system

Abstract

The system according to this embodiment aims to provide immediate response and continuous support by recognizing the user's emotions and providing customized responses tailored to individual situations. [Solution] The system according to the embodiment comprises a reception unit, an emotion recognition unit, a customization response unit, and a support unit. The reception unit receives inquiries from users. The emotion recognition unit recognizes the user's emotions based on the inquiries received by the reception unit. The customization response unit provides customized responses tailored to the individual user's situation based on the emotions recognized by the emotion recognition unit. The support unit provides immediate responses and ongoing support based on the customized responses provided by the customization response unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to make an appropriate response according to the user's emotions, and there is room for improvement in stress reduction and emotional support.

[0005] The system according to an embodiment aims to provide an immediate response and continuous support by recognizing the user's emotions and making a customized response according to individual situations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an emotion recognition unit, a customization unit, and a support unit. The reception unit receives inquiries from users. The emotion recognition unit recognizes the user's emotions based on the inquiries received by the reception unit. The customization unit provides customized support tailored to the individual user's situation based on the emotions recognized by the emotion recognition unit. The support unit provides immediate responses and ongoing support based on the customized support provided by the customization unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide immediate response and continuous support by recognizing the user's emotions and providing customized responses tailored to individual situations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​chatbot system according to an embodiment of the present invention is a system that responds to the stress and worries of individual users 24 hours a day. This system allows users to easily consult at any time and receive stress reduction and emotional support. Specifically, it consists of the following steps. First, the user accesses the chatbot and inputs the content of their consultation. Next, the AI ​​uses natural language processing to recognize the user's emotions and provides customized support tailored to the individual user's situation. The AI ​​provides immediate responses and continuous support to reduce the user's stress level. For example, the user accesses the chatbot and inputs the content of their consultation. At this time, the user can consult anonymously and their privacy is protected. For example, if the user inputs "I'm stressed out at work," the AI ​​analyzes the content. Next, the AI ​​uses natural language processing to recognize the user's emotions. The AI ​​analyzes the emotions from the user's input and determines the user's stress level. For example, if the user inputs "I'm stressed out at work," the AI ​​recognizes that the user is feeling stressed. The AI ​​provides customized support tailored to the individual user's situation. For example, if the user inputs "I'm stressed out at work," the AI ​​provides advice such as "Try taking some deep breaths to relax." Furthermore, the AI ​​learns user behavior patterns and evolves its interactions to meet user needs. The AI ​​provides instant responses and continuous support. When a user enters their question, the AI ​​responds immediately and provides appropriate support for the user's concerns. For example, if a user enters "I'm stressed out at work," the AI ​​immediately provides advice such as "Try taking some deep breaths to relax." Also, if the user consults again, the AI ​​remembers the content of the previous consultation and provides continuous support. This system allows users to easily consult anytime and receive stress reduction and emotional support. For example, this service is extremely useful for people who are too busy to go to face-to-face counseling or who want to consult anonymously and casually.Furthermore, AI-powered real-time sentiment analysis technology and algorithms that learn user behavior patterns enable evolving interactions tailored to user needs, improving service satisfaction. As a result, the AI ​​chatbot system can recognize emotions in response to user inquiries and provide customized responses and support, reducing stress and offering emotional support.

[0029] The AI ​​chatbot system according to this embodiment comprises a reception unit, an emotion recognition unit, a customization unit, and a support unit. The reception unit receives inquiries from users. For example, the reception unit allows users to access the chatbot and input their inquiries. The reception unit allows users to consult anonymously, ensuring their privacy is protected. For example, if a user inputs "I'm stressed out at work," the reception unit receives this information. The emotion recognition unit recognizes the user's emotions based on the inquiries received by the reception unit. For example, the emotion recognition unit uses natural language processing to recognize the user's emotions. The emotion recognition unit analyzes the user's input to determine their stress level. For example, if a user inputs "I'm stressed out at work," the emotion recognition unit recognizes that the user is feeling stressed. The customization unit provides customized support tailored to the individual user's situation based on the emotions recognized by the emotion recognition unit. For example, if a user inputs "I'm stressed out at work," the customization unit provides advice such as "Try taking some deep breaths to relax." The customization unit learns user behavior patterns and evolves interactions to meet user needs. The support unit provides immediate responses and ongoing support based on the customization provided by the customization unit. For example, when a user inputs a question, the support unit responds immediately and provides appropriate support for the user's concerns. For example, if a user inputs "I'm stressed out at work," the support unit immediately provides advice such as "Try taking some deep breaths to relax." If the user consults again, the support unit remembers the content of the previous consultation and provides ongoing support. As a result, the AI ​​chatbot system according to this embodiment can reduce stress and provide emotional support by recognizing emotions in response to the user's questions and providing customized responses and support.

[0030] The reception department receives inquiries from users. For example, users can access the chatbot and input their inquiries. The reception department allows users to consult anonymously, protecting their privacy. Specifically, when a user accesses the chatbot, an anonymous user ID is automatically generated, and a system is in place to protect the user's personal information. Users can freely input their inquiries into the chat window in text format. For example, if a user inputs "I'm stressed out at work," the reception department receives that information. The reception department has the function to analyze the input text in real time and route it to the appropriate department. Furthermore, the reception department automatically categorizes the user's input and sends it to the emotion recognition department and the customization support department. This ensures that the user's inquiries are processed quickly and accurately. The reception department can also save the user's input as a log and use it for subsequent support. In this way, the reception department can receive inquiries quickly and accurately while protecting the user's privacy.

[0031] The emotion recognition unit recognizes the user's emotions based on the consultation content received by the reception unit. The emotion recognition unit uses, for example, AI to recognize the user's emotions using natural language processing. Specifically, it uses natural language processing technology to analyze emotions from the user's input. The emotion recognition unit tokenizes the user's input and calculates an emotion score for each token. For example, for the input "I'm stressed out at work," it breaks it down into tokens such as "work," "stress," and "stressed," and assigns an emotion score to each token. The emotion scores are classified into categories such as positive, negative, and neutral, and an overall emotion score is calculated. Based on these scores, the emotion recognition unit determines the user's emotions. For example, if a user inputs "I'm stressed out at work," the emotion recognition unit recognizes that the user is feeling stressed. Furthermore, the emotion recognition unit can refer to the user's past consultation content and behavioral history to analyze changes and trends in emotions. This allows the emotion recognition unit to accurately recognize the user's emotions and provide basic information for appropriate responses.

[0032] The customized support unit provides customized responses tailored to each user's situation based on the emotions recognized by the emotion recognition unit. For example, if a user inputs "I'm stressed out from work," the customized support unit will provide advice such as "Try taking some deep breaths to relax." Specifically, the customized support unit generates the optimal response according to the user's situation based on the emotional data received from the emotion recognition unit. The customized support unit learns the user's preferences and tendencies by referring to the user's past consultation content and behavioral history. For example, if a user has previously provided feedback that "deep breathing was effective," the customized support unit will recommend deep breathing again. Furthermore, the customized support unit can provide different responses depending on the user's emotional state. For example, if a user is experiencing high levels of stress, it may not only suggest relaxation methods but also recommend consulting a professional. In addition, the customized support unit collects user feedback and continuously improves the accuracy and effectiveness of its responses. This allows the customized support unit to provide flexible and effective responses that meet the user's needs.

[0033] The support department provides immediate responses and ongoing support based on the customized solutions provided by the customized solutions department. For example, when a user enters their inquiry, the support department responds immediately and provides appropriate support for the user's concerns. Specifically, the support department immediately conveys the advice provided by the customized solutions department to the user. For example, if a user enters "I'm stressed out at work," the support department immediately provides advice such as "Try taking some deep breaths to relax." If a user consults the support department again, the support department remembers the content of the previous consultation and provides ongoing support. For example, if a user consults the support department again saying "I'm stressed out," the support department refers to the content of the previous consultation and checks whether the previous advice was effective. Furthermore, the support department collects user feedback and continuously improves the accuracy and effectiveness of its responses. For example, if a user provides feedback that "deep breathing was effective," the support department records this information and uses it for future responses. The support department can also save the user's consultation content as a log and use it for subsequent support. This allows the support department to provide prompt and appropriate support for users' concerns, and to reduce user stress and provide emotional support through continuous support.

[0034] The emotion recognition unit can analyze user emotions from user input and determine the user's stress level. For example, the emotion recognition unit can analyze user emotions using text analysis. The emotion recognition unit analyzes user emotions from user input and determines the stress level. For example, if the user inputs "I'm stressed out from work," the emotion recognition unit recognizes that the user is feeling stressed. The emotion recognition unit can also analyze user emotions using voice analysis. For example, the emotion recognition unit analyzes the tone and speed of the user's voice and determines the emotion. The emotion recognition unit can also analyze user emotions using facial expression analysis. For example, the emotion recognition unit analyzes changes in the user's facial expression and determines the emotion. This allows for appropriate responses by analyzing emotions from user input and determining the stress level. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input user input into a generative AI and have the generative AI perform the emotion analysis.

[0035] The customization unit can learn user behavior patterns and evolve interactions to meet user needs. For example, the customization unit can learn user behavior patterns using machine learning algorithms. The customization unit learns user behavior patterns and provides interactions that meet those needs. For example, the customization unit can collect past user behavior data and learn behavior patterns. The customization unit can also learn user behavior patterns using data collection methods. For example, the customization unit can collect user behavior data in real time and learn behavior patterns. The customization unit can also evolve interactions based on user feedback. For example, the customization unit collects user feedback and evolves the types of interactions. By learning user behavior patterns, it can provide interactions that meet those needs. Some or all of the above processing in the customization unit may be performed using, for example, generative AI, or without using generative AI. For example, the customization unit can input user behavior data into a generative AI and have the generative AI perform behavior pattern learning.

[0036] The support unit can remember the content of a previous consultation when a user consults again, enabling it to provide continuous support. The support unit can, for example, use a database to remember the content of the previous consultation. The support unit can also remember the content of a previous consultation when a user consults again, enabling it to provide continuous support. For example, the support unit can save the user's consultation content in a database and refer to it when the user consults again. The support unit can also set a memory period for remembering consultation content. For example, the support unit can remember consultation content within a certain period and delete consultation content after that period has passed. The support unit can also set a memory range for consultation content. For example, the support unit can remember only certain types of consultation content and not other types. This makes continuous support possible by remembering the content of the previous consultation. Some or all of the above processing in the support unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the support unit can input the user's consultation content into a generation AI and have the generation AI remember the consultation content.

[0037] The reception desk can enable users to consult anonymously. The reception desk can implement anonymous consultations, for example, by using methods to protect user information. The reception desk can enable users to consult anonymously. For example, the reception desk can protect the user's personal information and ensure anonymity. The reception desk can also accept consultations after setting up methods to ensure anonymity. For example, the reception desk can anonymize the user's IP address and accept the consultation content. The reception desk can also implement anonymous consultations by setting up methods to protect user information. For example, the reception desk can encrypt the user's personal information and ensure anonymity. This protects the user's privacy by enabling them to consult anonymously. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's personal information into a generative AI and have the generative AI perform the anonymity assurance.

[0038] The emotion recognition unit can utilize AI-based real-time emotion analysis technology. For example, the emotion recognition unit recognizes the user's emotions using real-time emotion analysis technology. The emotion recognition unit utilizes AI-based real-time emotion analysis technology. For example, the emotion recognition unit analyzes emotions in real time using an AI algorithm. The emotion recognition unit can also perform real-time emotion analysis using data processing methods. For example, the emotion recognition unit processes the user's input data in real time and recognizes emotions. The emotion recognition unit can also perform real-time emotion analysis by setting the algorithm to be used. For example, the emotion recognition unit analyzes emotions using a specific algorithm and outputs the results in real time. This improves the accuracy of emotion recognition by utilizing real-time emotion analysis technology. Some or all of the above-described processes in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input user input data into a generative AI and have the generative AI perform real-time emotion analysis.

[0039] The customization unit can utilize algorithms to learn user behavior patterns. For example, the customization unit can learn user behavior patterns using a machine learning algorithm. The customization unit can utilize algorithms to learn user behavior patterns. For example, the customization unit can learn behavior patterns using a specific algorithm. The customization unit can also learn behavior patterns using a data collection method. For example, the customization unit can collect user behavior data and learn it using an algorithm. The customization unit can also learn behavior patterns by setting the type of algorithm. For example, the customization unit can learn behavior patterns using a specific machine learning algorithm and provide interactions that meet user needs. This improves the accuracy of customization by utilizing algorithms to learn behavior patterns. Some or all of the above processing in the customization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the customization unit can input user behavior data into a generative AI and have the generative AI perform behavior pattern learning.

[0040] The reception department can analyze a user's past consultation history and select the most suitable reception method. The reception department can analyze past consultation history using, for example, data analysis techniques. The reception department analyzes the user's past consultation history and selects the most suitable reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. The reception department can also suggest the most suitable reception method for a specific time period based on the user's past consultation history. For example, the reception department can select the most suitable reception method based on the content of the user's past consultations. The reception department can also analyze past consultation history using analytical algorithms. For example, the reception department can analyze the user's past consultation data and select the most suitable reception method. In this way, the most suitable reception method can be selected by analyzing past consultation history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception department can input the user's past consultation data into a generative AI and have the generative AI perform the analysis of the consultation history.

[0041] The reception unit can filter consultation requests based on the user's current living situation and areas of interest. For example, the reception unit can filter consultation requests using a filtering algorithm. The reception unit can filter consultation requests based on the user's current living situation and areas of interest. For example, the reception unit can prioritize receiving consultation requests relevant to the user's current living situation. The reception unit can also filter consultation requests relevant to the user's areas of interest. For example, the reception unit can suggest the most suitable consultation request based on the user's living situation and areas of interest. The reception unit can also filter consultation requests by setting filtering criteria. For example, the reception unit can filter consultation requests based on specific criteria and prioritize receiving highly relevant requests. This allows the reception unit to prioritize receiving relevant consultation requests by filtering based on living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input data on the user's living situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0042] The reception department can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location information. For example, the reception department can filter inquiries using geographical location information. The reception department can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location information. For example, the reception department prioritizes receiving relevant inquiries based on the user's geographical location information. The reception department can also suggest the most suitable inquiries, taking into account the user's geographical location information. For example, the reception department prioritizes receiving region-specific inquiries based on the user's geographical location information. The reception department can also filter inquiries by setting filtering criteria. For example, the reception department filters inquiries based on specific criteria and prioritizes receiving highly relevant content. This allows for the priority of receiving highly relevant inquiries by considering geographical location information. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception department can input the user's geographical location information into a generative AI and have the generative AI perform the filtering.

[0043] The reception department can analyze the user's social media activity when receiving inquiries and accept relevant inquiries. For example, the reception department can filter inquiries using a social media analysis algorithm. The reception department can analyze the user's social media activity when receiving inquiries and accept relevant inquiries. For example, the reception department can prioritize accepting relevant inquiries based on the user's social media activity. The reception department can also analyze the user's social media activity and suggest the most suitable inquiries. For example, the reception department can accept inquiries related to the user's interests based on their social media activity. The reception department can also filter inquiries by setting filtering criteria. For example, the reception department can filter inquiries based on specific criteria and prioritize accepting highly relevant content. This allows for the priority acceptance of relevant inquiries by analyzing social media activity. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's social media data into a generative AI and have the generative AI perform an analysis of social media activity.

[0044] The emotion recognition unit can optimize its recognition algorithm by referring to the user's past emotional data during emotion recognition. For example, the emotion recognition unit can optimize its recognition algorithm using past emotional data. The emotion recognition unit can optimize its recognition algorithm by referring to the user's past emotional data during emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion recognition by referring to the user's past emotional data. The emotion recognition unit can also optimize its recognition algorithm based on the user's past emotional data. For example, the emotion recognition unit can analyze past emotional data and adjust the parameters of the recognition algorithm. The emotion recognition unit can also optimize its recognition algorithm using data feedback. For example, the emotion recognition unit can feed back the user's past emotional data and optimize the algorithm. This allows the recognition algorithm to be optimized by referring to past emotional data. Some or all of the above processes in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's past emotional data into a generative AI and have the generative AI perform the optimization of the recognition algorithm.

[0045] The emotion recognition unit can analyze emotions while considering the context of the user's input. For example, the emotion recognition unit analyzes emotions using a contextual analysis algorithm. The emotion recognition unit analyzes emotions while considering the context of the user's input. For example, the emotion recognition unit accurately analyzes emotions while considering the context of the user's input. The emotion recognition unit can also improve the accuracy of emotion recognition based on the context of the user's input. For example, the emotion recognition unit analyzes emotions while considering the surrounding sentences and related topics. The emotion recognition unit can also analyze emotions by setting the type of context. For example, the emotion recognition unit analyzes emotions based on a specific context to improve accuracy. This allows for accurate emotion analysis by considering the context of the input. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's input into a generative AI and have the generative AI perform sentiment analysis that considers the context.

[0046] The emotion recognition unit can analyze emotions while considering the user's geographical distribution. For example, the emotion recognition unit can use an algorithm that analyzes emotions using geographical distribution. The emotion recognition unit analyzes emotions while considering the user's geographical distribution. For example, the emotion recognition unit accurately analyzes emotions based on the user's geographical distribution. The emotion recognition unit can also improve the accuracy of emotion recognition by considering the user's geographical distribution. For example, the emotion recognition unit analyzes emotions based on the user's location information. The emotion recognition unit can also analyze emotions by setting the type of geographical distribution. For example, the emotion recognition unit analyzes emotions while considering the emotional trends of each specific region. This allows for accurate analysis of emotions by considering geographical distribution. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's location information into a generative AI and have the generative AI perform an emotion analysis that considers geographical distribution.

[0047] The emotion recognition unit can improve the accuracy of emotion recognition by referring to the user's relevant literature during emotion recognition. For example, the emotion recognition unit uses an algorithm that improves the accuracy of emotion recognition using relevant literature. The emotion recognition unit can also improve the accuracy of emotion recognition by referring to the user's relevant literature during emotion recognition. For example, the emotion recognition unit improves the accuracy of emotion recognition by referring to the user's relevant literature. The emotion recognition unit can also optimize the emotion recognition algorithm based on the user's relevant literature. For example, the emotion recognition unit analyzes the relevant literature and adjusts the accuracy of emotion recognition. The emotion recognition unit can also improve the accuracy of emotion recognition by setting the type of literature. For example, the emotion recognition unit optimizes the emotion recognition algorithm based on specific literature. As a result, the accuracy of emotion recognition is improved by referring to relevant literature. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's relevant literature into a generative AI and have the generative AI perform the emotion recognition accuracy improvement.

[0048] The customization support unit can analyze the user's past behavior patterns and select the optimal support method when providing customization support. For example, the customization support unit can analyze past behavior patterns using data analysis techniques. The customization support unit can also improve the accuracy of customization support based on the user's past behavior patterns. For example, the customization support unit can propose the optimal support method by referring to the user's past behavior patterns. The customization support unit can also analyze past behavior patterns using analytical algorithms. For example, the customization support unit analyzes the user's past behavior data and selects the optimal support method. This allows for the selection of the optimal support method by analyzing past behavior patterns. Some or all of the above-described processes in the customization support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the customization support unit can input the user's past behavior data into a generative AI and have the generative AI perform the behavior pattern analysis.

[0049] The customization unit can customize the means of response based on the user's current living situation when providing customization support. For example, the customization unit utilizes an algorithm that customizes the means of response considering the user's living situation. The customization unit customizes the means of response based on the user's current living situation when providing customization support. For example, the customization unit provides the optimal means of response based on the user's current living situation. The customization unit can also improve the accuracy of the customization support by considering the user's current living situation. For example, the customization unit analyzes the user's current living situation and proposes the optimal means of response. The customization unit can also customize the means of response by setting the type of living situation. For example, the customization unit customizes the means of response based on a specific living situation to improve accuracy. This allows for more appropriate responses by customizing the means of response based on the current living situation. Some or all of the above-described processes in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input the user's living situation data into a generative AI and have the generative AI perform the customization of the means of response.

[0050] The customization support unit can select the optimal support method when performing customization, taking into account the user's geographical location information. For example, the customization support unit may use an algorithm that selects a support method using geographical location information. The customization support unit selects the optimal support method when performing customization, taking into account the user's geographical location information. For example, the customization support unit selects the optimal support method based on the user's geographical location information. The customization support unit can also improve the accuracy of customization by considering the user's geographical location information. For example, the customization support unit analyzes the user's geographical location information and proposes the optimal support method. The customization support unit can also select a support method by setting the type of geographical location information. For example, the customization support unit selects a support method based on specific geographical location information to improve accuracy. This allows for the selection of the optimal support method by considering geographical location information. Some or all of the above-described processes in the customization support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization support unit can input the user's geographical location information into a generative AI and have the generative AI perform the selection of a support method.

[0051] The customization support unit can analyze the user's social media activity and propose a response method when providing customization support. For example, the customization support unit can propose a response method using a social media analysis algorithm. The customization support unit can also analyze the user's social media activity and propose a response method when providing customization support. For example, the customization support unit can propose the optimal response method based on the user's social media activity. The customization support unit can also analyze the user's social media activity to improve the accuracy of the customization support. For example, the customization support unit can refer to the user's social media activity to provide the optimal response method. The customization support unit can also analyze social media activity by setting analysis criteria. For example, the customization support unit can analyze social media activity based on specific criteria and propose the optimal response method. In this way, the optimal response method can be proposed by analyzing social media activity. Some or all of the above processing in the customization support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization support unit can input the user's social media data into a generative AI and have the generative AI perform an analysis of social media activity.

[0052] The support department can select the optimal support method by referring to the user's past consultations during support. For example, the support department can analyze past consultations using data analysis techniques. The support department selects the optimal support method by referring to the user's past consultations during support. For example, the support department selects the optimal support method by referring to the user's past consultations. The support department can also improve the accuracy of support based on the user's past consultations. For example, the support department analyzes the user's past consultations and proposes the optimal support method. The support department can also analyze past consultations using analytical algorithms. For example, the support department analyzes the user's past consultation data and selects the optimal support method. This allows the optimal support method to be selected by referring to past consultations. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without using a generative AI. For example, the support department can input the user's past consultation data into a generative AI and have the generative AI perform an analysis of the consultation content.

[0053] The support unit can customize the means of support based on the user's current living situation during support. For example, the support unit may use an algorithm that customizes the means of support considering the living situation. The support unit customizes the means of support based on the user's current living situation during support. For example, the support unit provides the optimal means of support based on the user's current living situation. The support unit can also improve the accuracy of support by considering the user's current living situation. For example, the support unit analyzes the user's current living situation and proposes the optimal means of support. The support unit can also customize the means of support by setting the type of living situation. For example, the support unit customizes the means of support based on a specific living situation to improve accuracy. This makes it possible to provide more appropriate support by customizing the means of support based on the current living situation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the support unit can input the user's living situation data into a generative AI and have the generative AI perform the customization of the means of support.

[0054] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, the support unit may use an algorithm that selects a support method using geographical location information. The support unit selects the optimal support method by considering the user's geographical location information during support. For example, the support unit selects the optimal support method based on the user's geographical location information. The support unit can also improve the accuracy of support by considering the user's geographical location information. For example, the support unit analyzes the user's geographical location information and proposes the optimal support method. The support unit can also select a support method by setting the type of geographical location information. For example, the support unit selects a support method based on specific geographical location information to improve accuracy. This allows the optimal support method to be selected by considering geographical location information. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI and have the generative AI perform the selection of a support method.

[0055] The support unit can analyze the user's social media activity and propose support measures during support. For example, the support unit can propose support measures using a social media analysis algorithm. The support unit can also analyze the user's social media activity and propose support measures during support. For example, the support unit can propose the optimal support measures based on the user's social media activity. The support unit can also analyze the user's social media activity to improve the accuracy of support. For example, the support unit can refer to the user's social media activity to provide the optimal support measures. The support unit can also analyze social media activity by setting analysis criteria. For example, the support unit can analyze social media activity based on specific criteria and propose the optimal support measures. In this way, the optimal support measures can be proposed by analyzing social media activity. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's social media data into a generative AI and have the generative AI perform an analysis of social media activity.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can refer to a user's past consultation history when receiving a user's inquiry, enabling it to provide a more appropriate response. For example, if a user previously consulted about "work-related stress," the reception desk can refer to that history and provide advice based on past responses when a similar inquiry is submitted again. The reception desk can also analyze a user's past consultation history and identify specific patterns to provide customized responses tailored to the user's needs. For example, if a user frequently consults about "work-related stress," the reception desk can recognize this pattern and prioritize providing information related to stress management. Furthermore, the reception desk can learn what kind of advice a user prefers based on their past consultation history, enabling it to provide more personalized responses. For example, if a user previously preferred and accepted the advice "try taking deep breaths to relax," the reception desk can provide similar advice again.

[0058] The emotion recognition unit can analyze user input to determine emotions, referencing the user's past emotional data to achieve more accurate emotion recognition. For example, if a user previously entered "I'm stressed at work," the emotion recognition unit can record this data, allowing for faster and more accurate emotion recognition when similar input is made again. The emotion recognition unit can also analyze the user's past emotional data and identify specific emotional patterns to predict changes in the user's emotions. For instance, if a user frequently experiences "stress," the emotion recognition unit can recognize this pattern and predict situations in which the user is likely to experience stress. Furthermore, the emotion recognition unit can optimize its emotion recognition algorithm based on the user's past emotional data. For example, it can adjust the parameters of the emotion recognition algorithm based on data from when the user previously experienced "stress," thereby improving the accuracy of emotion recognition.

[0059] The customized support unit can learn user behavior patterns and analyze user social media activity to provide more appropriate responses. For example, if a user frequently posts about "work stress" on social media, the customized support unit can use that information to provide stress management advice. The customized support unit can also analyze user social media activity and provide responses based on user interests. For example, if a user posts about "relaxation methods" on social media, the customized support unit can use that information to provide relaxation methods. Furthermore, the customized support unit can predict user behavior patterns based on user social media activity and provide more personalized responses. For example, if a user frequently posts about "work stress" on social media, the customized support unit can recognize that pattern and prioritize providing stress management-related information.

[0060] The support department can provide more appropriate support by considering the user's geographical location when receiving inquiries. For example, if a user lives in a specific area, the support department can provide information relevant to that area. Furthermore, based on the user's geographical location, the support department can provide support that takes into account region-specific stressors. For example, if a user lives in an urban area, the support department can provide advice on urban-specific stressors. Additionally, based on the user's geographical location, the support department can provide support that utilizes local resources. For example, if a user lives in a specific area, the support department can provide information on local counseling services and support groups.

[0061] The reception desk can take into account the user's current living situation when receiving inquiries, enabling them to provide more appropriate responses. For example, if a user consults about "work-related stress," the reception desk can consider the user's current living situation and provide advice on work-related stress. The reception desk can also customize responses based on the user's current living situation to meet their needs. For example, if a user consults about "stress from family problems," the reception desk can use that information to provide advice on family problems. Furthermore, the reception desk can predict what kind of support the user needs based on their current living situation and provide more personalized responses. For example, if a user is experiencing stress from both "work-related stress" and "family problems," the reception desk can use that information to provide advice on both issues.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives inquiries from users. For example, a user can access the chatbot and enter their inquiry. Users can consult anonymously, and their privacy is protected. For example, if a user enters "I'm stressed out at work," the reception desk will receive that information. Step 2: The emotion recognition unit recognizes the user's emotions based on the consultation content received by the reception unit. For example, AI uses natural language processing to recognize the user's emotions. The emotion recognition unit analyzes the emotions from the user's input and determines the user's stress level. For example, if the user inputs "I'm stressed out from work," the emotion recognition unit recognizes that the user is feeling stressed. Step 3: The customization unit provides customized responses tailored to each user's situation based on the emotions recognized by the emotion recognition unit. For example, if a user inputs "I'm stressed out from work," the customization unit will provide advice such as "Try taking some deep breaths to relax." The customization unit learns the user's behavior patterns and evolves its interactions to meet the user's needs. Step 4: The support department provides immediate responses and ongoing support based on the customized solutions provided by the customized solutions department. For example, when a user enters their inquiry, the support department responds immediately and provides appropriate support for the user's concerns. For instance, if a user enters "I'm stressed out at work," the support department immediately provides advice such as "Try taking some deep breaths to relax." If the user contacts the support department again, the support department remembers the content of the previous inquiry and provides ongoing support.

[0064] (Example of form 2) The AI ​​chatbot system according to an embodiment of the present invention is a system that responds to the stress and worries of individual users 24 hours a day. This system allows users to easily consult at any time and receive stress reduction and emotional support. Specifically, it consists of the following steps. First, the user accesses the chatbot and inputs the content of their consultation. Next, the AI ​​uses natural language processing to recognize the user's emotions and provides customized support tailored to the individual user's situation. The AI ​​provides immediate responses and continuous support to reduce the user's stress level. For example, the user accesses the chatbot and inputs the content of their consultation. At this time, the user can consult anonymously and their privacy is protected. For example, if the user inputs "I'm stressed out at work," the AI ​​analyzes the content. Next, the AI ​​uses natural language processing to recognize the user's emotions. The AI ​​analyzes the emotions from the user's input and determines the user's stress level. For example, if the user inputs "I'm stressed out at work," the AI ​​recognizes that the user is feeling stressed. The AI ​​provides customized support tailored to the individual user's situation. For example, if the user inputs "I'm stressed out at work," the AI ​​provides advice such as "Try taking some deep breaths to relax." Furthermore, the AI ​​learns user behavior patterns and evolves its interactions to meet user needs. The AI ​​provides instant responses and continuous support. When a user enters their question, the AI ​​responds immediately and provides appropriate support for the user's concerns. For example, if a user enters "I'm stressed out at work," the AI ​​immediately provides advice such as "Try taking some deep breaths to relax." Also, if the user consults again, the AI ​​remembers the content of the previous consultation and provides continuous support. This system allows users to easily consult anytime and receive stress reduction and emotional support. For example, this service is extremely useful for people who are too busy to go to face-to-face counseling or who want to consult anonymously and casually.Furthermore, AI-powered real-time sentiment analysis technology and algorithms that learn user behavior patterns enable evolving interactions tailored to user needs, improving service satisfaction. As a result, the AI ​​chatbot system can recognize emotions in response to user inquiries and provide customized responses and support, reducing stress and offering emotional support.

[0065] The AI ​​chatbot system according to this embodiment comprises a reception unit, an emotion recognition unit, a customization unit, and a support unit. The reception unit receives inquiries from users. For example, the reception unit allows users to access the chatbot and input their inquiries. The reception unit allows users to consult anonymously, ensuring their privacy is protected. For example, if a user inputs "I'm stressed out at work," the reception unit receives this information. The emotion recognition unit recognizes the user's emotions based on the inquiries received by the reception unit. For example, the emotion recognition unit uses natural language processing to recognize the user's emotions. The emotion recognition unit analyzes the user's input to determine their stress level. For example, if a user inputs "I'm stressed out at work," the emotion recognition unit recognizes that the user is feeling stressed. The customization unit provides customized support tailored to the individual user's situation based on the emotions recognized by the emotion recognition unit. For example, if a user inputs "I'm stressed out at work," the customization unit provides advice such as "Try taking some deep breaths to relax." The customization unit learns user behavior patterns and evolves interactions to meet user needs. The support unit provides immediate responses and ongoing support based on the customization provided by the customization unit. For example, when a user inputs a question, the support unit responds immediately and provides appropriate support for the user's concerns. For example, if a user inputs "I'm stressed out at work," the support unit immediately provides advice such as "Try taking some deep breaths to relax." If the user consults again, the support unit remembers the content of the previous consultation and provides ongoing support. As a result, the AI ​​chatbot system according to this embodiment can reduce stress and provide emotional support by recognizing emotions in response to the user's questions and providing customized responses and support.

[0066] The reception department receives inquiries from users. For example, users can access the chatbot and input their inquiries. The reception department allows users to consult anonymously, protecting their privacy. Specifically, when a user accesses the chatbot, an anonymous user ID is automatically generated, and a system is in place to protect the user's personal information. Users can freely input their inquiries into the chat window in text format. For example, if a user inputs "I'm stressed out at work," the reception department receives that information. The reception department has the function to analyze the input text in real time and route it to the appropriate department. Furthermore, the reception department automatically categorizes the user's input and sends it to the emotion recognition department and the customization support department. This ensures that the user's inquiries are processed quickly and accurately. The reception department can also save the user's input as a log and use it for subsequent support. In this way, the reception department can receive inquiries quickly and accurately while protecting the user's privacy.

[0067] The emotion recognition unit recognizes the user's emotions based on the consultation content received by the reception unit. The emotion recognition unit uses, for example, AI to recognize the user's emotions using natural language processing. Specifically, it uses natural language processing technology to analyze emotions from the user's input. The emotion recognition unit tokenizes the user's input and calculates an emotion score for each token. For example, for the input "I'm stressed out at work," it breaks it down into tokens such as "work," "stress," and "stressed," and assigns an emotion score to each token. The emotion scores are classified into categories such as positive, negative, and neutral, and an overall emotion score is calculated. Based on these scores, the emotion recognition unit determines the user's emotions. For example, if a user inputs "I'm stressed out at work," the emotion recognition unit recognizes that the user is feeling stressed. Furthermore, the emotion recognition unit can refer to the user's past consultation content and behavioral history to analyze changes and trends in emotions. This allows the emotion recognition unit to accurately recognize the user's emotions and provide basic information for appropriate responses.

[0068] The customized support unit provides customized responses tailored to each user's situation based on the emotions recognized by the emotion recognition unit. For example, if a user inputs "I'm stressed out from work," the customized support unit will provide advice such as "Try taking some deep breaths to relax." Specifically, the customized support unit generates the optimal response according to the user's situation based on the emotional data received from the emotion recognition unit. The customized support unit learns the user's preferences and tendencies by referring to the user's past consultation content and behavioral history. For example, if a user has previously provided feedback that "deep breathing was effective," the customized support unit will recommend deep breathing again. Furthermore, the customized support unit can provide different responses depending on the user's emotional state. For example, if a user is experiencing high levels of stress, it may not only suggest relaxation methods but also recommend consulting a professional. In addition, the customized support unit collects user feedback and continuously improves the accuracy and effectiveness of its responses. This allows the customized support unit to provide flexible and effective responses that meet the user's needs.

[0069] The support department provides immediate responses and ongoing support based on the customized solutions provided by the customized solutions department. For example, when a user enters their inquiry, the support department responds immediately and provides appropriate support for the user's concerns. Specifically, the support department immediately conveys the advice provided by the customized solutions department to the user. For example, if a user enters "I'm stressed out at work," the support department immediately provides advice such as "Try taking some deep breaths to relax." If a user consults the support department again, the support department remembers the content of the previous consultation and provides ongoing support. For example, if a user consults the support department again saying "I'm stressed out," the support department refers to the content of the previous consultation and checks whether the previous advice was effective. Furthermore, the support department collects user feedback and continuously improves the accuracy and effectiveness of its responses. For example, if a user provides feedback that "deep breathing was effective," the support department records this information and uses it for future responses. The support department can also save the user's consultation content as a log and use it for subsequent support. This allows the support department to provide prompt and appropriate support for users' concerns, and to reduce user stress and provide emotional support through continuous support.

[0070] The emotion recognition unit can analyze user emotions from user input and determine the user's stress level. For example, the emotion recognition unit can analyze user emotions using text analysis. The emotion recognition unit analyzes user emotions from user input and determines the stress level. For example, if the user inputs "I'm stressed out from work," the emotion recognition unit recognizes that the user is feeling stressed. The emotion recognition unit can also analyze user emotions using voice analysis. For example, the emotion recognition unit analyzes the tone and speed of the user's voice and determines the emotion. The emotion recognition unit can also analyze user emotions using facial expression analysis. For example, the emotion recognition unit analyzes changes in the user's facial expression and determines the emotion. This allows for appropriate responses by analyzing emotions from user input and determining the stress level. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input user input into a generative AI and have the generative AI perform the emotion analysis.

[0071] The customization unit can learn user behavior patterns and evolve interactions to meet user needs. For example, the customization unit can learn user behavior patterns using machine learning algorithms. The customization unit learns user behavior patterns and provides interactions that meet those needs. For example, the customization unit can collect past user behavior data and learn behavior patterns. The customization unit can also learn user behavior patterns using data collection methods. For example, the customization unit can collect user behavior data in real time and learn behavior patterns. The customization unit can also evolve interactions based on user feedback. For example, the customization unit collects user feedback and evolves the types of interactions. By learning user behavior patterns, it can provide interactions that meet those needs. Some or all of the above processing in the customization unit may be performed using, for example, generative AI, or without using generative AI. For example, the customization unit can input user behavior data into a generative AI and have the generative AI perform behavior pattern learning.

[0072] The support unit can remember the content of a previous consultation when a user consults again, enabling it to provide continuous support. The support unit can, for example, use a database to remember the content of the previous consultation. The support unit can also remember the content of a previous consultation when a user consults again, enabling it to provide continuous support. For example, the support unit can save the user's consultation content in a database and refer to it when the user consults again. The support unit can also set a memory period for remembering consultation content. For example, the support unit can remember consultation content within a certain period and delete consultation content after that period has passed. The support unit can also set a memory range for consultation content. For example, the support unit can remember only certain types of consultation content and not other types. This makes continuous support possible by remembering the content of the previous consultation. Some or all of the above processing in the support unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the support unit can input the user's consultation content into a generation AI and have the generation AI remember the consultation content.

[0073] The reception desk can enable users to consult anonymously. The reception desk can implement anonymous consultations, for example, by using methods to protect user information. The reception desk can enable users to consult anonymously. For example, the reception desk can protect the user's personal information and ensure anonymity. The reception desk can also accept consultations after setting up methods to ensure anonymity. For example, the reception desk can anonymize the user's IP address and accept the consultation content. The reception desk can also implement anonymous consultations by setting up methods to protect user information. For example, the reception desk can encrypt the user's personal information and ensure anonymity. This protects the user's privacy by enabling them to consult anonymously. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's personal information into a generative AI and have the generative AI perform the anonymity assurance.

[0074] The emotion recognition unit can utilize AI-based real-time emotion analysis technology. For example, the emotion recognition unit recognizes the user's emotions using real-time emotion analysis technology. The emotion recognition unit utilizes AI-based real-time emotion analysis technology. For example, the emotion recognition unit analyzes emotions in real time using an AI algorithm. The emotion recognition unit can also perform real-time emotion analysis using data processing methods. For example, the emotion recognition unit processes the user's input data in real time and recognizes emotions. The emotion recognition unit can also perform real-time emotion analysis by setting the algorithm to be used. For example, the emotion recognition unit analyzes emotions using a specific algorithm and outputs the results in real time. This improves the accuracy of emotion recognition by utilizing real-time emotion analysis technology. Some or all of the above-described processes in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input user input data into a generative AI and have the generative AI perform real-time emotion analysis.

[0075] The customization unit can utilize algorithms to learn user behavior patterns. For example, the customization unit can learn user behavior patterns using a machine learning algorithm. The customization unit can utilize algorithms to learn user behavior patterns. For example, the customization unit can learn behavior patterns using a specific algorithm. The customization unit can also learn behavior patterns using a data collection method. For example, the customization unit can collect user behavior data and learn it using an algorithm. The customization unit can also learn behavior patterns by setting the type of algorithm. For example, the customization unit can learn behavior patterns using a specific machine learning algorithm and provide interactions that meet user needs. This improves the accuracy of customization by utilizing algorithms to learn behavior patterns. Some or all of the above processing in the customization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the customization unit can input user behavior data into a generative AI and have the generative AI perform behavior pattern learning.

[0076] The reception desk can estimate the user's emotions and adjust the timing of receiving the consultation based on the estimated emotions. For example, the reception desk estimates the user's emotions using an emotion estimation algorithm. The reception desk estimates the user's emotions and adjusts the timing of receiving the consultation based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will immediately accept the consultation. If the user is relaxed, the reception desk can also accept the consultation at an appropriate time. For example, the reception desk adjusts the timing of acceptance based on the user's emotional state. If the user is in a hurry, the reception desk can also accept the consultation quickly. For example, the reception desk analyzes the user's emotional state in real time and adjusts the timing of acceptance. This allows for consultations to be accepted at an appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reception area may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception department can analyze a user's past consultation history and select the most suitable reception method. The reception department can analyze past consultation history using, for example, data analysis techniques. The reception department analyzes the user's past consultation history and selects the most suitable reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. The reception department can also suggest the most suitable reception method for a specific time period based on the user's past consultation history. For example, the reception department can select the most suitable reception method based on the content of the user's past consultations. The reception department can also analyze past consultation history using analytical algorithms. For example, the reception department can analyze the user's past consultation data and select the most suitable reception method. In this way, the most suitable reception method can be selected by analyzing past consultation history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception department can input the user's past consultation data into a generative AI and have the generative AI perform the analysis of the consultation history.

[0078] The reception unit can filter consultation requests based on the user's current living situation and areas of interest. For example, the reception unit can filter consultation requests using a filtering algorithm. The reception unit can filter consultation requests based on the user's current living situation and areas of interest. For example, the reception unit can prioritize receiving consultation requests relevant to the user's current living situation. The reception unit can also filter consultation requests relevant to the user's areas of interest. For example, the reception unit can suggest the most suitable consultation request based on the user's living situation and areas of interest. The reception unit can also filter consultation requests by setting filtering criteria. For example, the reception unit can filter consultation requests based on specific criteria and prioritize receiving highly relevant requests. This allows the reception unit to prioritize receiving relevant consultation requests by filtering based on living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input data on the user's living situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of the consultations to be received based on the estimated emotions. For example, the reception desk estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the reception desk will prioritize the consultation. If the user is relaxed, the reception desk may prioritize the consultation. For example, if the user is in a hurry, the reception desk will prioritize the consultation quickly. The reception desk can also analyze the user's emotional state in real time and determine priorities. For example, based on the user's emotional state, the reception desk will prioritize important consultations. This allows for prioritizing important consultations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reception area may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception department can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location information. For example, the reception department can filter inquiries using geographical location information. The reception department can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location information. For example, the reception department prioritizes receiving relevant inquiries based on the user's geographical location information. The reception department can also suggest the most suitable inquiries, taking into account the user's geographical location information. For example, the reception department prioritizes receiving region-specific inquiries based on the user's geographical location information. The reception department can also filter inquiries by setting filtering criteria. For example, the reception department filters inquiries based on specific criteria and prioritizes receiving highly relevant content. This allows for the priority of receiving highly relevant inquiries by considering geographical location information. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception department can input the user's geographical location information into a generative AI and have the generative AI perform the filtering.

[0081] The reception department can analyze the user's social media activity when receiving inquiries and accept relevant inquiries. For example, the reception department can filter inquiries using a social media analysis algorithm. The reception department can analyze the user's social media activity when receiving inquiries and accept relevant inquiries. For example, the reception department can prioritize accepting relevant inquiries based on the user's social media activity. The reception department can also analyze the user's social media activity and suggest the most suitable inquiries. For example, the reception department can accept inquiries related to the user's interests based on their social media activity. The reception department can also filter inquiries by setting filtering criteria. For example, the reception department can filter inquiries based on specific criteria and prioritize accepting highly relevant content. This allows for the priority acceptance of relevant inquiries by analyzing social media activity. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's social media data into a generative AI and have the generative AI perform an analysis of social media activity.

[0082] The emotion recognition unit can estimate the user's emotions and adjust the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit estimates the user's emotions using an emotion estimation algorithm. For example, the emotion recognition unit improves the accuracy of emotion recognition when the user is stressed. It can also perform emotion recognition with normal accuracy when the user is relaxed. For example, it performs emotion recognition quickly when the user is in a hurry. The emotion recognition unit can also set algorithm parameters to adjust the accuracy of emotion recognition. For example, it can improve the accuracy of emotion recognition by adjusting specific parameters. This improves the accuracy of emotion recognition by adjusting it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotion recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion recognition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The emotion recognition unit can optimize its recognition algorithm by referring to the user's past emotional data during emotion recognition. For example, the emotion recognition unit can optimize its recognition algorithm using past emotional data. The emotion recognition unit can optimize its recognition algorithm by referring to the user's past emotional data during emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion recognition by referring to the user's past emotional data. The emotion recognition unit can also optimize its recognition algorithm based on the user's past emotional data. For example, the emotion recognition unit can analyze past emotional data and adjust the parameters of the recognition algorithm. The emotion recognition unit can also optimize its recognition algorithm using data feedback. For example, the emotion recognition unit can feed back the user's past emotional data and optimize the algorithm. This allows the recognition algorithm to be optimized by referring to past emotional data. Some or all of the above processes in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's past emotional data into a generative AI and have the generative AI perform the optimization of the recognition algorithm.

[0084] The emotion recognition unit can analyze emotions while considering the context of the user's input. For example, the emotion recognition unit analyzes emotions using a contextual analysis algorithm. The emotion recognition unit analyzes emotions while considering the context of the user's input. For example, the emotion recognition unit accurately analyzes emotions while considering the context of the user's input. The emotion recognition unit can also improve the accuracy of emotion recognition based on the context of the user's input. For example, the emotion recognition unit analyzes emotions while considering the surrounding sentences and related topics. The emotion recognition unit can also analyze emotions by setting the type of context. For example, the emotion recognition unit analyzes emotions based on a specific context to improve accuracy. This allows for accurate emotion analysis by considering the context of the input. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's input into a generative AI and have the generative AI perform sentiment analysis that considers the context.

[0085] The emotion recognition unit can estimate the user's emotions and adjust the order in which it displays the emotion recognition results based on the estimated emotions. For example, the emotion recognition unit estimates the user's emotions using an emotion estimation algorithm. The emotion recognition unit estimates the user's emotions and adjusts the order in which it displays the emotion recognition results based on the estimated emotions. For example, if the user is stressed, the emotion recognition unit will prioritize displaying the emotion recognition results. If the user is relaxed, the emotion recognition unit can also display the emotion recognition results in the normal order. For example, if the user is in a hurry, the emotion recognition unit will quickly display the emotion recognition results. The emotion recognition unit can also set algorithm parameters to adjust the order in which results are displayed. For example, the emotion recognition unit can optimize the order of result display by adjusting specific parameters. This allows for prioritizing the display of important results by adjusting the order of result display based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotion recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion recognition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The emotion recognition unit can analyze emotions while considering the user's geographical distribution. For example, the emotion recognition unit can use an algorithm that analyzes emotions using geographical distribution. The emotion recognition unit analyzes emotions while considering the user's geographical distribution. For example, the emotion recognition unit accurately analyzes emotions based on the user's geographical distribution. The emotion recognition unit can also improve the accuracy of emotion recognition by considering the user's geographical distribution. For example, the emotion recognition unit analyzes emotions based on the user's location information. The emotion recognition unit can also analyze emotions by setting the type of geographical distribution. For example, the emotion recognition unit analyzes emotions while considering the emotional trends of each specific region. This allows for accurate analysis of emotions by considering geographical distribution. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's location information into a generative AI and have the generative AI perform an emotion analysis that considers geographical distribution.

[0087] The emotion recognition unit can improve the accuracy of emotion recognition by referring to the user's relevant literature during emotion recognition. For example, the emotion recognition unit uses an algorithm that improves the accuracy of emotion recognition using relevant literature. The emotion recognition unit can also improve the accuracy of emotion recognition by referring to the user's relevant literature during emotion recognition. For example, the emotion recognition unit improves the accuracy of emotion recognition by referring to the user's relevant literature. The emotion recognition unit can also optimize the emotion recognition algorithm based on the user's relevant literature. For example, the emotion recognition unit analyzes the relevant literature and adjusts the accuracy of emotion recognition. The emotion recognition unit can also improve the accuracy of emotion recognition by setting the type of literature. For example, the emotion recognition unit optimizes the emotion recognition algorithm based on specific literature. As a result, the accuracy of emotion recognition is improved by referring to relevant literature. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the user's relevant literature into a generative AI and have the generative AI perform the emotion recognition accuracy improvement.

[0088] The customization unit can estimate the user's emotions and adjust its customized response method based on the estimated emotions. For example, the customization unit estimates the user's emotions using an emotion estimation algorithm. The customization unit estimates the user's emotions and adjusts its customized response method based on the estimated emotions. For example, if the user is stressed, the customization unit provides advice to help them relax. If the user is relaxed, the customization unit may also provide a standard response method. For example, if the user is in a hurry, the customization unit responds quickly. The customization unit can also set algorithm parameters to adjust the response method. For example, the customization unit optimizes the response method by adjusting specific parameters. This allows for a more appropriate response by adjusting the response method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using, for example, generative AI, or without generative AI. For example, the customization function can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0089] The customization support unit can analyze the user's past behavior patterns and select the optimal support method when providing customization support. For example, the customization support unit can analyze past behavior patterns using data analysis techniques. The customization support unit can also improve the accuracy of customization support based on the user's past behavior patterns. For example, the customization support unit can propose the optimal support method by referring to the user's past behavior patterns. The customization support unit can also analyze past behavior patterns using analytical algorithms. For example, the customization support unit analyzes the user's past behavior data and selects the optimal support method. This allows for the selection of the optimal support method by analyzing past behavior patterns. Some or all of the above-described processes in the customization support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the customization support unit can input the user's past behavior data into a generative AI and have the generative AI perform the behavior pattern analysis.

[0090] The customization unit can customize the means of response based on the user's current living situation when providing customization support. For example, the customization unit utilizes an algorithm that customizes the means of response considering the user's living situation. The customization unit customizes the means of response based on the user's current living situation when providing customization support. For example, the customization unit provides the optimal means of response based on the user's current living situation. The customization unit can also improve the accuracy of the customization support by considering the user's current living situation. For example, the customization unit analyzes the user's current living situation and proposes the optimal means of response. The customization unit can also customize the means of response by setting the type of living situation. For example, the customization unit customizes the means of response based on a specific living situation to improve accuracy. This allows for more appropriate responses by customizing the means of response based on the current living situation. Some or all of the above-described processes in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input the user's living situation data into a generative AI and have the generative AI perform the customization of the means of response.

[0091] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated emotions. For example, the customization unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the customization unit will prioritize customization. If the user is relaxed, the customization unit can respond with normal priority. For example, if the user is in a hurry, the customization unit will respond quickly. The customization unit can also set algorithm parameters to determine priority. For example, the customization unit can optimize priority by adjusting specific parameters. This allows important responses to be prioritized by determining priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The customization support unit can select the optimal support method when performing customization, taking into account the user's geographical location information. For example, the customization support unit may use an algorithm that selects a support method using geographical location information. The customization support unit selects the optimal support method when performing customization, taking into account the user's geographical location information. For example, the customization support unit selects the optimal support method based on the user's geographical location information. The customization support unit can also improve the accuracy of customization by considering the user's geographical location information. For example, the customization support unit analyzes the user's geographical location information and proposes the optimal support method. The customization support unit can also select a support method by setting the type of geographical location information. For example, the customization support unit selects a support method based on specific geographical location information to improve accuracy. This allows for the selection of the optimal support method by considering geographical location information. Some or all of the above-described processes in the customization support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization support unit can input the user's geographical location information into a generative AI and have the generative AI perform the selection of a support method.

[0093] The customization support unit can analyze the user's social media activity and propose a response method when providing customization support. For example, the customization support unit can propose a response method using a social media analysis algorithm. The customization support unit can also analyze the user's social media activity and propose a response method when providing customization support. For example, the customization support unit can propose the optimal response method based on the user's social media activity. The customization support unit can also analyze the user's social media activity to improve the accuracy of the customization support. For example, the customization support unit can refer to the user's social media activity to provide the optimal response method. The customization support unit can also analyze social media activity by setting analysis criteria. For example, the customization support unit can analyze social media activity based on specific criteria and propose the optimal response method. In this way, the optimal response method can be proposed by analyzing social media activity. Some or all of the above processing in the customization support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization support unit can input the user's social media data into a generative AI and have the generative AI perform an analysis of social media activity.

[0094] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, the support unit estimates the user's emotions using an emotion estimation algorithm. The support unit estimates the user's emotions and adjusts its support methods based on the estimated emotions. For example, if the user is stressed, the support unit provides support to help them relax. If the user is relaxed, the support unit may provide normal support methods. For example, if the user is in a hurry, the support unit provides quick support. The support unit can also set algorithm parameters to adjust its support methods. For example, the support unit optimizes its support methods by adjusting specific parameters. This allows for more appropriate support by adjusting support methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using, for example, generative AI, or without generative AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0095] The support department can select the optimal support method by referring to the user's past consultations during support. For example, the support department can analyze past consultations using data analysis techniques. The support department selects the optimal support method by referring to the user's past consultations during support. For example, the support department selects the optimal support method by referring to the user's past consultations. The support department can also improve the accuracy of support based on the user's past consultations. For example, the support department analyzes the user's past consultations and proposes the optimal support method. The support department can also analyze past consultations using analytical algorithms. For example, the support department analyzes the user's past consultation data and selects the optimal support method. This allows the optimal support method to be selected by referring to past consultations. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without using a generative AI. For example, the support department can input the user's past consultation data into a generative AI and have the generative AI perform an analysis of the consultation content.

[0096] The support unit can customize the means of support based on the user's current living situation during support. For example, the support unit may use an algorithm that customizes the means of support considering the living situation. The support unit customizes the means of support based on the user's current living situation during support. For example, the support unit provides the optimal means of support based on the user's current living situation. The support unit can also improve the accuracy of support by considering the user's current living situation. For example, the support unit analyzes the user's current living situation and proposes the optimal means of support. The support unit can also customize the means of support by setting the type of living situation. For example, the support unit customizes the means of support based on a specific living situation to improve accuracy. This makes it possible to provide more appropriate support by customizing the means of support based on the current living situation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the support unit can input the user's living situation data into a generative AI and have the generative AI perform the customization of the means of support.

[0097] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, the support unit estimates the user's emotions using an emotion estimation algorithm. For example, the support unit prioritizes support if the user is stressed. If the user is relaxed, the support unit may provide support with normal priority. For example, if the user is in a hurry, the support unit provides support quickly. The support unit can also set algorithm parameters to determine priorities. For example, the support unit optimizes priorities by adjusting specific parameters. This allows for the priority of important support by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using, for example, generative AI, or without generative AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0098] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, the support unit may use an algorithm that selects a support method using geographical location information. The support unit selects the optimal support method by considering the user's geographical location information during support. For example, the support unit selects the optimal support method based on the user's geographical location information. The support unit can also improve the accuracy of support by considering the user's geographical location information. For example, the support unit analyzes the user's geographical location information and proposes the optimal support method. The support unit can also select a support method by setting the type of geographical location information. For example, the support unit selects a support method based on specific geographical location information to improve accuracy. This allows the optimal support method to be selected by considering geographical location information. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI and have the generative AI perform the selection of a support method.

[0099] The support unit can analyze the user's social media activity and propose support measures during support. For example, the support unit can propose support measures using a social media analysis algorithm. The support unit can also analyze the user's social media activity and propose support measures during support. For example, the support unit can propose the optimal support measures based on the user's social media activity. The support unit can also analyze the user's social media activity to improve the accuracy of support. For example, the support unit can refer to the user's social media activity to provide the optimal support measures. The support unit can also analyze social media activity by setting analysis criteria. For example, the support unit can analyze social media activity based on specific criteria and propose the optimal support measures. In this way, the optimal support measures can be proposed by analyzing social media activity. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's social media data into a generative AI and have the generative AI perform an analysis of social media activity.

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

[0101] The reception desk can refer to a user's past consultation history when receiving a user's inquiry, enabling it to provide a more appropriate response. For example, if a user previously consulted about "work-related stress," the reception desk can refer to that history and provide advice based on past responses when a similar inquiry is submitted again. The reception desk can also analyze a user's past consultation history and identify specific patterns to provide customized responses tailored to the user's needs. For example, if a user frequently consults about "work-related stress," the reception desk can recognize this pattern and prioritize providing information related to stress management. Furthermore, the reception desk can learn what kind of advice a user prefers based on their past consultation history, enabling it to provide more personalized responses. For example, if a user previously preferred and accepted the advice "try taking deep breaths to relax," the reception desk can provide similar advice again.

[0102] The emotion recognition unit can analyze user input to determine emotions, referencing the user's past emotional data to achieve more accurate emotion recognition. For example, if a user previously entered "I'm stressed at work," the emotion recognition unit can record this data, allowing for faster and more accurate emotion recognition when similar input is made again. The emotion recognition unit can also analyze the user's past emotional data and identify specific emotional patterns to predict changes in the user's emotions. For instance, if a user frequently experiences "stress," the emotion recognition unit can recognize this pattern and predict situations in which the user is likely to experience stress. Furthermore, the emotion recognition unit can optimize its emotion recognition algorithm based on the user's past emotional data. For example, it can adjust the parameters of the emotion recognition algorithm based on data from when the user previously experienced "stress," thereby improving the accuracy of emotion recognition.

[0103] The customized support unit can learn user behavior patterns and analyze user social media activity to provide more appropriate responses. For example, if a user frequently posts about "work stress" on social media, the customized support unit can use that information to provide stress management advice. The customized support unit can also analyze user social media activity and provide responses based on user interests. For example, if a user posts about "relaxation methods" on social media, the customized support unit can use that information to provide relaxation methods. Furthermore, the customized support unit can predict user behavior patterns based on user social media activity and provide more personalized responses. For example, if a user frequently posts about "work stress" on social media, the customized support unit can recognize that pattern and prioritize providing stress management-related information.

[0104] The support department can provide more appropriate support by considering the user's geographical location when receiving inquiries. For example, if a user lives in a specific area, the support department can provide information relevant to that area. Furthermore, based on the user's geographical location, the support department can provide support that takes into account region-specific stressors. For example, if a user lives in an urban area, the support department can provide advice on urban-specific stressors. Additionally, based on the user's geographical location, the support department can provide support that utilizes local resources. For example, if a user lives in a specific area, the support department can provide information on local counseling services and support groups.

[0105] The reception desk can take into account the user's current living situation when receiving inquiries, enabling them to provide more appropriate responses. For example, if a user consults about "work-related stress," the reception desk can consider the user's current living situation and provide advice on work-related stress. The reception desk can also customize responses based on the user's current living situation to meet their needs. For example, if a user consults about "stress from family problems," the reception desk can use that information to provide advice on family problems. Furthermore, the reception desk can predict what kind of support the user needs based on their current living situation and provide more personalized responses. For example, if a user is experiencing stress from both "work-related stress" and "family problems," the reception desk can use that information to provide advice on both issues.

[0106] The reception desk can estimate the user's emotions and adjust the timing of receiving the consultation based on those emotions. For example, if the user is feeling stressed, the reception desk can receive the consultation immediately. Conversely, if the user is relaxed, the reception desk can receive the consultation at an appropriate time. For example, if the user is in a hurry, the reception desk can receive the consultation quickly. Furthermore, the reception desk can analyze the user's emotional state in real time and adjust the timing of the consultation. For example, if the user is feeling stressed, the reception desk can analyze that emotional state in real time and receive the consultation immediately. This allows the reception desk to adjust the timing of the consultation based on the user's emotions, ensuring that the consultation is received at the appropriate time.

[0107] The emotion recognition unit can estimate the user's emotions and adjust the accuracy of emotion recognition based on the estimated emotions. For example, if the user is stressed, the emotion recognition unit can improve the accuracy of emotion recognition. Conversely, if the user is relaxed, it can perform emotion recognition with normal accuracy. For example, if the user is in a hurry, the emotion recognition unit can perform emotion recognition quickly. Furthermore, the emotion recognition unit can also set algorithm parameters to adjust the accuracy of emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion recognition by adjusting specific parameters. This improves the accuracy of emotion recognition by adjusting it based on the user's emotions.

[0108] The customized response unit can estimate the user's emotions and adjust its response method based on those emotions. For example, if the user is stressed, the customized response unit can provide advice on how to relax. If the user is relaxed, it can provide a standard response method. For example, if the user is in a hurry, the customized response unit can respond quickly. Furthermore, the customized response unit can set algorithmic parameters to adjust the response method. For example, the customized response unit can optimize the response method by adjusting specific parameters. This allows for more appropriate responses by adjusting the response method based on the user's emotions.

[0109] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is stressed, the support unit can provide support to help them relax. If the user is relaxed, it can provide standard support. For example, if the user is in a hurry, the support unit can provide support quickly. Furthermore, the support unit can set algorithmic parameters to adjust its support methods. For example, it can optimize its support methods by adjusting specific parameters. This allows for more appropriate support by adjusting support methods based on the user's emotions.

[0110] The reception desk can estimate the user's emotions and determine the priority of the consultation content based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can prioritize the consultation. If the user is relaxed, the consultation can be handled with the normal priority. For example, if the user is in a hurry, the reception desk can quickly prioritize the consultation. Furthermore, the reception desk can analyze the user's emotional state in real time and determine priorities accordingly. For example, if the user is feeling stressed, the reception desk can analyze their emotional state in real time and prioritize important consultations. In this way, by determining priorities based on the user's emotions, important consultations can be given priority.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives inquiries from users. For example, a user can access the chatbot and enter their inquiry. Users can consult anonymously, and their privacy is protected. For example, if a user enters "I'm stressed out at work," the reception desk will receive that information. Step 2: The emotion recognition unit recognizes the user's emotions based on the consultation content received by the reception unit. For example, AI uses natural language processing to recognize the user's emotions. The emotion recognition unit analyzes the emotions from the user's input and determines the user's stress level. For example, if the user inputs "I'm stressed out from work," the emotion recognition unit recognizes that the user is feeling stressed. Step 3: The customization unit provides customized responses tailored to each user's situation based on the emotions recognized by the emotion recognition unit. For example, if a user inputs "I'm stressed out from work," the customization unit will provide advice such as "Try taking some deep breaths to relax." The customization unit learns the user's behavior patterns and evolves its interactions to meet the user's needs. Step 4: The support department provides immediate responses and ongoing support based on the customized solutions provided by the customized solutions department. For example, when a user enters their inquiry, the support department responds immediately and provides appropriate support for the user's concerns. For instance, if a user enters "I'm stressed out at work," the support department immediately provides advice such as "Try taking some deep breaths to relax." If the user contacts the support department again, the support department remembers the content of the previous inquiry and provides ongoing support.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, emotion recognition unit, customization unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives inquiries from the user. The emotion recognition unit is implemented by the specific processing unit 290 of the data processing unit 12, where AI recognizes the user's emotions using natural language processing. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, where it provides customized support tailored to the user's situation. The support unit is implemented by the control unit 46A of the smart device 14, where it provides immediate response and continuous support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, emotion recognition unit, customization unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives inquiries from the user. The emotion recognition unit is implemented by the specific processing unit 290 of the data processing unit 12, where AI recognizes the user's emotions using natural language processing. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, where it provides customized support tailored to the user's situation. The support unit is implemented by the control unit 46A of the smart glasses 214, where it provides immediate response and continuous support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, emotion recognition unit, customization unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives inquiries from the user. The emotion recognition unit is implemented by the specific processing unit 290 of the data processing unit 12, where AI recognizes the user's emotions using natural language processing. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, where customized responses are provided according to the user's situation. The support unit is implemented by the control unit 46A of the headset terminal 314, where immediate responses and continuous support are provided. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, emotion recognition unit, customization unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives inquiries from the user. The emotion recognition unit is implemented by the specific processing unit 290 of the data processing unit 12, where the AI ​​recognizes the user's emotions using natural language processing. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, where it provides customized support tailored to the user's situation. The support unit is implemented by the control unit 46A of the robot 414, where it provides immediate response and continuous support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception department that receives inquiries from users, An emotion recognition unit recognizes the user's emotions based on the consultation content received by the reception unit, A customization unit that performs customized responses tailored to the individual user's situation based on the emotions recognized by the emotion recognition unit, The system includes a support unit that provides immediate responses and continuous support based on the customization provided by the customization unit. A system characterized by the following features. (Note 2) The emotion recognition unit, The system analyzes user input to understand their emotions and determine their stress level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned customization support unit is Learn user behavior patterns and evolve interactions to meet user needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is When a user contacts us again, we remember the details of their previous consultation and provide continuous support. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Allows users to consult anonymously. The system described in Appendix 1, characterized by the features described herein. (Note 6) The emotion recognition unit, Utilizing AI-powered real-time sentiment analysis technology The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned customization support unit is Utilizing algorithms that learn user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving inquiries, the system filters them based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving inquiries, the system prioritizes accepting inquiries that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving a consultation request, the system analyzes the user's social media activity and accepts consultation requests related to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion recognition unit, It estimates the user's emotions and adjusts the accuracy of emotion recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion recognition unit, When recognizing emotions, the recognition algorithm is optimized by referring to the user's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion recognition unit, When recognizing emotions, the system analyzes emotions while considering the context of the user's input. The system described in Appendix 1, characterized by the features described herein. (Note 17) The emotion recognition unit, It estimates the user's emotions and adjusts the order in which the emotion recognition results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The emotion recognition unit, When recognizing emotions, the system analyzes emotions while considering the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The emotion recognition unit, When recognizing emotions, the system improves the accuracy of emotion recognition by referencing relevant literature from the user. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned customization support unit is It estimates the user's emotions and adjusts the customized response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned customization support unit is When providing customization support, we analyze the user's past behavior patterns to select the most appropriate solution. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned customization support unit is When providing customization, the means of support are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned customization support unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned customization support unit is When providing customization support, the optimal approach is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization support unit is When providing customization support, we analyze the user's social media activity and propose solutions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, the system will refer to the user's past inquiries to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception department that receives inquiries from users, An emotion recognition unit recognizes the user's emotions based on the consultation content received by the reception unit, A customization unit that performs customized responses tailored to the individual user's situation based on the emotions recognized by the emotion recognition unit, The system includes a support unit that provides immediate responses and continuous support based on the customization provided by the customization unit. A system characterized by the following features.

2. The emotion recognition unit, The system analyzes user input to understand their emotions and determine their stress level. The system according to feature 1.

3. The aforementioned customization support unit is Learn user behavior patterns and evolve interactions to meet user needs. The system according to feature 1.

4. The aforementioned support unit is When a user contacts us again, we remember the details of their previous consultation and provide continuous support. The system according to feature 1.

5. The aforementioned reception unit is Allows users to consult anonymously. The system according to feature 1.

6. The emotion recognition unit, Utilizing AI-powered real-time sentiment analysis technology The system according to feature 1.

7. The aforementioned customization support unit is Utilizing algorithms that learn user behavior patterns. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving inquiries based on those estimated emotions. The system according to feature 1.

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