generation AI and have the generation AI select the optimal collection method.

[0035] The collection unit can filter information based on the user's current activity status and areas of interest when collecting information. For example, the collection unit can filter information based on the user's current activity status and areas of interest when collecting information. For example, the collection unit can collect information during the user's currently active time period and respond in real time. The collection unit can also collect only relevant information based on the user's areas of interest. The collection unit can also analyze the user's current activity status and collect information at the optimal timing. This makes it possible to collect highly relevant information by filtering information based on the user's current activity status and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's current activity status and areas of interest into a generation AI and have the generation AI perform information filtering.

[0036] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user posts an image, the collection unit can prioritize collecting image data. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0037] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting information around the user's home. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0038] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can collect information related to topics that the user frequently posts on social media. The collection unit can also analyze the activities of the user's friends on social media and collect related information. The collection unit can also analyze the content of the user's posts on social media and collect related information. In this way, by analyzing the user's social media activity, related information can be efficiently collected. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input the user's social media activity into a generation AI and cause the generation AI to collect information.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also select an optimal collection means from the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. In this way, the collection method can be customized by reflecting the user's past feedback, and information can be collected more effectively. Some or all of the above-mentioned 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 the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit analyzes information of high importance in detail. The analysis unit can also analyze information of low importance in a simplified manner. The analysis unit can also determine the priority of the analysis according to the importance. As a result, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the collected information. 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 importance of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. The analysis unit can also apply an image analysis algorithm to image information. The analysis unit can also apply a voice analysis algorithm to voice information. In this way, by applying different analysis algorithms depending on the category of information, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the current analysis based on the user's past analysis results. The analysis unit can also select the optimal analysis method from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority based on the time when the information was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the information was collected during analysis. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also postpone analyzing older information. The analysis unit can also adjust the order of analysis depending on the time when the information was collected. In this way, by determining the analysis priority based on the time when the information was collected, the latest information can be analyzed preferentially. 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 time when the information was collected into the generation AI and have the generation AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of information to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, the analysis unit can avoid technical terms if the user does not have technical expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. 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 user's level of expertise into the generation AI and cause the generation AI to use technical terms in the analysis.

[0046] The generation unit can adjust the level of detail of the response based on the importance of the information when generating a response. For example, the generation unit adjusts the level of detail of the response based on the importance of the information when generating a response. For example, the generation unit generates a detailed response for information of high importance. The generation unit can also generate a brief response for information of low importance. The generation unit can also determine the priority of responses according to the importance. In this way, responses can be generated efficiently by adjusting the level of detail of the response based on the importance of the information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the response.

[0047] The generation unit can apply different response algorithms depending on the category of information when generating a response. For example, the generation unit applies different response algorithms depending on the category of information when generating a response. For example, the generation unit applies a natural language generation algorithm to text information. The generation unit can also apply an image generation algorithm to image information. The generation unit can also apply a voice generation algorithm to voice information. In this way, by applying different response algorithms depending on the category of information, it is possible to generate more accurate responses. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the category of information to the generation AI and cause the generation AI to apply the response algorithm.

[0048] The generation unit can improve the accuracy of a response by referring to the user's past response results when generating a response. For example, the generation unit can improve the accuracy of a response by referring to the user's past response results when generating a response. For example, the generation unit can adjust a current response based on the user's past response results. The generation unit can also select an optimal response method from the user's past response results. The generation unit can also analyze the user's past response results and improve the response algorithm. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past response results into the generation AI and cause the generation AI to improve the accuracy of the response.

[0049] The generation unit can determine the priority of responses based on the time when the information was collected when generating a response. The generation unit, for example, determines the priority of responses based on the time when the information was collected when generating a response. For example, the generation unit generates responses preferentially based on the latest information. The generation unit can also generate responses by leaving older information for later. The generation unit can also adjust the order of responses according to the time when the information was collected. In this way, by determining the priority of responses based on the time when the information was collected, the latest information can be preferentially reflected in the response. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the information was collected into the generation AI and have the generation AI determine the priority of responses.

[0050] The generation unit can adjust the order of responses based on the relevance of the information when generating a response. The generation unit, for example, adjusts the order of responses based on the relevance of the information when generating a response. For example, the generation unit preferentially reflects highly relevant information in the response. The generation unit can also generate a response after leaving less relevant information behind. The generation unit can also adjust the order of responses according to the relevance of the information. In this way, by adjusting the order of responses based on the relevance of the information, highly relevant information can be preferentially reflected in the response. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the information to the generation AI and cause the generation AI to adjust the order of the responses.

[0051] The generation unit can adjust the use of technical terms in the response according to the user's level of expertise when generating a response. For example, the generation unit can adjust the use of technical terms in the response according to the user's level of expertise when generating a response. For example, the generation unit uses a lot of technical terms if the user has technical knowledge. The generation unit can also avoid technical terms if the user does not have technical knowledge. The generation unit can also adjust the way the response is expressed according to the user's level of expertise. This makes it possible to provide a response that is easy for the user to understand by adjusting the use of technical terms in the response according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms in the response.

[0052] When providing a response, the providing unit can select the optimal delivery method by referring to the user's past response history. For example, when providing a response, the providing unit selects the optimal delivery method by referring to the user's past response history. For example, the providing unit preferentially uses a delivery method that the user has previously preferred. The providing unit can also select the optimal delivery means from the user's past response history. The providing unit can also analyze the user's past response history and improve the delivery method. In this way, the optimal delivery method can be selected by referring to the user's past response history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past response history into the generation AI and cause the generation AI to select the optimal delivery method.

[0053] The providing unit can customize the content to be provided according to the user's current task when providing a response. For example, the providing unit customizes the content to be provided according to the user's current task when providing a response. For example, when the user is working, the providing unit provides a concise and to-the-point response. Furthermore, when the user is on a break, the providing unit can provide a response including detailed information. Furthermore, the providing unit can adjust the content of the response according to the user's current task. In this way, by customizing the content to be provided according to the user's current task, a more appropriate response can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task to a generation AI and cause the generation AI to customize the content to be provided.

[0054] The providing unit can improve the response delivery method by reflecting user feedback when providing a response. For example, the providing unit can improve the response delivery method by reflecting user feedback when providing a response. For example, the providing unit adjusts the response delivery method based on user feedback. The providing unit can also select an optimal response delivery means from user feedback. The providing unit can also analyze user feedback and improve the response delivery method. In this way, the response delivery method can be improved by reflecting user feedback, and a more effective response can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into a generation AI and cause the generation AI to improve the response delivery method.

[0055] The providing unit can select the optimal delivery method by taking into account the user's device information when providing a response. For example, the providing unit selects the optimal delivery method by taking into account the user's device information when providing a response. For example, if the user is using a smartphone, the providing unit uses a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can use a delivery method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can use a delivery method that includes detailed information. This makes it possible to select the optimal delivery method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery method.

[0056] The providing unit can provide multilingual content in accordance with the user's language setting when providing a response. For example, the providing unit can provide multilingual content in accordance with the user's language setting when providing a response. For example, the providing unit can automatically set the language of the response based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide a response in a specific language when the user selects that language. This allows appropriate responses to be provided to users globally by providing multilingual content in accordance with the user's language setting. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's language setting into a generation AI and cause the generation AI to provide multilingual content.

[0057] The providing unit can customize the response delivery method by reflecting the user's past feedback when providing a response. For example, the providing unit customizes the response delivery method by reflecting the user's past feedback when providing a response. For example, the providing unit adjusts the response delivery method based on the user's past feedback. The providing unit can also select an optimal response delivery method from the user's past feedback. The providing unit can also analyze the user's past feedback and improve the response delivery method. In this way, the response delivery method can be customized by reflecting the user's past feedback, and a more effective response can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the response delivery method.

[0058] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships of information during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships of information during monitoring. For example, the monitoring unit groups related information for monitoring. The monitoring unit can also analyze the interrelationships of information and improve the accuracy of monitoring. The monitoring unit can also determine monitoring priorities based on the interrelationships of information. In this way, the accuracy of monitoring can be improved by taking the interrelationships of information into account. 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 can input the interrelationships of information into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0059] The monitoring unit can perform monitoring while taking into consideration attribute information of the information submitter. For example, the monitoring unit performs monitoring while taking into consideration attribute information of the information submitter. For example, if the information submitter is trustworthy, the monitoring unit prioritizes monitoring of that information. Furthermore, if the information submitter is unknown, the monitoring unit can monitor that information later. Furthermore, the monitoring unit can determine the monitoring priority based on the attribute information of the information submitter. In this way, by taking into consideration the attribute information of the information submitter, more reliable information can be prioritized for monitoring. 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 attribute information of the information submitter to the generation AI and have the generation AI perform monitoring.

[0060] The monitoring unit can weight the monitoring based on the frequency of information submission during monitoring. The monitoring unit, for example, weights the monitoring based on the frequency of information submission during monitoring. For example, the monitoring unit prioritizes monitoring of information that is submitted frequently. The monitoring unit can also monitor information that is submitted less frequently later. The monitoring unit can also weight the monitoring based on the frequency of information submission. In this way, by weighting the monitoring based on the frequency of information submission, it is possible to prioritize monitoring of information that is submitted frequently. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the frequency of information submission to the generation AI and cause the generation AI to perform the monitoring weighting.

[0061] The monitoring unit can perform monitoring while taking into account the geographical distribution of information. For example, the monitoring unit performs monitoring while taking into account the geographical distribution of information. For example, the monitoring unit prioritizes monitoring of information related to a specific region. The monitoring unit can also determine the priority of monitoring based on the geographical distribution. The monitoring unit can also improve the accuracy of monitoring by taking into account the geographical distribution. In this way, by taking into account the geographical distribution of information, it is possible to prioritize monitoring of information related to a specific region. 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 the geographical distribution of information into a generation AI and have the generation AI perform monitoring.

[0062] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the information during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the information during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to related literature. The monitoring unit can also determine a monitoring priority based on the related literature. The monitoring unit can also analyze related literature and improve the accuracy of monitoring. In this way, the accuracy of monitoring can be improved by referring to literature related to the information. 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 can input literature related to the information into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0063] The monitoring unit can perform monitoring taking into consideration the market value of the information when monitoring. For example, the monitoring unit performs monitoring taking into consideration the market value of the information when monitoring. For example, the monitoring unit prioritizes monitoring of information with high market value. The monitoring unit can also monitor information with low market value later. The monitoring unit can also determine the priority of monitoring based on the market value of the information. In this way, by taking into consideration the market value of the information, it is possible to prioritize monitoring of information with high value. 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 can input the market value of the information into the generation AI and have the generation AI perform monitoring.

[0064] The response unit can select the optimal response method by referring to the user's past inquiry history when responding. For example, the response unit selects the optimal response method by referring to the user's past inquiry history when responding. For example, the response unit selects the optimal response method based on the user's past inquiry history. The response unit can also select the optimal response means from the content of the user's past inquiry. The response unit can also analyze the user's past inquiry history and improve the response method. In this way, the optimal response method can be selected by referring to the user's past inquiry history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past inquiry history into a generation AI and have the generation AI select the optimal response method.

[0065] The response unit can customize the response measures based on the user's current situation when responding. The response unit, for example, customizes the response measures based on the user's current situation when responding. For example, when the user is at work, the response unit provides a concise and to-the-point response. When the user is on a break, the response unit can also provide a response that includes detailed information. The response unit can also adjust the response measures according to the user's current situation. In this way, by customizing the response measures based on the user's current situation, a more appropriate response can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's current situation to a generation AI and cause the generation AI to customize the response measures.

[0066] The response unit can improve the response method by reflecting user feedback when responding. For example, the response unit improves the response method by reflecting user feedback when responding. For example, the response unit adjusts the response method based on user feedback. The response unit can also select the optimal response means from the user feedback. The response unit can also analyze user feedback and improve the response method. In this way, by reflecting user feedback, the response method can be improved and a more effective response can be made. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input user feedback into a generation AI and have the generation AI improve the response method.

[0067] The response unit can select the optimal response method by taking into account the user's geographical location information when responding. For example, the response unit selects the optimal response method by taking into account the user's geographical location information when responding. For example, if the user is in a specific area, the response unit can provide information related to the area. Furthermore, if the user is traveling, the response unit can provide information related to the travel destination. Furthermore, if the user is at home, the response unit can provide information about the area around the user's home. In this way, the optimal response method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal response method.

[0068] The response unit can analyze the user's social media activity and suggest a response measure when responding. For example, the response unit can analyze the user's social media activity and suggest a response measure when responding. For example, the response unit can provide information related to topics that the user frequently posts on social media. The response unit can also analyze the activity of the user's friends on social media and provide related information. The response unit can also analyze the content of the user's posts on social media and provide related information. In this way, by analyzing the user's social media activity, more appropriate response measures can be suggested. Some or all of the above-described processing in the response unit can be performed using AI, for example, or can be performed without using AI. For example, the response unit can input the user's social media activity into a generation AI and have the generation AI suggest a response measure.

[0069] The response unit can customize the response method by reflecting the user's past feedback when responding. For example, the response unit customizes the response method by reflecting the user's past feedback when responding. For example, the response unit adjusts the response method based on the user's past feedback. The response unit can also select an optimal response method from the user's past feedback. The response unit can also analyze the user's past feedback and improve the response method. In this way, by reflecting the user's past feedback, the response method can be customized and a more effective response can be performed. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past feedback into a generation AI and cause the generation AI to customize the response method.

[0070] 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 selects an optimal language support method by referring to the user's past language usage history during language support. For example, the language support unit selects an optimal language support method based on the user's past language usage history. The language support unit can also select an optimal language support method from the user's past language usage history. The language support unit can also analyze the user's past language usage history and improve the language support method. In this way, the optimal language support method can be selected by referring to the user's past language usage history. 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 past language usage history into a generation AI and cause the generation AI to select an optimal language support method.

[0071] The language support unit can customize language support means based on the user's current language setting during language support. For example, the language support unit customizes language support means based on the user's current language setting during language support. For example, the language support unit selects an optimal language support means based on the user's current language setting. The language support unit can also adjust the language support method according to the user's current language setting. The language support unit can also analyze the user's current language setting and improve the language support method. This allows for more appropriate language support by customizing the language support means based on the user's current language setting. Some or all of the above-described processing in the language support unit may be performed using, or without, AI, for example. For example, the language support unit can input the user's current language setting into a generation AI and cause the generation AI to customize the language support means.

[0072] The language support unit can improve the language support method by reflecting user feedback during language support. The language support unit, for example, improves the language support method by reflecting user feedback during language support. For example, the language support unit adjusts the language support method based on user feedback. The language support unit can also select the optimal language support means from the user feedback. The language support unit can also analyze user feedback and improve the language support method. In this way, by reflecting user feedback, the language support method can be improved and more effective language support can be performed. Some or all of the above-mentioned processing in the language support unit may be performed using AI, for example, or may be performed without using AI. For example, the language support unit can input user feedback into a generation AI and cause the generation AI to improve the language support method.

[0073] 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, the language support unit selects 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 area, the language support unit provides language support related to that area. Furthermore, if the user is traveling, the language support unit can provide language support related to the user's travel destination. Furthermore, if the user is at home, the language support unit can provide language support for the area around the user's home. In this way, the optimal language support method can be selected 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 a generation AI and cause the generation AI to select the optimal language support method.

[0074] The language support unit can analyze the user's social media activity and suggest language support means during language support. For example, the language support unit analyzes the user's social media activity and suggests language support means during language support. For example, the language support unit performs language support related to topics that the user frequently posts on social media. The language support unit can also analyze the activity of the user's friends on social media and provide related language support. The language support unit can also analyze the content of the user's social media posts and provide related language support. In this way, by analyzing the user's social media activity, more appropriate language support means can be suggested. Some or all of the above-described processing in the language support unit may be performed using AI, for example, or may be performed without using AI. For example, the language support unit can input the user's social media activity into a generation AI and cause the generation AI to suggest language support means.

[0075] The language support unit can customize the language support method by reflecting the user's past feedback during language support. For example, the language support unit customizes the language support method by reflecting the user's past feedback during language support. For example, the language support unit adjusts the language support method based on the user's past feedback. The language support unit can also select the optimal language support means from the user's past feedback. The language support unit can also analyze the user's past feedback and improve the language support method. In this way, by reflecting the user's past feedback, the language support method can be customized and more effective language support can be performed. Some or all of the above-mentioned 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 past feedback into a generation AI and cause the generation AI to customize the language support method.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The SNS auto-response system may further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit collects the user's past purchase data and provides it to the analysis unit. For example, if a user frequently purchases a particular product, the analysis unit may provide new product and sale information related to that product. Also, if a user frequently purchases products in a particular category, the analysis unit may suggest recommended products related to that category. Furthermore, the purchase history analysis unit may analyze a user's purchasing patterns and provide individually optimized promotions.

[0078] The SNS auto-response system can further include a hobby analysis unit that digs deeper into the user's hobbies and interests. The hobby analysis unit identifies the user's hobbies and interests from their posts and profile information and provides them to the analysis unit. For example, if the user is interested in a particular sport, the latest news and event information related to that sport can be provided. Also, if the user likes a particular music genre, information on new songs and artists in that genre can be provided. Furthermore, the hobby analysis unit can introduce related communities and groups based on the user's interests.

[0079] The SNS auto-response system may further include a learning monitoring unit that tracks the user's learning progress. The learning monitoring unit collects information about the user's learning and progress, and provides the collected information to the analysis unit. For example, if the user is taking a specific course, the learning monitoring unit may provide encouraging messages and study advice based on the user's progress in the course. If the user is about to take an exam, the learning monitoring unit may also provide information and resources useful for exam preparation. Furthermore, the learning monitoring unit may suggest optimal learning methods based on the user's learning style.

[0080] The SNS auto-response system can further include a location information analysis unit that utilizes the user's geographical location information. The location information analysis unit identifies the user's current location and provides it to the analysis unit. For example, if the user is in a specific area, it can provide event information and recommended spots related to that area. Also, if the user is traveling, it can provide tourist information and restaurant recommendations related to the travel destination. Furthermore, the location information analysis unit can analyze the user's movement patterns and provide optimal travel routes and transportation information.

[0081] The SNS auto-response system can further include a device analysis unit that utilizes the user's device information. The device analysis unit collects the type and settings of the device used by the user and provides the information to the analysis unit. For example, if the user is using a smartphone, a response optimized for the smartphone can be provided. Also, if the user is using a tablet, a response optimized for the tablet can be provided. Furthermore, the device analysis unit can suggest the optimal display format and notification method based on the user's device settings.

[0082] The SNS auto-response system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit collects a user's history of posts, comments, and likes and provides them to the analysis unit. For example, if a user frequently responds to a particular topic, information related to that topic may be provided. Also, if a user frequently interacts with a particular user, information related to that user may be provided. Furthermore, the social media analysis unit may analyze a user's influence on social media and generate an optimal response.

[0083] The processing flow of the first embodiment will be briefly explained below.

[0084] Step 1: The collection unit collects user activity information. User activity information includes browsing history, click history, purchase history, past posts, profile information, current activity status, areas of interest, etc. The collection unit collects information during the user's current active time period and responds in real time. Step 2: The analyzer analyzes the collected information and generates personalized responses. The analyzer generates responses that provide relevant information based on topics the user has previously shown interest in. Step 3: The generator analyzes the user's emotions and tone and generates a personalized emotional response. For example, if the user is angry, it generates a calming response, and if the user is happy, it generates an empathetic response. Step 4: The provider provides the response generated by the generator. The provider can provide the generated response in real time or in batches. Step 5: The monitoring unit performs real-time monitoring and responds immediately to important inquiries and emergency requests. For example, if there is an urgent inquiry from a user, it will respond immediately. Step 6: The response department responds immediately to important inquiries and urgent requests. For example, if there is an urgent inquiry from a user, it will be responded to immediately. Step 7: The language support section supports multiple languages ​​and provides appropriate responses to users who speak different languages, such as English, Spanish, and French.

[0085] (Example 2) An SNS auto-response system according to an embodiment of the present invention collects and analyzes user activity information, and generates and provides optimal responses. The SNS auto-response system collects and analyzes user activity information and generates optimal responses, allowing SNS operators to efficiently communicate with users even when staffing is limited. For example, the SNS auto-response system collects user activity information. For example, the SNS auto-response system collects users' past posts and profile information. Then, the SNS auto-response system analyzes the collected information and generates individually optimized responses. For example, the SNS auto-response system generates responses that provide relevant information based on topics the user has previously shown interest in. Next, the SNS auto-response system analyzes the emotions and tone of the user's posts and messages to generate emotional responses tailored to the individual. For example, if the user is angry, the system generates a calming response, and if the user is happy, the system generates an empathetic response. Next, the SNS auto-response system performs real-time monitoring and immediately responds to important inquiries and emergency requests. For example, if a user makes an urgent inquiry, the system responds immediately, improving user satisfaction. Next, the SNS auto-response system supports multiple languages ​​and provides appropriate responses to users who speak different languages. For example, by supporting various languages ​​such as English, Spanish, and French, SNS auto-response systems can accommodate global users. This allows SNS operators to resolve staff shortages and communicate with users efficiently. For example, SNS auto-response systems can quickly and accurately collect and analyze user activity information to generate optimal responses. SNS auto-response systems can also improve user satisfaction by analyzing users' emotions and tone and generating emotional responses tailored to each individual. Furthermore, SNS auto-response systems can improve user satisfaction by monitoring in real time and responding immediately to important inquiries and emergency requests. Furthermore, by supporting multiple languages, SNS auto-response systems can provide appropriate responses to global users.

[0086] An SNS automatic response system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, a monitoring unit, a response unit, and a language response unit. The collection unit collects user activity information. The user activity information includes, but is not limited to, browsing history, click history, and purchase history. The collection unit collects, for example, the user's past posts and profile information. The collection unit can also collect information based on the user's current activity status and areas of interest. For example, the collection unit collects information during the user's currently active time period and responds in real time. The analysis unit analyzes the collected information and generates an individually optimized response. For example, the analysis unit analyzes the collected information and generates an optimal response for the user. For example, the analysis unit generates a response that provides relevant information based on topics in which the user has previously shown interest. The generation unit analyzes the user's emotions and tone and generates an emotional response tailored to the user. For example, the generation unit analyzes the user's emotions and tone and generates an emotional response. For example, the generation unit generates a calming response when the user is angry, and generates a sympathetic response when the user is happy. The provision unit provides the response generated by the generation unit. The provision unit provides the generated response, for example, in real time. The provision unit can also provide the generated responses in batches. The monitoring unit performs monitoring in real time and immediately responds to important inquiries and emergency requests. The monitoring unit performs monitoring in real time and immediately responds to important inquiries and emergency requests. For example, the monitoring unit immediately responds when an emergency inquiry is made by a user. The response unit immediately responds to important inquiries and emergency requests. The response unit immediately responds to important inquiries and emergency requests. For example, the response unit immediately responds when an emergency inquiry is made by a user. The language support unit supports multiple languages ​​and provides appropriate responses to users who speak different languages. The language support unit supports multiple languages ​​and provides appropriate responses to users who speak different languages. For example, the language support unit supports various languages ​​such as English, Spanish, and French.As a result, the SNS automatic response system according to the embodiment can collect and analyze user activity information, generate and provide optimal responses, and, for example, enable SNS operators to communicate with users efficiently even when they are short-staffed.

[0087] The collection unit can collect the user's past posts or profile information. The collection unit, for example, collects the user's past posts. For example, the collection unit collects social media posts, blog articles, etc. The collection unit can also collect the user's profile information. For example, the collection unit collects profile information such as the user's name, age, and interests. By collecting the user's past posts and profile information, information for generating individually optimized responses can be obtained. Some or all of the above-mentioned 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 the user's past posts and profile information into the generation AI and cause the generation AI to collect the information.

[0088] The analysis unit can analyze the collected information and generate an individually optimized response. The analysis unit, for example, analyzes the collected information and generates an individually optimized response. For example, the analysis unit generates a response that provides relevant information based on topics in which the user has previously shown interest. The analysis unit can also analyze the collected information and generate an optimal response for the user. For example, the analysis unit analyzes the user's past posts and profile information and generates an individually optimized response. In this way, the optimal response for the user can be generated by analyzing the collected information. 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 collected information to a generation AI and have the generation AI analyze the information.

[0089] The generation unit can analyze the user's emotions or tone and generate an emotional response tailored to the user. The generation unit, for example, analyzes the user's emotions and tone and generates an emotional response. For example, if the user is angry, the generation unit generates a calming response, and if the user is happy, the generation unit generates an empathetic response. The generation unit can also analyze the user's emotions and tone and generate an emotional response tailored to the user. For example, if the user is sad, the generation unit generates a comforting response. This allows for the generation of a more human-like response by analyzing the user's emotions and tone. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotions and tone into the generation AI and cause the generation AI to generate an emotional response.

[0090] The monitoring unit performs monitoring in real time and can respond immediately to important inquiries or emergency requests. The monitoring unit, for example, performs monitoring in real time and responds immediately to important inquiries or emergency requests. For example, the monitoring unit responds immediately when an emergency inquiry is received from a user. The monitoring unit can also perform monitoring in real time and respond immediately to important inquiries or emergency requests. For example, the monitoring unit monitors user activity information in real time and responds immediately to important inquiries or emergency requests. In this way, real-time monitoring can respond immediately to important inquiries or emergency requests. 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 user activity information to a generation AI and cause the generation AI to perform real-time monitoring.

[0091] The language support unit supports multiple languages ​​and can provide appropriate responses to users who speak different languages. The language support unit, for example, supports multiple languages ​​and can provide appropriate responses to users who speak different languages. For example, the language support unit supports various languages ​​such as English, Spanish, and French. The language support unit can also support multiple languages ​​and provide appropriate responses to users who speak different languages. For example, the language support unit provides a response in the most appropriate language based on the user's language setting. By supporting multiple languages, appropriate responses can be provided to 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 the user's language setting into a generation AI and cause the generation AI to execute a response in the most appropriate language.

[0092] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during a time period when the user is relaxed. Furthermore, if the user is excited, the collection unit can collect information immediately, enabling a quick response. Furthermore, if the user is tired, the collection unit can collect information during a time period when the user is resting. Thus, by adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotions into the generation AI and cause the generation AI to adjust the timing of information collection.

[0093] The collection unit can analyze a user's past posts and profile information and select the optimal collection method. The collection unit, for example, analyzes a user's past posts and profile information and selects the optimal collection method. For example, the collection unit analyzes time periods during which the user frequently posted in the past and collects information during those time periods. The collection unit can also identify the user's interests from the user's profile information and collect information based on those interests. The collection unit can also analyze the content of a user's past posts and prioritize collecting related information. In this way, the optimal collection method can be selected by analyzing the user's past posts and profile information. 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 the user's past posts and profile information into a generation AI and have the generation AI select the optimal collection method.

[0094] The collection unit can filter information based on the user's current activity status and areas of interest when collecting information. For example, the collection unit can filter information based on the user's current activity status and areas of interest when collecting information. For example, the collection unit can collect information during the user's currently active time period and respond in real time. The collection unit can also collect only relevant information based on the user's areas of interest. The collection unit can also analyze the user's current activity status and collect information at the optimal timing. This makes it possible to collect highly relevant information by filtering information based on the user's current activity status and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's current activity status and areas of interest into a generation AI and have the generation AI perform information filtering.

[0095] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user posts an image, the collection unit can prioritize collecting image data. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0096] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, if the user is angry, the collection unit can prioritize collecting information with high urgency. Also, if the user is happy, the collection unit can prioritize collecting positive information. Also, if the user is sad, the collection unit can prioritize collecting information to comfort the user. In this way, by determining the priority of information to be collected based on the user's emotions, important 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotions into the generation AI and have the generation AI determine the priority of information.

[0097] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting information around the user's home. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0098] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can collect information related to topics that the user frequently posts on social media. The collection unit can also analyze the activities of the user's friends on social media and collect related information. The collection unit can also analyze the content of the user's posts on social media and collect related information. In this way, by analyzing the user's social media activity, related information can be efficiently collected. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input the user's social media activity into a generation AI and cause the generation AI to collect information.

[0099] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also select an optimal collection means from the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. In this way, the collection method can be customized by reflecting the user's past feedback, and information can be collected more effectively. Some or all of the above-mentioned 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 the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0100] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is angry, the analysis unit expresses the analysis results simply and clearly. If the user is happy, the analysis unit can express the analysis results in detail. If the user is sad, the analysis unit can express the analysis results in a gentle manner. This allows for adjusting the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotions into the generation AI and have the generation AI adjust the way the analysis is presented.

[0101] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit analyzes information of high importance in detail. The analysis unit can also analyze information of low importance in a simplified manner. The analysis unit can also determine the priority of the analysis according to the importance. As a result, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the collected information. 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 importance of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0102] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. The analysis unit can also apply an image analysis algorithm to image information. The analysis unit can also apply a voice analysis algorithm to voice information. In this way, by applying different analysis algorithms depending on the category of information, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0103] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the current analysis based on the user's past analysis results. The analysis unit can also select the optimal analysis method from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0104] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can shorten the analysis results. If the user is relaxed, the analysis unit can also describe the analysis results in detail. If the user is excited, the analysis unit can also present the analysis results in a visually appealing manner. By adjusting the length of the analysis based on the user's emotions, it is possible to provide optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotions into the generation AI and have the generation AI adjust the length of the analysis.

[0105] The analysis unit can determine the analysis priority based on the time when the information was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the information was collected during analysis. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also postpone analyzing older information. The analysis unit can also adjust the order of analysis depending on the time when the information was collected. In this way, by determining the analysis priority based on the time when the information was collected, the latest information can be analyzed preferentially. 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 time when the information was collected into the generation AI and have the generation AI determine the analysis priority.

[0106] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of information to the generation AI and cause the generation AI to adjust the order of analysis.

[0107] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, the analysis unit can avoid technical terms if the user does not have technical expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. 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 user's level of expertise into the generation AI and cause the generation AI to use technical terms in the analysis.

[0108] The generation unit can estimate the user's emotion and adjust the response expression style based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the response expression style based on the estimated user emotion. For example, if the user is angry, the generation unit uses a calming expression style. Furthermore, if the user is happy, the generation unit can use an empathetic expression style. Furthermore, if the user is sad, the generation unit can use a comforting expression style. This allows the response expression style to be adjusted based on the user's emotion, thereby providing a more appropriate response. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion into the generation AI and cause the generation AI to adjust the response expression style.

[0109] The generation unit can adjust the level of detail of the response based on the importance of the information when generating a response. For example, the generation unit adjusts the level of detail of the response based on the importance of the information when generating a response. For example, the generation unit generates a detailed response for information of high importance. The generation unit can also generate a brief response for information of low importance. The generation unit can also determine the priority of responses according to the importance. In this way, responses can be generated efficiently by adjusting the level of detail of the response based on the importance of the information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the response.

[0110] The generation unit can apply different response algorithms depending on the category of information when generating a response. For example, the generation unit applies different response algorithms depending on the category of information when generating a response. For example, the generation unit applies a natural language generation algorithm to text information. The generation unit can also apply an image generation algorithm to image information. The generation unit can also apply a voice generation algorithm to voice information. In this way, by applying different response algorithms depending on the category of information, it is possible to generate more accurate responses. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the category of information to the generation AI and cause the generation AI to apply the response algorithm.

[0111] The generation unit can improve the accuracy of a response by referring to the user's past response results when generating a response. For example, the generation unit can improve the accuracy of a response by referring to the user's past response results when generating a response. For example, the generation unit can adjust a current response based on the user's past response results. The generation unit can also select an optimal response method from the user's past response results. The generation unit can also analyze the user's past response results and improve the response algorithm. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past response results into the generation AI and cause the generation AI to improve the accuracy of the response.

[0112] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the response based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point response. If the user is relaxed, the generation unit can generate a longer response with detailed explanations. If the user is excited, the generation unit can generate a response with visually stimulating effects. By adjusting the length of the response based on the user's emotions, the optimal response can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotions into the generation AI and cause the generation AI to adjust the length of the response.

[0113] The generation unit can determine the priority of responses based on the time when the information was collected when generating a response. The generation unit, for example, determines the priority of responses based on the time when the information was collected when generating a response. For example, the generation unit generates responses preferentially based on the latest information. The generation unit can also generate responses by leaving older information for later. The generation unit can also adjust the order of responses according to the time when the information was collected. In this way, by determining the priority of responses based on the time when the information was collected, the latest information can be preferentially reflected in the response. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the information was collected into the generation AI and have the generation AI determine the priority of responses.

[0114] The generation unit can adjust the order of responses based on the relevance of the information when generating a response. The generation unit, for example, adjusts the order of responses based on the relevance of the information when generating a response. For example, the generation unit preferentially reflects highly relevant information in the response. The generation unit can also generate a response after leaving less relevant information behind. The generation unit can also adjust the order of responses according to the relevance of the information. In this way, by adjusting the order of responses based on the relevance of the information, highly relevant information can be preferentially reflected in the response. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the information to the generation AI and cause the generation AI to adjust the order of the responses.

[0115] The generation unit can adjust the use of technical terms in the response according to the user's level of expertise when generating a response. For example, the generation unit can adjust the use of technical terms in the response according to the user's level of expertise when generating a response. For example, the generation unit uses a lot of technical terms if the user has technical knowledge. The generation unit can also avoid technical terms if the user does not have technical knowledge. The generation unit can also adjust the way the response is expressed according to the user's level of expertise. This makes it possible to provide a response that is easy for the user to understand by adjusting the use of technical terms in the response according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms in the response.

[0116] The providing unit can estimate the user's emotions and adjust the response provision method based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the response provision method based on the estimated user emotions. For example, if the user is angry, the providing unit can provide a response in a calming tone. Also, if the user is happy, the providing unit can provide a response in an empathetic tone. Also, if the user is sad, the providing unit can provide a response in a comforting tone. This allows for adjusting the response provision method based on the user's emotions 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 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 providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotions into the generation AI and cause the generation AI to adjust the response provision method.

[0117] When providing a response, the providing unit can select the optimal delivery method by referring to the user's past response history. For example, when providing a response, the providing unit selects the optimal delivery method by referring to the user's past response history. For example, the providing unit preferentially uses a delivery method that the user has previously preferred. The providing unit can also select the optimal delivery means from the user's past response history. The providing unit can also analyze the user's past response history and improve the delivery method. In this way, the optimal delivery method can be selected by referring to the user's past response history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past response history into the generation AI and cause the generation AI to select the optimal delivery method.

[0118] The providing unit can customize the content to be provided according to the user's current task when providing a response. For example, the providing unit customizes the content to be provided according to the user's current task when providing a response. For example, when the user is working, the providing unit provides a concise and to-the-point response. Furthermore, when the user is on a break, the providing unit can provide a response including detailed information. Furthermore, the providing unit can adjust the content of the response according to the user's current task. In this way, by customizing the content to be provided according to the user's current task, a more appropriate response can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task to a generation AI and cause the generation AI to customize the content to be provided.

[0119] The providing unit can improve the response delivery method by reflecting user feedback when providing a response. For example, the providing unit can improve the response delivery method by reflecting user feedback when providing a response. For example, the providing unit adjusts the response delivery method based on user feedback. The providing unit can also select an optimal response delivery means from user feedback. The providing unit can also analyze user feedback and improve the response delivery method. In this way, the response delivery method can be improved by reflecting user feedback, and a more effective response can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into a generation AI and cause the generation AI to improve the response delivery method.

[0120] The providing unit can estimate the user's emotions and adjust the order in which responses are provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the order in which responses are provided based on the estimated user emotions. For example, if the user is angry, the providing unit can prioritize providing responses with a high level of urgency. Furthermore, if the user is happy, the providing unit can prioritize providing responses that sympathize. Furthermore, if the user is sad, the providing unit can prioritize providing responses that comfort. This allows responses to be provided in a more appropriate order by adjusting the order in which responses are provided 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 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotions into the generation AI and cause the generation AI to adjust the order in which responses are provided.

[0121] The providing unit can select the optimal delivery method by taking into account the user's device information when providing a response. For example, the providing unit selects the optimal delivery method by taking into account the user's device information when providing a response. For example, if the user is using a smartphone, the providing unit uses a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can use a delivery method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can use a delivery method that includes detailed information. This makes it possible to select the optimal delivery method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery method.

[0122] The providing unit can provide multilingual content in accordance with the user's language setting when providing a response. For example, the providing unit can provide multilingual content in accordance with the user's language setting when providing a response. For example, the providing unit can automatically set the language of the response based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide a response in a specific language when the user selects that language. This allows appropriate responses to be provided to users globally by providing multilingual content in accordance with the user's language setting. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's language setting into a generation AI and cause the generation AI to provide multilingual content.

[0123] The providing unit can customize the response delivery method by reflecting the user's past feedback when providing a response. For example, the providing unit customizes the response delivery method by reflecting the user's past feedback when providing a response. For example, the providing unit adjusts the response delivery method based on the user's past feedback. The providing unit can also select an optimal response delivery method from the user's past feedback. The providing unit can also analyze the user's past feedback and improve the response delivery method. In this way, the response delivery method can be customized by reflecting the user's past feedback, and a more effective response can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the response delivery method.

[0124] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. For example, if the user is angry, the monitoring unit can prioritize monitoring information with high urgency. Also, if the user is happy, the monitoring unit can prioritize monitoring positive information. Also, if the user is sad, the monitoring unit can prioritize monitoring comforting information. This allows for more appropriate monitoring by adjusting the monitoring criteria 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotions into the generation AI and cause the generation AI to adjust the monitoring criteria.

[0125] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships of information during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships of information during monitoring. For example, the monitoring unit groups related information for monitoring. The monitoring unit can also analyze the interrelationships of information and improve the accuracy of monitoring. The monitoring unit can also determine monitoring priorities based on the interrelationships of information. In this way, the accuracy of monitoring can be improved by taking the interrelationships of information into account. 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 can input the interrelationships of information into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0126] The monitoring unit can perform monitoring while taking into consideration attribute information of the information submitter. For example, the monitoring unit performs monitoring while taking into consideration attribute information of the information submitter. For example, if the information submitter is trustworthy, the monitoring unit prioritizes monitoring of that information. Furthermore, if the information submitter is unknown, the monitoring unit can monitor that information later. Furthermore, the monitoring unit can determine the monitoring priority based on the attribute information of the information submitter. In this way, by taking into consideration the attribute information of the information submitter, more reliable information can be prioritized for monitoring. 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 attribute information of the information submitter to the generation AI and have the generation AI perform monitoring.

[0127] The monitoring unit can weight the monitoring based on the frequency of information submission during monitoring. The monitoring unit, for example, weights the monitoring based on the frequency of information submission during monitoring. For example, the monitoring unit prioritizes monitoring of information that is submitted frequently. The monitoring unit can also monitor information that is submitted less frequently later. The monitoring unit can also weight the monitoring based on the frequency of information submission. In this way, by weighting the monitoring based on the frequency of information submission, it is possible to prioritize monitoring of information that is submitted frequently. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the frequency of information submission to the generation AI and cause the generation AI to perform the monitoring weighting.

[0128] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the order in which the monitoring results are displayed based on the estimated user emotions. For example, if the user is angry, the monitoring unit can prioritize displaying information with high urgency. Also, if the user is happy, the monitoring unit can prioritize displaying positive information. Also, if the user is sad, the monitoring unit can prioritize displaying comforting information. In this way, by adjusting the order in which the monitoring results are displayed based on the user's emotions, it is possible to provide information in a more appropriate order. 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotions into the generation AI and cause the generation AI to adjust the display order of the monitoring results.

[0129] The monitoring unit can perform monitoring while taking into account the geographical distribution of information. For example, the monitoring unit performs monitoring while taking into account the geographical distribution of information. For example, the monitoring unit prioritizes monitoring of information related to a specific region. The monitoring unit can also determine the priority of monitoring based on the geographical distribution. The monitoring unit can also improve the accuracy of monitoring by taking into account the geographical distribution. In this way, by taking into account the geographical distribution of information, it is possible to prioritize monitoring of information related to a specific region. 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 the geographical distribution of information into a generation AI and have the generation AI perform monitoring.

[0130] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the information during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the information during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to related literature. The monitoring unit can also determine a monitoring priority based on the related literature. The monitoring unit can also analyze related literature and improve the accuracy of monitoring. In this way, the accuracy of monitoring can be improved by referring to literature related to the information. 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 can input literature related to the information into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0131] The monitoring unit can perform monitoring taking into consideration the market value of the information when monitoring. For example, the monitoring unit performs monitoring taking into consideration the market value of the information when monitoring. For example, the monitoring unit prioritizes monitoring of information with high market value. The monitoring unit can also monitor information with low market value later. The monitoring unit can also determine the priority of monitoring based on the market value of the information. In this way, by taking into consideration the market value of the information, it is possible to prioritize monitoring of information with high value. 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 can input the market value of the information into the generation AI and have the generation AI perform monitoring.

[0132] The response unit can estimate the user's emotion and adjust the response method based on the estimated user's emotion. For example, the response unit can estimate the user's emotion and adjust the response method based on the estimated user's emotion. For example, if the user is angry, the response unit can respond by calming the user. Furthermore, if the user is happy, the response unit can respond by empathizing with the user. Furthermore, if the user is sad, the response unit can respond by comforting the user. This allows for a more appropriate response by adjusting the response method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response unit may be performed using an AI, for example, or without an AI. For example, the response unit can input the user's emotion into the generation AI and have the generation AI adjust the response method.

[0133] The response unit can select the optimal response method by referring to the user's past inquiry history when responding. For example, the response unit selects the optimal response method by referring to the user's past inquiry history when responding. For example, the response unit selects the optimal response method based on the user's past inquiry history. The response unit can also select the optimal response means from the content of the user's past inquiry. The response unit can also analyze the user's past inquiry history and improve the response method. In this way, the optimal response method can be selected by referring to the user's past inquiry history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past inquiry history into a generation AI and have the generation AI select the optimal response method.

[0134] The response unit can customize the response measures based on the user's current situation when responding. The response unit, for example, customizes the response measures based on the user's current situation when responding. For example, when the user is at work, the response unit provides a concise and to-the-point response. When the user is on a break, the response unit can also provide a response that includes detailed information. The response unit can also adjust the response measures according to the user's current situation. In this way, by customizing the response measures based on the user's current situation, a more appropriate response can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's current situation to a generation AI and cause the generation AI to customize the response measures.

[0135] The response unit can improve the response method by reflecting user feedback when responding. For example, the response unit improves the response method by reflecting user feedback when responding. For example, the response unit adjusts the response method based on user feedback. The response unit can also select the optimal response means from the user feedback. The response unit can also analyze user feedback and improve the response method. In this way, by reflecting user feedback, the response method can be improved and a more effective response can be made. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input user feedback into a generation AI and have the generation AI improve the response method.

[0136] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated user emotions. The response unit, for example, estimates the user's emotions and determines the priority of responses based on the estimated user emotions. For example, if the user is angry, the response unit prioritizes responses with high urgency. Furthermore, if the user is happy, the response unit can prioritize responses that show empathy. Furthermore, if the user is sad, the response unit can prioritize responses that comfort the user. In this way, by determining the priority of responses based on the user's emotions, responses can be performed in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response unit may be performed using an AI, for example, or without an AI. For example, the response unit can input the user's emotions into the generation AI and have the generation AI determine the priority of responses.

[0137] The response unit can select the optimal response method by taking into account the user's geographical location information when responding. For example, the response unit selects the optimal response method by taking into account the user's geographical location information when responding. For example, if the user is in a specific area, the response unit can provide information related to the area. Furthermore, if the user is traveling, the response unit can provide information related to the travel destination. Furthermore, if the user is at home, the response unit can provide information about the area around the user's home. In this way, the optimal response method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal response method.

[0138] The response unit can analyze the user's social media activity and suggest a response measure when responding. For example, the response unit can analyze the user's social media activity and suggest a response measure when responding. For example, the response unit can provide information related to topics that the user frequently posts on social media. The response unit can also analyze the activity of the user's friends on social media and provide related information. The response unit can also analyze the content of the user's posts on social media and provide related information. In this way, by analyzing the user's social media activity, more appropriate response measures can be suggested. Some or all of the above-described processing in the response unit can be performed using AI, for example, or can be performed without using AI. For example, the response unit can input the user's social media activity into a generation AI and have the generation AI suggest a response measure.

[0139] The response unit can customize the response method by reflecting the user's past feedback when responding. For example, the response unit customizes the response method by reflecting the user's past feedback when responding. For example, the response unit adjusts the response method based on the user's past feedback. The response unit can also select an optimal response method from the user's past feedback. The response unit can also analyze the user's past feedback and improve the response method. In this way, by reflecting the user's past feedback, the response method can be customized and a more effective response can be performed. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past feedback into a generation AI and cause the generation AI to customize the response method.

[0140] The language support unit can estimate the user's emotions and adjust the language support method based on the estimated user emotions. For example, the language support unit estimates the user's emotions and adjusts the language support method based on the estimated user emotions. For example, if the user is angry, the language support unit can use a soothing tone to support the user. Furthermore, if the user is happy, the language support unit can use an empathetic tone to support the user. Furthermore, if the user is sad, the language support unit can use a comforting tone to support the user. 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 generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the language support unit may be performed using AI, for example, or without AI. For example, the language support unit can input the user's emotions into the generation AI and cause the generation AI to adjust the language support method.

[0141] 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 selects an optimal language support method by referring to the user's past language usage history during language support. For example, the language support unit selects an optimal language support method based on the user's past language usage history. The language support unit can also select an optimal language support method from the user's past language usage history. The language support unit can also analyze the user's past language usage history and improve the language support method. In this way, the optimal language support method can be selected by referring to the user's past language usage history. 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 past language usage history into a generation AI and cause the generation AI to select an optimal language support method.

[0142] The language support unit can customize language support means based on the user's current language setting during language support. For example, the language support unit customizes language support means based on the user's current language setting during language support. For example, the language support unit selects an optimal language support means based on the user's current language setting. The language support unit can also adjust the language support method according to the user's current language setting. The language support unit can also analyze the user's current language setting and improve the language support method. This allows for more appropriate language support by customizing the language support means based on the user's current language setting. Some or all of the above-described processing in the language support unit may be performed using, or without, AI, for example. For example, the language support unit can input the user's current language setting into a generation AI and cause the generation AI to customize the language support means.

[0143] The language support unit can improve the language support method by reflecting user feedback during language support. The language support unit, for example, improves the language support method by reflecting user feedback during language support. For example, the language support unit adjusts the language support method based on user feedback. The language support unit can also select the optimal language support means from the user feedback. The language support unit can also analyze user feedback and improve the language support method. In this way, by reflecting user feedback, the language support method can be improved and more effective language support can be performed. Some or all of the above-mentioned processing in the language support unit may be performed using AI, for example, or may be performed without using AI. For example, the language support unit can input user feedback into a generation AI and cause the generation AI to improve the language support method.

[0144] The language support unit can estimate the user's emotions and determine the priority of language support based on the estimated user emotions. For example, the language support unit can estimate the user's emotions and determine the priority of language support based on the estimated user emotions. For example, if the user is angry, the language support unit can prioritize language support with high urgency. Also, if the user is happy, the language support unit can prioritize language support that shows empathy. Also, if the user is sad, the language support unit can prioritize language support that shows comfort. In this way, by determining the priority of language support based on the user's emotions, language support can be performed in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the language support unit may be performed using, for example, AI, or without AI. For example, the language support unit can input the user's emotions into the generation AI and have the generation AI determine the priority of language support.

[0145] 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, the language support unit selects 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 area, the language support unit provides language support related to that area. Furthermore, if the user is traveling, the language support unit can provide language support related to the user's travel destination. Furthermore, if the user is at home, the language support unit can provide language support for the area around the user's home. In this way, the optimal language support method can be selected 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 a generation AI and cause the generation AI to select the optimal language support method.

[0146] The language support unit can analyze the user's social media activity and suggest language support means during language support. For example, the language support unit analyzes the user's social media activity and suggests language support means during language support. For example, the language support unit performs language support related to topics that the user frequently posts on social media. The language support unit can also analyze the activity of the user's friends on social media and provide related language support. The language support unit can also analyze the content of the user's social media posts and provide related language support. In this way, by analyzing the user's social media activity, more appropriate language support means can be suggested. Some or all of the above-described processing in the language support unit may be performed using AI, for example, or may be performed without using AI. For example, the language support unit can input the user's social media activity into a generation AI and cause the generation AI to suggest language support means.

[0147] The language support unit can customize the language support method by reflecting the user's past feedback during language support. For example, the language support unit customizes the language support method by reflecting the user's past feedback during language support. For example, the language support unit adjusts the language support method based on the user's past feedback. The language support unit can also select the optimal language support means from the user's past feedback. The language support unit can also analyze the user's past feedback and improve the language support method. In this way, by reflecting the user's past feedback, the language support method can be customized and more effective language support can be performed. Some or all of the above-mentioned 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 past feedback into a generation AI and cause the generation AI to customize the language support method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, response unit, and language support unit, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect user activity information using the camera 42 and microphone 38B of the smart device 14. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates an individually optimized response. The generation unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the user's emotions and tone and generates an emotional response. The provision unit provides the response generated by the control unit 46A of the smart device 14 in real time. The monitoring unit performs real-time monitoring using the specific processing unit 290 of the data processing device 12 and immediately responds to important inquiries and emergency requests. The response unit, realized by the control unit 46A of the smart device 14, immediately responds to important inquiries and emergency requests. The language support unit, realized by the specific processing unit 290 of the data processing device 12, supports multiple languages ​​and provides appropriate responses to users who speak different languages. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, response unit, and language response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect user activity information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate an individually optimized response. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's emotions and tone to generate an emotional response. The provision unit provides the response generated by the control unit 46A of the smart glasses 214 in real time. The monitoring unit performs real-time monitoring by the specific processing unit 290 of the data processing device 12 and immediately responds to important inquiries or emergency requests. The response unit is realized by the control unit 46A of the smart glasses 214 and immediately responds to important inquiries or emergency requests. The language support unit supports multiple languages ​​using the specific processing unit 290 of the data processing device 12, and provides appropriate responses to users who speak different languages. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, response unit, and language response unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect user activity information using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate an individually optimized response. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's emotions and tone to generate an emotional response. The provision unit provides the response generated by the control unit 46A of the headset type terminal 314 in real time. The monitoring unit performs real-time monitoring by the specific processing unit 290 of the data processing device 12 and immediately responds to important inquiries and emergency requests. The response unit is realized by the control unit 46A of the headset type terminal 314 and immediately responds to important inquiries and emergency requests. The language support unit supports multiple languages ​​using the specific processing unit 290 of the data processing device 12, and provides appropriate responses to users who speak different languages. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, response unit, and language support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect user activity information using the camera 42 or microphone 238 of the robot 414. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates an individually optimized response. The generation unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the user's emotions and tone and generates an emotional response. The provision unit provides the response generated by the control unit 46A of the robot 414 in real time. The monitoring unit, performed by the specific processing unit 290 of the data processing device 12, performs real-time monitoring and immediately responds to important inquiries and emergency requests. The response unit, performed by the control unit 46A of the robot 414, immediately responds to important inquiries and emergency requests. The language support unit, supported by the specific processing unit 290 of the data processing device 12, supports multiple languages ​​and provides appropriate responses to users who speak different languages.

[0148] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0149] The SNS auto-response system may further include a health monitoring unit that monitors the user's health status. The health monitoring unit collects physiological data such as the user's heart rate and stress level and provides it to the analysis unit. For example, if the user's heart rate is high, it may infer that the user is feeling stressed and generate a response encouraging relaxation. If the user is exercising, it may also provide advice or encouraging messages about exercise. Furthermore, the health monitoring unit may analyze the user's sleep patterns and provide notifications and responses at appropriate times.

[0150] The SNS auto-response system may further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit collects the user's past purchase data and provides it to the analysis unit. For example, if a user frequently purchases a particular product, the analysis unit may provide new product and sale information related to that product. Also, if a user frequently purchases products in a particular category, the analysis unit may suggest recommended products related to that category. Furthermore, the purchase history analysis unit may analyze a user's purchasing patterns and provide individually optimized promotions.

[0151] The SNS auto-response system can further include a hobby analysis unit that digs deeper into the user's hobbies and interests. The hobby analysis unit identifies the user's hobbies and interests from their posts and profile information and provides them to the analysis unit. For example, if the user is interested in a particular sport, the latest news and event information related to that sport can be provided. Also, if the user likes a particular music genre, information on new songs and artists in that genre can be provided. Furthermore, the hobby analysis unit can introduce related communities and groups based on the user's interests.

[0152] The SNS auto-response system may further include a learning monitoring unit that tracks the user's learning progress. The learning monitoring unit collects information about the user's learning and progress, and provides the collected information to the analysis unit. For example, if the user is taking a specific course, the learning monitoring unit may provide encouraging messages and study advice based on the user's progress in the course. If the user is about to take an exam, the learning monitoring unit may also provide information and resources useful for exam preparation. Furthermore, the learning monitoring unit may suggest optimal learning methods based on the user's learning style.

[0153] The SNS auto-response system can further estimate the user's emotions and adjust the timing of the response based on the estimated user's emotions. For example, if the user is feeling stressed, the system can delay the response to give the user time to relax. If the user is excited, the system can provide an immediate response to share the user's excitement. Furthermore, if the user is sad, the system can provide a comforting response at an appropriate time. This allows for more appropriate communication by adjusting the timing of the response based on the user's emotions.

[0154] The SNS auto-response system can further include a location information analysis unit that utilizes the user's geographical location information. The location information analysis unit identifies the user's current location and provides it to the analysis unit. For example, if the user is in a specific area, it can provide event information and recommended spots related to that area. Also, if the user is traveling, it can provide tourist information and restaurant recommendations related to the travel destination. Furthermore, the location information analysis unit can analyze the user's movement patterns and provide optimal travel routes and transportation information.

[0155] The SNS auto-response system can further estimate the user's emotions and customize the content of the response based on the estimated user's emotions. For example, if the user is angry, a response with calming content can be provided. If the user is happy, a response with empathy can be provided. Furthermore, if the user is sad, a response with comforting content can be provided. In this way, by customizing the content of the response based on the user's emotions, more appropriate communication can be achieved.

[0156] The SNS auto-response system can further include a device analysis unit that utilizes the user's device information. The device analysis unit collects the type and settings of the device used by the user and provides the information to the analysis unit. For example, if the user is using a smartphone, a response optimized for the smartphone can be provided. Also, if the user is using a tablet, a response optimized for the tablet can be provided. Furthermore, the device analysis unit can suggest the optimal display format and notification method based on the user's device settings.

[0157] The SNS auto-response system can further estimate the user's emotions and adjust the format of the response based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand response can be provided. If the user is relaxed, a response containing detailed information can be provided. Furthermore, if the user is excited, a visually appealing response can be provided. This allows for more appropriate communication by adjusting the format of the response based on the user's emotions.

[0158] The SNS auto-response system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit collects a user's history of posts, comments, and likes and provides them to the analysis unit. For example, if a user frequently responds to a particular topic, information related to that topic may be provided. Also, if a user frequently interacts with a particular user, information related to that user may be provided. Furthermore, the social media analysis unit may analyze a user's influence on social media and generate an optimal response.

[0159] The processing flow of the second embodiment will be briefly explained below.

[0160] Step 1: The collection unit collects user activity information. User activity information includes browsing history, click history, purchase history, past posts, profile information, current activity status, areas of interest, etc. The collection unit collects information during the user's current active time period and responds in real time. Step 2: The analyzer analyzes the collected information and generates personalized responses. The analyzer generates responses that provide relevant information based on topics the user has previously shown interest in. Step 3: The generator analyzes the user's emotions and tone and generates a personalized emotional response. For example, if the user is angry, it generates a calming response, and if the user is happy, it generates an empathetic response. Step 4: The provider provides the response generated by the generator. The provider can provide the generated response in real time or in batches. Step 5: The monitoring unit performs real-time monitoring and responds immediately to important inquiries and emergency requests. For example, if there is an urgent inquiry from a user, it will respond immediately. Step 6: The response department responds immediately to important inquiries and urgent requests. For example, if there is an urgent inquiry from a user, it will be responded to immediately. Step 7: The language support section supports multiple languages ​​and provides appropriate responses to users who speak different languages, such as English, Spanish, and French.

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] 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.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0198] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0212] 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.

[0213] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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."

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Explanation of symbols]

[0233] 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 activity information; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a response based on the information analyzed by the analysis unit; a providing unit that provides the response generated by the generating unit; A monitoring unit that performs real-time monitoring; A response department that responds immediately to important inquiries or emergency requests; A language support unit that supports multiple languages. A system characterized by:

2. The collecting unit Collecting users' past posts or profile information 2. The system of claim 1.

3. The analysis unit Analyze the collected information and generate personalized responses 2. The system of claim 1.

4. The generation unit Analyzes the user's emotion or tone and generates a tailored emotional response 2. The system of claim 1.

5. The monitoring unit Real-time monitoring and immediate response to critical inquiries or emergency requests 2. The system of claim 1.

6. The language support unit Supports multiple languages ​​and provides appropriate responses for users who speak different languages 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past posts and profile information to select the optimal collection method 2. The system of claim 1.

9. The collecting unit When collecting information, filter it based on the user's current activity and interests.

2. The system of claim 1.

10. The collecting unit When collecting information, select the optimal collection method according to the user's input method.

2. The system of claim 1.

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