system

Abstract

The system according to this embodiment aims to analyze the content of the consultation and provide prompt and appropriate support. [Solution] The system according to the embodiment comprises a reception unit, a detection unit, a support unit, and a notification unit. The reception unit receives inquiries from users. The detection unit analyzes the content of the inquiries received by the reception unit and detects specific words. The support unit provides support via push notification based on the specific words detected by the detection unit. The notification unit sends an SOS notification to a counselor if the support unit is unable to handle the matter.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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, there was a problem that in the prevention of youth suicide, there was a lack of prompt and appropriate support for the counseling content.

[0005] The system according to the embodiment aims to analyze the counseling content and provide prompt and appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a detection unit, a support unit, and a notification unit. The reception unit receives inquiries from users. The detection unit analyzes the content of the inquiries received by the reception unit and detects specific words. The support unit provides support via push notification based on the specific words detected by the detection unit. The notification unit sends an SOS notification to a counselor if the support unit is unable to handle the matter. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the content of the consultation and provide prompt and appropriate support. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied 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 reception 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 reception 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 medical application utilizing a generative AI counselor according to an embodiment of the present invention is a system designed to address the serious problem of increasing suicide rates among young people. This system aims to save the lives of people in need of support by utilizing a medical application to create a generative AI counselor. The generative AI counselor not only receives consultations but also provides active support. For example, when it detects a specific word being searched or a statement being made, the generative AI provides support via push notification. It also has a backup function that sends an SOS notification to a human support provider, such as a counselor, if the situation is difficult to handle. For example, the generative AI counselor receives consultations from users. When a user searches for or makes a statement using a specific word such as "I want to commit suicide," the generative AI detects the content and provides support via push notification. The generative AI analyzes the user's statements and search history to provide appropriate advice and support. Furthermore, the generative AI counselor has a backup function that sends an SOS notification to a human support provider, such as a counselor, if the situation is difficult to handle. For example, if the generative AI analyzes the user's condition and determines that it is highly urgent, it sends an SOS notification to a counselor. In this way, the system provides support to save users' lives in conjunction with human support. This mechanism can address the serious problem of the increasing number of suicides among young people. The generated AI counselor can accept consultations 24 hours a day and can respond to multiple users simultaneously. This allows users to easily seek advice and receive the best possible guidance. Furthermore, the generated AI counselor can provide appropriate support while protecting user privacy. For example, it can manage student health check results and reasons for absence in cooperation with schools. This allows for understanding students' health status and providing necessary support. In addition, the generated AI counselor can detect anomalies from web search history and message history and provide proactive support. For example, if a user searches for something like "I want to commit suicide," the generated AI will detect the content and provide support via push notification.In this way, by utilizing a generated AI counselor, we can address the serious problem of the rising suicide rate among young people and save lives. The generated AI counselor can accept consultations 24 hours a day and can handle multiple users simultaneously. It also has a backup function that sends an SOS notification to human support such as a counselor if it is unable to handle the situation. This allows us to provide support that can save users' lives. Thus, a medical app that utilizes a generated AI counselor can address the serious problem of the rising suicide rate among young people and save lives.

[0029] The medical application utilizing the generative AI counselor according to this embodiment comprises a reception unit, a detection unit, a support unit, and a notification unit. The reception unit receives consultations from users. For example, the user can input the consultation content through the application. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can use voice recognition technology to convert the user's voice into text and receive the consultation content. The reception unit can also allow the user to directly input the consultation content using text input. Furthermore, the reception unit can refer to the user's past consultation history and select the most appropriate response method. For example, the reception unit can provide appropriate responses to similar problems based on the user's past consultation content. The detection unit analyzes the consultation content received by the reception unit and detects specific words. For example, the detection unit can use generative AI to analyze the consultation content and detect specific words. For example, the detection unit can issue an alert when the generative AI detects a specific word such as "I want to commit suicide." The detection unit can also analyze the user's statements and search history to detect specific words. For example, the detection unit can detect if a user performs a search such as "I want to commit suicide" and issue an alert. Furthermore, the detection unit can manage a list of specific words and detect words based on that list. For example, the detection unit can periodically update the list of specific words to respond to the latest trends. The support unit provides support via push notifications based on the specific words detected by the detection unit. The support unit can provide appropriate advice and support to the user using, for example, generative AI. For example, the support unit can use generative AI to analyze the user's statements and search history and provide appropriate advice. The support unit can also analyze the user's state and provide appropriate support. For example, the support unit can use generative AI to analyze the user's psychological state and provide appropriate support. Furthermore, the support unit can estimate the user's emotions and adjust the way support is expressed based on those emotions.For example, the support unit can use a generative AI to analyze the user's emotions and provide support using gentle language. The notification unit sends an SOS notification to a counselor when the support unit is unable to handle the situation. The notification unit can, for example, use a generative AI to analyze the user's condition and send an SOS notification to a counselor if it determines that the situation is urgent. For example, the notification unit can use a generative AI to analyze the user's statements and search history and send an SOS notification to a counselor if it determines that the situation is urgent. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on those emotions. For example, the notification unit can use a generative AI to analyze the user's emotions and immediately send a notification to a counselor if it determines that the situation is urgent. As a result, the medical application utilizing the generative AI counselor according to this embodiment can efficiently receive, analyze, support, and notify users of their consultations.

[0030] The reception desk receives inquiries from users. For example, users can input their inquiries through an app. The reception desk can also support multiple input methods, including voice and text input. Specifically, it can use speech recognition technology to convert user speech into text and receive inquiries. Speech recognition technology analyzes user speech with high accuracy and converts it into text using natural language processing technology. This allows users to easily communicate their inquiries by voice. Users can also directly input their inquiries using text input. Text input is a method where users input their inquiries using a keyboard or touchscreen, and is usable even in environments where voice input is difficult. Furthermore, the reception desk can refer to the user's past inquiry history and select the most appropriate response. For example, the reception desk can save the user's past inquiries in a database and provide appropriate responses to similar problems. This allows users to receive consistent support and accurate advice based on their past inquiries. To protect user privacy, the reception desk encrypts data and controls access to prevent inquiries from being leaked to third parties. Furthermore, the reception department can quickly process user inquiries and forward them to the appropriate department or person in charge. This allows users to receive prompt and appropriate responses, enabling them to seek advice with peace of mind.

[0031] The detection unit analyzes the content of consultations received by the reception unit and detects specific words. For example, the detection unit can use generative AI to analyze the content of consultations and detect specific words. The generative AI utilizes natural language processing technology to analyze the user's consultation content in detail and extract important keywords and phrases. For example, the detection unit can issue an alert when the generative AI detects a specific word such as "I want to commit suicide." The generative AI can understand the content of the user's statements in context and detect dangerous signs early. The detection unit can also analyze the content of the user's statements and search history to detect specific words. For example, if a user performs a search for "I want to commit suicide," the unit can detect this content and issue an alert. Furthermore, the detection unit manages a list of specific words and can detect words based on this list. The list of specific words is regularly updated under the supervision of experts to respond to the latest trends and dangerous signs. As a result, the detection unit can quickly and accurately analyze the content of user consultations and detect highly urgent problems early. The detection unit can provide information to take appropriate action based on the specific word detected, thereby ensuring user safety.

[0032] The support unit provides support via push notifications based on specific words detected by the detection unit. For example, the support unit can use generative AI to provide appropriate advice and support to users. The generative AI analyzes the user's statements and search history to provide appropriate advice. For example, if a user says they are "stressed," the generative AI can suggest stress relief methods and relaxation techniques. The support unit can also analyze the user's state and provide appropriate support. The generative AI analyzes the user's psychological state and provides appropriate support. For example, if a user is feeling anxious, the generative AI can offer advice on relaxation or recommend consulting a mental health professional. Furthermore, the support unit can estimate the user's emotions and adjust the way support is expressed based on those emotions. The generative AI analyzes the user's emotions and provides support using gentle language. For example, if a user is sad, the generative AI can send words of encouragement and comforting messages. This allows the support unit to provide appropriate advice and support to users, reducing their psychological burden. The support department can collect user feedback and continuously improve the accuracy and effectiveness of its support services. This allows the support department to provide more effective support to users and increase user satisfaction.

[0033] The notification unit sends an SOS notification to a counselor when the support unit is unable to handle the situation. For example, the notification unit can use a generative AI to analyze the user's state and send an SOS notification to a counselor if it determines that the situation is urgent. The generative AI analyzes the user's statements and search history and sends an SOS notification to a counselor if it determines that the situation is urgent. For example, if a user says "I want to commit suicide," the generative AI can immediately determine the urgency and send a notification to a counselor. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on those emotions. The generative AI analyzes the user's emotions and immediately sends a notification to a counselor if the situation is urgent. For example, if a user is experiencing extreme anxiety or panic, the generative AI can determine the urgency and quickly send a notification to a counselor. The notification unit provides counselors with detailed information about the user to support a quick and appropriate response. This allows counselors to accurately understand the user's situation and provide appropriate support. The notification department will encrypt data and implement access controls to protect user privacy and prevent the disclosure of consultation details to third parties. This will allow the notification department to ensure user safety and respond quickly and appropriately in emergencies.

[0034] The detection unit manages a list of specific words. For example, the detection unit can periodically update this list of specific words to keep up with the latest trends. For instance, it can use generative AI to analyze user statements and search history and add new specific words to the list. Furthermore, the detection unit can dynamically update the list of specific words to keep up with the latest trends. For example, it can obtain the latest trend information from social media and news sites and update the list of specific words. In addition, the detection unit can improve detection accuracy by managing the list of specific words. For example, it can analyze user statements and search history based on the list of specific words to detect specific words. Thus, managing the list of specific words improves detection accuracy.

[0035] The detection unit analyzes the user's statements and search history. For example, the detection unit can use generative AI to analyze the user's statements and detect specific words. For instance, the detection unit can use generative AI to analyze the user's statements and detect specific words such as "I want to commit suicide." The detection unit can also analyze the user's search history and detect specific words. For example, the detection unit can use generative AI to analyze the user's search history and detect if a search for "I want to commit suicide" was performed. Furthermore, by analyzing the user's statements and search history, the detection unit can provide appropriate support. For example, the detection unit can use generative AI to analyze the user's statements and search history and provide appropriate advice and support. This allows for the provision of appropriate support by analyzing the user's statements and search history.

[0036] The support department analyzes the user's condition and provides appropriate advice and support. For example, the support department can use generative AI to analyze the user's psychological state and provide appropriate advice. For instance, the support department can use generative AI to analyze the user's statements and search history to understand the user's psychological state and provide appropriate advice. The support department can also analyze the user's health condition and provide appropriate support. For example, the support department can use generative AI to analyze the user's health check results and reasons for absence and provide appropriate support. Furthermore, the support department can estimate the user's emotions and adjust the way support is expressed based on those emotions. For example, the support department can use generative AI to analyze the user's emotions and provide support using gentle language. In this way, by analyzing the user's condition, appropriate advice and support can be provided.

[0037] The notification unit sends an SOS notification to a counselor when it determines that the situation is highly urgent. For example, the notification unit can use a generative AI to analyze the user's state and send an SOS notification to a counselor when it determines that the situation is highly urgent. For example, the notification unit can use a generative AI to analyze the user's statements and search history and send an SOS notification to a counselor when it determines that the situation is highly urgent. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on those emotions. For example, the notification unit can use a generative AI to analyze the user's emotions and immediately send a notification to a counselor if the situation is highly urgent. Furthermore, the notification unit can refer to the user's past consultation history to determine the urgency. For example, based on the user's past consultation history, the notification unit can prioritize notifying counselors of highly urgent issues. This enables a rapid response by sending an SOS notification to a counselor when the situation is highly urgent.

[0038] The reception desk accepts consultations 24 hours a day. For example, the reception desk can implement a shift system to provide 24-hour support. For instance, multiple staff members can work in shifts to ensure 24-hour service. Alternatively, the reception desk can utilize an automated response system to accept consultations 24 hours a day. For example, an automated response system using AI generation can be implemented to receive user consultations 24 hours a day. Furthermore, the reception desk can record user consultation details and refer to them during subsequent consultations. For example, the reception desk can save user consultation details in a database and refer to past consultation details during subsequent consultations. This allows users to consult at any time by accepting consultations 24 hours a day.

[0039] The reception desk can handle multiple users simultaneously. For example, the reception desk can use a chatbot to handle multiple users at once. For instance, the reception desk can implement a chatbot using generative AI to receive inquiries from multiple users simultaneously. Furthermore, the reception desk can use a multitasking system to handle multiple users simultaneously. For example, the reception desk can implement a multitasking system using generative AI to process inquiries from multiple users simultaneously. In addition, the reception desk can prioritize user inquiries and handle them efficiently. For example, the reception desk can use generative AI to analyze user inquiries and prioritize those with high urgency. This enables efficient consultation reception by handling multiple users simultaneously.

[0040] The reception department selects the most suitable reception method by referring to the user's past consultation history at the time of reception. For example, the reception department can use generative AI to analyze the user's past consultation history and select the most suitable reception method. For example, the reception department can use generative AI to analyze the user's past consultation content and provide appropriate responses to similar problems. The reception department can also consider the compatibility with a specific counselor based on the user's past consultation history when making a reception request. For example, the reception department can use generative AI to analyze the user's past consultation history and select the most suitable counselor by considering compatibility with a specific counselor. Furthermore, the reception department can analyze the user's past consultation history and propose the most effective support method. For example, the reception department can use generative AI to analyze the user's past consultation history and propose the most effective support method. In this way, the reception department can select the most suitable reception method by referring to past consultation history.

[0041] The reception desk filters users based on their current living situation and areas of interest during the reception process. For example, the reception desk can use generative AI to analyze and filter users based on their current living situation and areas of interest. For instance, the reception desk can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest appropriate consultation topics. Furthermore, the reception desk can analyze users' areas of interest (hobbies, interests, learning content, etc.) and prioritize receiving consultations related to those areas. For example, the reception desk can use generative AI to analyze the user's areas of interest and prioritize receiving consultations related to those areas. In addition, the reception desk can select the most suitable counselor based on the user's living situation and areas of interest. This allows for the suggestion of appropriate consultation topics by filtering based on living situation and areas of interest.

[0042] The reception desk prioritizes accepting consultations that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can use generative AI to analyze the user's geographical location information and prioritize accepting consultations related to a specific region. Furthermore, the reception desk can also propose consultation content tailored to the characteristics and problems of the region, based on the user's geographical location information. For example, the reception desk can use generative AI to analyze the user's geographical location information and propose consultation content tailored to the characteristics and problems of the region. In addition, the reception desk can consider the user's geographical location information and collaborate with local counselors to provide the best possible response. This allows for the priority acceptance of consultations related to a specific region by considering geographical location information.

[0043] The reception desk analyzes the user's social media activity upon receiving a call and accepts relevant inquiries. For example, the reception desk can use generative AI to analyze the user's social media activity and accept relevant inquiries. For instance, the reception desk can use generative AI to analyze the user's social media posts and suggest relevant inquiries. Furthermore, the reception desk can prioritize accepting appropriate inquiries based on the user's frequency and content of social media activity. For example, the reception desk can use generative AI to analyze the user's social media activity and prioritize accepting appropriate inquiries. Additionally, the reception desk can consider the user's social media friendships and communities when accepting relevant inquiries. For example, the reception desk can use generative AI to analyze the user's social media friendships and communities and suggest relevant inquiries. This allows for the suggestion of relevant inquiries by analyzing social media activity.

[0044] The detection unit improves detection accuracy by referring to the user's past statements and search history when detection occurs. For example, the detection unit can use generative AI to analyze the user's past statements and improve the detection accuracy of specific words. For example, the detection unit can use generative AI to analyze the user's past statements and improve the accuracy of detecting specific words. The detection unit can also analyze the user's search history and prioritize the detection of relevant specific words. For example, the detection unit can use generative AI to analyze the user's search history and prioritize the detection of relevant specific words. Furthermore, the detection unit can dynamically update the list of specific words based on the user's past statements and search history. For example, the detection unit can use generative AI to analyze the user's past statements and search history and dynamically update the list of specific words. This improves detection accuracy by referring to past statements and search history.

[0045] The detection unit dynamically updates a list of specific words upon detection to respond to the latest trends. For example, the detection unit can periodically update the list of specific words using a generative AI to respond to the latest trends. For instance, the generative AI can obtain the latest trend information from social media and news sites and update the list of specific words. Furthermore, the detection unit can update the list of specific words in real time based on user statements and search history. For example, the generative AI can analyze user statements and search history and update the list of specific words in real time. In addition, the detection unit can respond to the latest trends by dynamically updating the list of specific words. For example, the generative AI can dynamically update the list of specific words based on the latest trend information. This allows the detection unit to respond to the latest trends by dynamically updating the list of specific words.

[0046] The detection unit prioritizes detecting highly relevant words by considering the user's geographical location information during detection. For example, the detection unit can analyze the user's geographical location information using generative AI and prioritize detecting highly relevant words. For instance, the detection unit can use generative AI to analyze the user's geographical location information and prioritize detecting words related to a specific region. Furthermore, the detection unit can prioritize detecting words that correspond to regional characteristics and issues based on the user's geographical location information. For example, the detection unit can use generative AI to analyze the user's geographical location information and prioritize detecting words that correspond to regional characteristics and issues. Additionally, the detection unit can prioritize detecting words related to regional trends by considering the user's geographical location information. For example, the detection unit can use generative AI to analyze the user's geographical location information and prioritize detecting words related to regional trends. This allows for the priority detection of region-related words by considering geographical location information.

[0047] The detection unit analyzes the user's social media activity and detects relevant words upon detection. For example, the detection unit can use generative AI to analyze the user's social media activity and detect relevant words. For instance, the detection unit can use generative AI to analyze the user's social media posts and detect relevant words. Furthermore, the detection unit can prioritize the detection of appropriate words based on the frequency and content of the user's social media activity. For example, the detection unit can use generative AI to analyze the frequency and content of the user's social media activity and prioritize the detection of appropriate words. Additionally, the detection unit can consider the user's social media friendships and communities when detecting relevant words. For example, the detection unit can use generative AI to analyze the user's social media friendships and communities and detect relevant words. This allows for the detection of relevant words by analyzing social media activity.

[0048] The support department provides optimal advice by referring to the user's past consultation history during support sessions. For example, the support department can use generative AI to analyze the user's past consultation history and provide optimal advice. For instance, the support department can use generative AI to analyze the user's past consultation history and provide appropriate advice for similar problems. Furthermore, the support department can provide advice considering the user's compatibility with a specific counselor based on their past consultation history. For example, the support department can use generative AI to analyze the user's past consultation history and provide optimal advice considering their compatibility with a specific counselor. In addition, the support department can analyze the user's past consultation history and propose the most effective support method. For example, the support department can use generative AI to analyze the user's past consultation history and propose the most effective support method. This allows the support department to provide optimal advice by referring to past consultation history.

[0049] The support department customizes the means of support based on the user's current living situation. For example, the support department can use generative AI to analyze the user's current living situation and customize the means of support. For example, the support department can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest appropriate means of support. The support department can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and provide relevant means of support. For example, the support department can use generative AI to analyze the user's areas of interest and provide relevant means of support. Furthermore, the support department can select the most suitable counselor based on the user's living situation and areas of interest. For example, the support department can use generative AI to analyze the user's living situation and areas of interest and select the most suitable counselor. This allows for the provision of more appropriate support by customizing the means of support based on the user's living situation.

[0050] The support department selects the optimal support method when providing support, taking into account the user's geographical location. For example, the support department can use generative AI to analyze the user's geographical location and select the optimal support method. For instance, the support department can use generative AI to analyze the user's geographical location and propose support methods relevant to a specific region. Furthermore, the support department can provide support methods tailored to regional characteristics and problems based on the user's geographical location. For example, the support department can use generative AI to analyze the user's geographical location and provide support methods tailored to regional characteristics and problems. In addition, the support department can collaborate with local counselors to provide support, taking the user's geographical location into consideration. For example, the support department can use generative AI to analyze the user's geographical location and collaborate with local counselors to provide optimal support. This allows for the provision of region-specific support methods by considering geographical location.

[0051] The support department analyzes the user's social media activity and provides relevant support during support sessions. For example, the support department can use generative AI to analyze the user's social media activity and provide relevant support. For instance, the support department can use generative AI to analyze the user's social media posts and provide relevant support. Furthermore, the support department can suggest appropriate support methods based on the user's frequency and content of social media activity. For example, the support department can use generative AI to analyze the user's social media activity and suggest appropriate support methods. Additionally, the support department can consider the user's social media friendships and communities when providing relevant support. For example, the support department can use generative AI to analyze the user's social media friendships and communities and provide relevant support. This allows the support department to provide relevant support by analyzing social media activity.

[0052] The notification unit determines the urgency of a situation by referring to the user's past consultation history when sending a notification. For example, the notification unit can use a generation AI to analyze the user's past consultation history and determine the urgency. For example, the notification unit can use a generation AI to analyze the user's past consultation content and prioritize notifications for consultations with high urgency. The notification unit can also consider the user's compatibility with a specific counselor based on their past consultation history when sending notifications. For example, the notification unit can use a generation AI to analyze the user's past consultation history and send notifications to the most suitable counselor, considering their compatibility. Furthermore, the notification unit can analyze the user's past consultation history and suggest the most effective notification method. For example, the notification unit can use a generation AI to analyze the user's past consultation history and suggest the most effective notification method. This makes it possible to send notifications with high urgency by referring to past consultation history.

[0053] The notification unit customizes the notification method based on the user's current living situation when a notification is sent. For example, the notification unit can use generative AI to analyze the user's current living situation and customize the notification method. For example, the notification unit can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest an appropriate notification method. The notification unit can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and provide relevant notification methods. For example, the notification unit can use generative AI to analyze the user's areas of interest and provide relevant notification methods. Furthermore, the notification unit can select the most suitable counselor based on the user's living situation and areas of interest. For example, the notification unit can use generative AI to analyze the user's living situation and areas of interest and select the most suitable counselor. By customizing the notification method based on the user's living situation, more appropriate notifications become possible.

[0054] The notification unit selects the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can use a generative AI to analyze the user's geographical location information and select the optimal notification method. For instance, the generative AI can analyze the user's geographical location information and propose a notification method relevant to a specific region. Furthermore, the notification unit can provide notification methods tailored to regional characteristics and issues based on the user's geographical location information. For example, the generative AI can analyze the user's geographical location information and provide notification methods tailored to regional characteristics and issues. In addition, the notification unit can consider the user's geographical location information and collaborate with local counselors to send notifications. For example, the generative AI can analyze the user's geographical location information and collaborate with local counselors to provide the most appropriate notification. This allows the system to provide region-specific notification methods by considering geographical location information.

[0055] The notification unit analyzes the user's social media activity and sends relevant notifications when a notification is sent. For example, the notification unit can use generative AI to analyze the user's social media activity and send relevant notifications. For instance, the notification unit can use generative AI to analyze the user's social media posts and send relevant notifications. Furthermore, the notification unit can suggest appropriate notification methods based on the user's frequency and content of social media activity. For example, the notification unit can use generative AI to analyze the user's social media activity frequency and content and suggest appropriate notification methods. Additionally, the notification unit can consider the user's social media friendships and communities when sending relevant notifications. For example, the notification unit can use generative AI to analyze the user's social media friendships and communities and send relevant notifications. This allows for relevant notifications by analyzing 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 select the most appropriate response method by referring to the user's past consultation history when receiving a user's inquiry. For example, the reception desk can use generative AI to analyze the user's past consultation content and provide appropriate responses to similar problems. Furthermore, the reception desk can consider the user's compatibility with a specific counselor based on their past consultation history when accepting inquiries. In addition, the reception desk can analyze the user's past consultation history and propose the most effective support method. This allows the reception desk to select the optimal method of handling inquiries by referring to past consultation history.

[0058] The detection unit can analyze user statements and search history while considering the user's current lifestyle and areas of interest. For example, the detection unit can use generative AI to analyze the user's lifestyle (work, family, health, etc.) and detect appropriate words. It can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and prioritize the detection of related words. Furthermore, the detection unit can dynamically update a list of specific words based on the user's lifestyle and areas of interest. This allows for the detection of more appropriate words by considering the user's lifestyle and areas of interest.

[0059] The reception desk can prioritize receiving consultations that are highly relevant to the user, taking into account the user's geographical location. For example, the reception desk can use generative AI to analyze the user's geographical location and prioritize consultations related to a specific region. Furthermore, the reception desk can suggest consultation content tailored to the characteristics and problems of the region, based on the user's geographical location. In addition, the reception desk can collaborate with local counselors, taking the user's geographical location into consideration. This allows for the priority of receiving consultations related to the region by considering geographical location.

[0060] The detection unit can analyze a user's social media activity and detect relevant words. For example, it can use generative AI to analyze a user's social media posts and detect relevant words. Furthermore, the detection unit can prioritize the detection of appropriate words based on the frequency and content of the user's social media activity. In addition, the detection unit can consider the user's social media friendships and communities to detect relevant words. Thus, by analyzing social media activity, relevant words can be detected.

[0061] The support department can customize support methods based on the user's current living situation. For example, the support department can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest appropriate support methods. It can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and provide relevant support methods. Furthermore, the support department can select the most suitable counselor based on the user's living situation and areas of interest. This allows for the provision of more appropriate support by customizing support methods based on the user's living situation.

[0062] The notification unit can determine the urgency of a situation by referring to the user's past consultation history. For example, the notification unit can use a generation AI to analyze the user's past consultation history and prioritize notifications for highly urgent consultations. Furthermore, the notification unit can consider the user's compatibility with a specific counselor based on their past consultation history and send notifications accordingly. In addition, the notification unit can analyze the user's past consultation history and suggest the most effective notification method. This allows for the provision of highly urgent notifications by referencing past consultation history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives inquiries from users. For example, users can input their inquiries through the app, and the system supports multiple input methods, such as voice input and text input. It can also use speech recognition technology to convert the user's voice into text and receive the inquiry. Furthermore, it can refer to the user's past inquiry history to select the most appropriate response method. Step 2: The detection unit analyzes the consultation content received by the reception unit and detects specific words. For example, it can use a generation AI to analyze the consultation content and detect specific words such as "I want to commit suicide," and issue an alert. It can also analyze the user's statements and search history to detect specific words. Furthermore, it can manage a list of specific words and detect words based on that list. Step 3: The support unit provides support via push notifications based on specific words detected by the detection unit. For example, it can use generative AI to provide appropriate advice and support to the user. It can also analyze the user's statements and search history to provide appropriate advice. Furthermore, it can analyze the user's psychological state and adjust the way support is expressed based on their emotions. Step 4: The notification unit sends an SOS notification to a counselor if the support unit is unable to handle the situation. For example, it can use a generation AI to analyze the user's condition and send an SOS notification to a counselor if it determines that the situation is urgent. It can also analyze the user's statements and search history and send an SOS notification to a counselor if it determines that the situation is urgent. Furthermore, it can estimate the user's emotions and immediately send a notification to a counselor if the situation is urgent.

[0065] (Example of form 2) The medical application utilizing a generative AI counselor according to an embodiment of the present invention is a system designed to address the serious problem of increasing suicide rates among young people. This system aims to save the lives of people in need of support by utilizing a medical application to create a generative AI counselor. The generative AI counselor not only receives consultations but also provides active support. For example, when it detects a specific word being searched or a statement being made, the generative AI provides support via push notification. It also has a backup function that sends an SOS notification to a human support provider, such as a counselor, if the situation is difficult to handle. For example, the generative AI counselor receives consultations from users. When a user searches for or makes a statement using a specific word such as "I want to commit suicide," the generative AI detects the content and provides support via push notification. The generative AI analyzes the user's statements and search history to provide appropriate advice and support. Furthermore, the generative AI counselor has a backup function that sends an SOS notification to a human support provider, such as a counselor, if the situation is difficult to handle. For example, if the generative AI analyzes the user's condition and determines that it is highly urgent, it sends an SOS notification to a counselor. In this way, the system provides support to save users' lives in conjunction with human support. This mechanism can address the serious problem of the increasing number of suicides among young people. The generated AI counselor can accept consultations 24 hours a day and can respond to multiple users simultaneously. This allows users to easily seek advice and receive the best possible guidance. Furthermore, the generated AI counselor can provide appropriate support while protecting user privacy. For example, it can manage student health check results and reasons for absence in cooperation with schools. This allows for understanding students' health status and providing necessary support. In addition, the generated AI counselor can detect anomalies from web search history and message history and provide proactive support. For example, if a user searches for something like "I want to commit suicide," the generated AI will detect the content and provide support via push notification.In this way, by utilizing a generated AI counselor, we can address the serious problem of the rising suicide rate among young people and save lives. The generated AI counselor can accept consultations 24 hours a day and can handle multiple users simultaneously. It also has a backup function that sends an SOS notification to human support such as a counselor if it is unable to handle the situation. This allows us to provide support that can save users' lives. Thus, a medical app that utilizes a generated AI counselor can address the serious problem of the rising suicide rate among young people and save lives.

[0066] The medical application utilizing the generative AI counselor according to this embodiment comprises a reception unit, a detection unit, a support unit, and a notification unit. The reception unit receives consultations from users. For example, the user can input the consultation content through the application. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can use voice recognition technology to convert the user's voice into text and receive the consultation content. The reception unit can also allow the user to directly input the consultation content using text input. Furthermore, the reception unit can refer to the user's past consultation history and select the most appropriate response method. For example, the reception unit can provide appropriate responses to similar problems based on the user's past consultation content. The detection unit analyzes the consultation content received by the reception unit and detects specific words. For example, the detection unit can use generative AI to analyze the consultation content and detect specific words. For example, the detection unit can issue an alert when the generative AI detects a specific word such as "I want to commit suicide." The detection unit can also analyze the user's statements and search history to detect specific words. For example, the detection unit can detect if a user performs a search such as "I want to commit suicide" and issue an alert. Furthermore, the detection unit can manage a list of specific words and detect words based on that list. For example, the detection unit can periodically update the list of specific words to respond to the latest trends. The support unit provides support via push notifications based on the specific words detected by the detection unit. The support unit can provide appropriate advice and support to the user using, for example, generative AI. For example, the support unit can use generative AI to analyze the user's statements and search history and provide appropriate advice. The support unit can also analyze the user's state and provide appropriate support. For example, the support unit can use generative AI to analyze the user's psychological state and provide appropriate support. Furthermore, the support unit can estimate the user's emotions and adjust the way support is expressed based on those emotions.For example, the support unit can use a generative AI to analyze the user's emotions and provide support using gentle language. The notification unit sends an SOS notification to a counselor when the support unit is unable to handle the situation. The notification unit can, for example, use a generative AI to analyze the user's condition and send an SOS notification to a counselor if it determines that the situation is urgent. For example, the notification unit can use a generative AI to analyze the user's statements and search history and send an SOS notification to a counselor if it determines that the situation is urgent. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on those emotions. For example, the notification unit can use a generative AI to analyze the user's emotions and immediately send a notification to a counselor if it determines that the situation is urgent. As a result, the medical application utilizing the generative AI counselor according to this embodiment can efficiently receive, analyze, support, and notify users of their consultations.

[0067] The reception desk receives inquiries from users. For example, users can input their inquiries through an app. The reception desk can also support multiple input methods, including voice and text input. Specifically, it can use speech recognition technology to convert user speech into text and receive inquiries. Speech recognition technology analyzes user speech with high accuracy and converts it into text using natural language processing technology. This allows users to easily communicate their inquiries by voice. Users can also directly input their inquiries using text input. Text input is a method where users input their inquiries using a keyboard or touchscreen, and is usable even in environments where voice input is difficult. Furthermore, the reception desk can refer to the user's past inquiry history and select the most appropriate response. For example, the reception desk can save the user's past inquiries in a database and provide appropriate responses to similar problems. This allows users to receive consistent support and accurate advice based on their past inquiries. To protect user privacy, the reception desk encrypts data and controls access to prevent inquiries from being leaked to third parties. Furthermore, the reception department can quickly process user inquiries and forward them to the appropriate department or person in charge. This allows users to receive prompt and appropriate responses, enabling them to seek advice with peace of mind.

[0068] The detection unit analyzes the content of consultations received by the reception unit and detects specific words. For example, the detection unit can use generative AI to analyze the content of consultations and detect specific words. The generative AI utilizes natural language processing technology to analyze the user's consultation content in detail and extract important keywords and phrases. For example, the detection unit can issue an alert when the generative AI detects a specific word such as "I want to commit suicide." The generative AI can understand the content of the user's statements in context and detect dangerous signs early. The detection unit can also analyze the content of the user's statements and search history to detect specific words. For example, if a user performs a search for "I want to commit suicide," the unit can detect this content and issue an alert. Furthermore, the detection unit manages a list of specific words and can detect words based on this list. The list of specific words is regularly updated under the supervision of experts to respond to the latest trends and dangerous signs. As a result, the detection unit can quickly and accurately analyze the content of user consultations and detect highly urgent problems early. The detection unit can provide information to take appropriate action based on the specific word detected, thereby ensuring user safety.

[0069] The support unit provides support via push notifications based on specific words detected by the detection unit. For example, the support unit can use generative AI to provide appropriate advice and support to users. The generative AI analyzes the user's statements and search history to provide appropriate advice. For example, if a user says they are "stressed," the generative AI can suggest stress relief methods and relaxation techniques. The support unit can also analyze the user's state and provide appropriate support. The generative AI analyzes the user's psychological state and provides appropriate support. For example, if a user is feeling anxious, the generative AI can offer advice on relaxation or recommend consulting a mental health professional. Furthermore, the support unit can estimate the user's emotions and adjust the way support is expressed based on those emotions. The generative AI analyzes the user's emotions and provides support using gentle language. For example, if a user is sad, the generative AI can send words of encouragement and comforting messages. This allows the support unit to provide appropriate advice and support to users, reducing their psychological burden. The support department can collect user feedback and continuously improve the accuracy and effectiveness of its support services. This allows the support department to provide more effective support to users and increase user satisfaction.

[0070] The notification unit sends an SOS notification to a counselor when the support unit is unable to handle the situation. For example, the notification unit can use a generative AI to analyze the user's state and send an SOS notification to a counselor if it determines that the situation is urgent. The generative AI analyzes the user's statements and search history and sends an SOS notification to a counselor if it determines that the situation is urgent. For example, if a user says "I want to commit suicide," the generative AI can immediately determine the urgency and send a notification to a counselor. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on those emotions. The generative AI analyzes the user's emotions and immediately sends a notification to a counselor if the situation is urgent. For example, if a user is experiencing extreme anxiety or panic, the generative AI can determine the urgency and quickly send a notification to a counselor. The notification unit provides counselors with detailed information about the user to support a quick and appropriate response. This allows counselors to accurately understand the user's situation and provide appropriate support. The notification department will encrypt data and implement access controls to protect user privacy and prevent the disclosure of consultation details to third parties. This will allow the notification department to ensure user safety and respond quickly and appropriately in emergencies.

[0071] The detection unit manages a list of specific words. For example, the detection unit can periodically update this list of specific words to keep up with the latest trends. For instance, it can use generative AI to analyze user statements and search history and add new specific words to the list. Furthermore, the detection unit can dynamically update the list of specific words to keep up with the latest trends. For example, it can obtain the latest trend information from social media and news sites and update the list of specific words. In addition, the detection unit can improve detection accuracy by managing the list of specific words. For example, it can analyze user statements and search history based on the list of specific words to detect specific words. Thus, managing the list of specific words improves detection accuracy.

[0072] The detection unit analyzes the user's statements and search history. For example, the detection unit can use generative AI to analyze the user's statements and detect specific words. For instance, the detection unit can use generative AI to analyze the user's statements and detect specific words such as "I want to commit suicide." The detection unit can also analyze the user's search history and detect specific words. For example, the detection unit can use generative AI to analyze the user's search history and detect if a search for "I want to commit suicide" was performed. Furthermore, by analyzing the user's statements and search history, the detection unit can provide appropriate support. For example, the detection unit can use generative AI to analyze the user's statements and search history and provide appropriate advice and support. This allows for the provision of appropriate support by analyzing the user's statements and search history.

[0073] The support department analyzes the user's condition and provides appropriate advice and support. For example, the support department can use generative AI to analyze the user's psychological state and provide appropriate advice. For instance, the support department can use generative AI to analyze the user's statements and search history to understand the user's psychological state and provide appropriate advice. The support department can also analyze the user's health condition and provide appropriate support. For example, the support department can use generative AI to analyze the user's health check results and reasons for absence and provide appropriate support. Furthermore, the support department can estimate the user's emotions and adjust the way support is expressed based on those emotions. For example, the support department can use generative AI to analyze the user's emotions and provide support using gentle language. In this way, by analyzing the user's condition, appropriate advice and support can be provided.

[0074] The notification unit sends an SOS notification to a counselor when it determines that the situation is highly urgent. For example, the notification unit can use a generative AI to analyze the user's state and send an SOS notification to a counselor when it determines that the situation is highly urgent. For example, the notification unit can use a generative AI to analyze the user's statements and search history and send an SOS notification to a counselor when it determines that the situation is highly urgent. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on those emotions. For example, the notification unit can use a generative AI to analyze the user's emotions and immediately send a notification to a counselor if the situation is highly urgent. Furthermore, the notification unit can refer to the user's past consultation history to determine the urgency. For example, based on the user's past consultation history, the notification unit can prioritize notifying counselors of highly urgent issues. This enables a rapid response by sending an SOS notification to a counselor when the situation is highly urgent.

[0075] The reception desk accepts consultations 24 hours a day. For example, the reception desk can implement a shift system to provide 24-hour support. For instance, multiple staff members can work in shifts to ensure 24-hour service. Alternatively, the reception desk can utilize an automated response system to accept consultations 24 hours a day. For example, an automated response system using AI generation can be implemented to receive user consultations 24 hours a day. Furthermore, the reception desk can record user consultation details and refer to them during subsequent consultations. For example, the reception desk can save user consultation details in a database and refer to past consultation details during subsequent consultations. This allows users to consult at any time by accepting consultations 24 hours a day.

[0076] The reception desk can handle multiple users simultaneously. For example, the reception desk can use a chatbot to handle multiple users at once. For instance, the reception desk can implement a chatbot using generative AI to receive inquiries from multiple users simultaneously. Furthermore, the reception desk can use a multitasking system to handle multiple users simultaneously. For example, the reception desk can implement a multitasking system using generative AI to process inquiries from multiple users simultaneously. In addition, the reception desk can prioritize user inquiries and handle them efficiently. For example, the reception desk can use generative AI to analyze user inquiries and prioritize those with high urgency. This enables efficient consultation reception by handling multiple users simultaneously.

[0077] The reception desk estimates the user's emotions and adjusts the consultation process based on those estimated emotions. For example, the reception desk can use generative AI to estimate the user's emotions and adjust the consultation process accordingly. For instance, the reception desk can use generative AI to analyze the user's voice or text and estimate their emotions. The reception desk can also use gentle language or a calm response based on the user's emotions. For example, the reception desk can use generative AI to analyze the user's emotions and respond to anxious users with gentle language and angry users with a calm response. Furthermore, the reception desk can also prioritize consultations based on the user's emotions. For example, the reception desk can use generative AI to analyze the user's emotions and prioritize consultations from users who indicate a high level of urgency. This allows for more appropriate responses by adjusting the consultation process based on the user's emotions.

[0078] The reception department selects the most suitable reception method by referring to the user's past consultation history at the time of reception. For example, the reception department can use generative AI to analyze the user's past consultation history and select the most suitable reception method. For example, the reception department can use generative AI to analyze the user's past consultation content and provide appropriate responses to similar problems. The reception department can also consider the compatibility with a specific counselor based on the user's past consultation history when making a reception request. For example, the reception department can use generative AI to analyze the user's past consultation history and select the most suitable counselor by considering compatibility with a specific counselor. Furthermore, the reception department can analyze the user's past consultation history and propose the most effective support method. For example, the reception department can use generative AI to analyze the user's past consultation history and propose the most effective support method. In this way, the reception department can select the most suitable reception method by referring to past consultation history.

[0079] The reception desk filters users based on their current living situation and areas of interest during the reception process. For example, the reception desk can use generative AI to analyze and filter users based on their current living situation and areas of interest. For instance, the reception desk can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest appropriate consultation topics. Furthermore, the reception desk can analyze users' areas of interest (hobbies, interests, learning content, etc.) and prioritize receiving consultations related to those areas. For example, the reception desk can use generative AI to analyze the user's areas of interest and prioritize receiving consultations related to those areas. In addition, the reception desk can select the most suitable counselor based on the user's living situation and areas of interest. This allows for the suggestion of appropriate consultation topics by filtering based on living situation and areas of interest.

[0080] The reception desk estimates the user's emotions and determines the priority of consultations based on those estimated emotions. For example, the reception desk can use generative AI to estimate the user's emotions and determine the priority of consultations based on those emotions. For example, the reception desk can use generative AI to analyze the user's voice or text and estimate their emotions. The reception desk can also prioritize consultations with high urgency based on the user's emotions. For example, the reception desk can use generative AI to analyze the user's emotions and prioritize consultations from users who are feeling strong anxiety. Furthermore, the reception desk can dynamically adjust the priority of consultations based on the user's emotions. For example, the reception desk can use generative AI to analyze the user's emotions and immediately accept consultations from users who are showing high urgency. This allows for a rapid response to high-urgency consultations by prioritizing based on emotions.

[0081] The reception desk prioritizes accepting consultations that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can use generative AI to analyze the user's geographical location information and prioritize accepting consultations related to a specific region. Furthermore, the reception desk can also propose consultation content tailored to the characteristics and problems of the region, based on the user's geographical location information. For example, the reception desk can use generative AI to analyze the user's geographical location information and propose consultation content tailored to the characteristics and problems of the region. In addition, the reception desk can consider the user's geographical location information and collaborate with local counselors to provide the best possible response. This allows for the priority acceptance of consultations related to a specific region by considering geographical location information.

[0082] The reception desk analyzes the user's social media activity upon receiving a call and accepts relevant inquiries. For example, the reception desk can use generative AI to analyze the user's social media activity and accept relevant inquiries. For instance, the reception desk can use generative AI to analyze the user's social media posts and suggest relevant inquiries. Furthermore, the reception desk can prioritize accepting appropriate inquiries based on the user's frequency and content of social media activity. For example, the reception desk can use generative AI to analyze the user's social media activity and prioritize accepting appropriate inquiries. Additionally, the reception desk can consider the user's social media friendships and communities when accepting relevant inquiries. For example, the reception desk can use generative AI to analyze the user's social media friendships and communities and suggest relevant inquiries. This allows for the suggestion of relevant inquiries by analyzing social media activity.

[0083] The detection unit estimates the user's emotions and adjusts the detection method for specific words based on the estimated user emotions. For example, the detection unit can use generative AI to estimate the user's emotions and adjust the detection method for specific words based on those emotions. For example, the detection unit can use generative AI to analyze the user's voice or text and estimate the user's emotions. The detection unit can also dynamically adjust the detection method for specific words based on the user's emotions. For example, the detection unit can use generative AI to analyze the user's emotions and prioritize detecting specific words related to anxiety for users who are feeling anxious. Furthermore, the detection unit can also determine the priority of words to detect based on the user's emotions. For example, the detection unit can use generative AI to analyze the user's emotions and prioritize detecting words for users who are showing a high level of urgency. This allows for the detection of more appropriate words by adjusting the detection method based on emotions.

[0084] The detection unit improves detection accuracy by referring to the user's past statements and search history when detection occurs. For example, the detection unit can use generative AI to analyze the user's past statements and improve the detection accuracy of specific words. For example, the detection unit can use generative AI to analyze the user's past statements and improve the accuracy of detecting specific words. The detection unit can also analyze the user's search history and prioritize the detection of relevant specific words. For example, the detection unit can use generative AI to analyze the user's search history and prioritize the detection of relevant specific words. Furthermore, the detection unit can dynamically update the list of specific words based on the user's past statements and search history. For example, the detection unit can use generative AI to analyze the user's past statements and search history and dynamically update the list of specific words. This improves detection accuracy by referring to past statements and search history.

[0085] The detection unit dynamically updates a list of specific words upon detection to respond to the latest trends. For example, the detection unit can periodically update the list of specific words using a generative AI to respond to the latest trends. For instance, the generative AI can obtain the latest trend information from social media and news sites and update the list of specific words. Furthermore, the detection unit can update the list of specific words in real time based on user statements and search history. For example, the generative AI can analyze user statements and search history and update the list of specific words in real time. In addition, the detection unit can respond to the latest trends by dynamically updating the list of specific words. For example, the generative AI can dynamically update the list of specific words based on the latest trend information. This allows the detection unit to respond to the latest trends by dynamically updating the list of specific words.

[0086] The detection unit estimates the user's emotions and determines the priority of words to detect based on the estimated emotions. For example, the detection unit can use generative AI to estimate the user's emotions and determine the priority of words to detect based on those emotions. For example, the detection unit can use generative AI to analyze the user's voice or text and estimate the user's emotions. The detection unit can also dynamically adjust the priority of words to detect based on the user's emotions. For example, the detection unit can use generative AI to analyze the user's emotions and prioritize detecting words related to anxiety for users who are feeling anxious. Furthermore, the detection unit can also determine the priority of words to detect based on the user's emotions. For example, the detection unit can use generative AI to analyze the user's emotions and prioritize detecting words for users who are showing a high level of urgency. In this way, important words can be detected preferentially by determining the priority of words based on emotions.

[0087] The detection unit prioritizes detecting highly relevant words by considering the user's geographical location information during detection. For example, the detection unit can analyze the user's geographical location information using generative AI and prioritize detecting highly relevant words. For instance, the detection unit can use generative AI to analyze the user's geographical location information and prioritize detecting words related to a specific region. Furthermore, the detection unit can prioritize detecting words that correspond to regional characteristics and issues based on the user's geographical location information. For example, the detection unit can use generative AI to analyze the user's geographical location information and prioritize detecting words that correspond to regional characteristics and issues. Additionally, the detection unit can prioritize detecting words related to regional trends by considering the user's geographical location information. For example, the detection unit can use generative AI to analyze the user's geographical location information and prioritize detecting words related to regional trends. This allows for the priority detection of region-related words by considering geographical location information.

[0088] The detection unit analyzes the user's social media activity and detects relevant words upon detection. For example, the detection unit can use generative AI to analyze the user's social media activity and detect relevant words. For instance, the detection unit can use generative AI to analyze the user's social media posts and detect relevant words. Furthermore, the detection unit can prioritize the detection of appropriate words based on the frequency and content of the user's social media activity. For example, the detection unit can use generative AI to analyze the frequency and content of the user's social media activity and prioritize the detection of appropriate words. Additionally, the detection unit can consider the user's social media friendships and communities when detecting relevant words. For example, the detection unit can use generative AI to analyze the user's social media friendships and communities and detect relevant words. This allows for the detection of relevant words by analyzing social media activity.

[0089] The support unit estimates the user's emotions and adjusts the way support is expressed based on those emotions. For example, the support unit can use generative AI to estimate the user's emotions and adjust the way support is expressed based on those emotions. For example, the support unit can use generative AI to analyze the user's voice or text and estimate their emotions. The support unit can also use gentle language or a calm response based on the user's emotions. For example, the support unit can use generative AI to analyze the user's emotions and respond to anxious users with gentle language and angry users with a calm response. Furthermore, the support unit can dynamically adjust the way support is expressed based on the user's emotions. For example, the support unit can use generative AI to analyze the user's emotions and adjust the way support is expressed according to those emotions. This allows for the provision of more appropriate support by adjusting the way support is expressed based on emotions.

[0090] The support department provides optimal advice by referring to the user's past consultation history during support sessions. For example, the support department can use generative AI to analyze the user's past consultation history and provide optimal advice. For instance, the support department can use generative AI to analyze the user's past consultation history and provide appropriate advice for similar problems. Furthermore, the support department can provide advice considering the user's compatibility with a specific counselor based on their past consultation history. For example, the support department can use generative AI to analyze the user's past consultation history and provide optimal advice considering their compatibility with a specific counselor. In addition, the support department can analyze the user's past consultation history and propose the most effective support method. For example, the support department can use generative AI to analyze the user's past consultation history and propose the most effective support method. This allows the support department to provide optimal advice by referring to past consultation history.

[0091] The support department customizes the means of support based on the user's current living situation. For example, the support department can use generative AI to analyze the user's current living situation and customize the means of support. For example, the support department can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest appropriate means of support. The support department can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and provide relevant means of support. For example, the support department can use generative AI to analyze the user's areas of interest and provide relevant means of support. Furthermore, the support department can select the most suitable counselor based on the user's living situation and areas of interest. For example, the support department can use generative AI to analyze the user's living situation and areas of interest and select the most suitable counselor. This allows for the provision of more appropriate support by customizing the means of support based on the user's living situation.

[0092] The support department estimates the user's emotions and determines support priorities based on those estimated emotions. For example, the support department can use generative AI to estimate user emotions and determine support priorities based on those emotions. For instance, the support department can use generative AI to analyze the user's voice or text and estimate their emotions. Furthermore, the support department can prioritize providing support for high-urgency situations based on user emotions. For example, the support department can use generative AI to analyze user emotions and prioritize support for users experiencing high levels of anxiety. In addition, the support department can dynamically adjust support priorities based on user emotions. For example, the support department can use generative AI to analyze user emotions and immediately provide support to users indicating high urgency. This allows for a rapid response to high-urgency support requests by prioritizing support based on emotions.

[0093] The support department selects the optimal support method when providing support, taking into account the user's geographical location. For example, the support department can use generative AI to analyze the user's geographical location and select the optimal support method. For instance, the support department can use generative AI to analyze the user's geographical location and propose support methods relevant to a specific region. Furthermore, the support department can provide support methods tailored to regional characteristics and problems based on the user's geographical location. For example, the support department can use generative AI to analyze the user's geographical location and provide support methods tailored to regional characteristics and problems. In addition, the support department can collaborate with local counselors to provide support, taking the user's geographical location into consideration. For example, the support department can use generative AI to analyze the user's geographical location and collaborate with local counselors to provide optimal support. This allows for the provision of region-specific support methods by considering geographical location.

[0094] The support department analyzes the user's social media activity and provides relevant support during support sessions. For example, the support department can use generative AI to analyze the user's social media activity and provide relevant support. For instance, the support department can use generative AI to analyze the user's social media posts and provide relevant support. Furthermore, the support department can suggest appropriate support methods based on the user's frequency and content of social media activity. For example, the support department can use generative AI to analyze the user's social media activity and suggest appropriate support methods. Additionally, the support department can consider the user's social media friendships and communities when providing relevant support. For example, the support department can use generative AI to analyze the user's social media friendships and communities and provide relevant support. This allows the support department to provide relevant support by analyzing social media activity.

[0095] The notification unit estimates the user's emotions and adjusts the method of sending SOS notifications based on the estimated emotions. For example, the notification unit can use generative AI to estimate the user's emotions and adjust the method of sending SOS notifications based on those emotions. For example, the notification unit can use generative AI to analyze the user's voice or text and estimate the user's emotions. Furthermore, based on the user's emotions, the notification unit can immediately send an SOS notification to a counselor in cases of high urgency. For example, the notification unit can use generative AI to analyze the user's emotions and immediately send a notification to a counselor if the user is experiencing strong anxiety. In addition, the notification unit can send SOS notifications to multiple counselors simultaneously based on the user's emotions. For example, the notification unit can use generative AI to analyze the user's emotions and simultaneously send notifications to multiple counselors in cases of high urgency. This allows for more appropriate notifications by adjusting the method of sending SOS notifications based on emotions.

[0096] The notification unit determines the urgency of a situation by referring to the user's past consultation history when sending a notification. For example, the notification unit can use a generation AI to analyze the user's past consultation history and determine the urgency. For example, the notification unit can use a generation AI to analyze the user's past consultation content and prioritize notifications for consultations with high urgency. The notification unit can also consider the user's compatibility with a specific counselor based on their past consultation history when sending notifications. For example, the notification unit can use a generation AI to analyze the user's past consultation history and send notifications to the most suitable counselor, considering their compatibility. Furthermore, the notification unit can analyze the user's past consultation history and suggest the most effective notification method. For example, the notification unit can use a generation AI to analyze the user's past consultation history and suggest the most effective notification method. This makes it possible to send notifications with high urgency by referring to past consultation history.

[0097] The notification unit customizes the notification method based on the user's current living situation when a notification is sent. For example, the notification unit can use generative AI to analyze the user's current living situation and customize the notification method. For example, the notification unit can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest an appropriate notification method. The notification unit can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and provide relevant notification methods. For example, the notification unit can use generative AI to analyze the user's areas of interest and provide relevant notification methods. Furthermore, the notification unit can select the most suitable counselor based on the user's living situation and areas of interest. For example, the notification unit can use generative AI to analyze the user's living situation and areas of interest and select the most suitable counselor. By customizing the notification method based on the user's living situation, more appropriate notifications become possible.

[0098] The notification unit estimates the user's emotions and determines notification priorities based on those estimated emotions. For example, the notification unit can use generative AI to estimate user emotions and determine notification priorities based on those emotions. For instance, the notification unit can use generative AI to analyze the user's voice or text and estimate their emotions. Furthermore, the notification unit can prioritize urgent notifications based on user emotions. For example, the notification unit can use generative AI to analyze user emotions and prioritize notifications for users experiencing high levels of anxiety. Additionally, the notification unit can dynamically adjust notification priorities based on user emotions. For example, the notification unit can use generative AI to analyze user emotions and immediately send notifications to users indicating high levels of urgency. This allows for a rapid response to urgent notifications by prioritizing them based on emotions.

[0099] The notification unit selects the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can use a generative AI to analyze the user's geographical location information and select the optimal notification method. For instance, the generative AI can analyze the user's geographical location information and propose a notification method relevant to a specific region. Furthermore, the notification unit can provide notification methods tailored to regional characteristics and issues based on the user's geographical location information. For example, the generative AI can analyze the user's geographical location information and provide notification methods tailored to regional characteristics and issues. In addition, the notification unit can consider the user's geographical location information and collaborate with local counselors to send notifications. For example, the generative AI can analyze the user's geographical location information and collaborate with local counselors to provide the most appropriate notification. This allows the system to provide region-specific notification methods by considering geographical location information.

[0100] The notification unit analyzes the user's social media activity and sends relevant notifications when a notification is sent. For example, the notification unit can use generative AI to analyze the user's social media activity and send relevant notifications. For instance, the notification unit can use generative AI to analyze the user's social media posts and send relevant notifications. Furthermore, the notification unit can suggest appropriate notification methods based on the user's frequency and content of social media activity. For example, the notification unit can use generative AI to analyze the user's social media activity frequency and content and suggest appropriate notification methods. Additionally, the notification unit can consider the user's social media friendships and communities when sending relevant notifications. For example, the notification unit can use generative AI to analyze the user's social media friendships and communities and send relevant notifications. This allows for relevant notifications by analyzing social media activity.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can select the most appropriate response method by referring to the user's past consultation history when receiving a user's inquiry. For example, the reception desk can use generative AI to analyze the user's past consultation content and provide appropriate responses to similar problems. Furthermore, the reception desk can consider the user's compatibility with a specific counselor based on their past consultation history when accepting inquiries. In addition, the reception desk can analyze the user's past consultation history and propose the most effective support method. This allows the reception desk to select the optimal method of handling inquiries by referring to past consultation history.

[0103] The detection unit can analyze user statements and search history while considering the user's current lifestyle and areas of interest. For example, the detection unit can use generative AI to analyze the user's lifestyle (work, family, health, etc.) and detect appropriate words. It can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and prioritize the detection of related words. Furthermore, the detection unit can dynamically update a list of specific words based on the user's lifestyle and areas of interest. This allows for the detection of more appropriate words by considering the user's lifestyle and areas of interest.

[0104] The support unit can estimate the user's emotions and adjust the way it expresses support based on those emotions. For example, the support unit can use generative AI to analyze the user's voice or text and estimate their emotions. Furthermore, the support unit can use gentle language or a calm approach based on the user's emotions. In addition, the support unit can dynamically adjust the way it expresses support based on the user's emotions. This allows for the provision of more appropriate support by adjusting the expression of support based on emotions.

[0105] The notification unit can estimate the user's emotions and adjust the method of sending SOS notifications based on those emotions. For example, the notification unit can use generative AI to analyze the user's voice or text and estimate their emotions. Based on the user's emotions, the notification unit can immediately send an SOS notification to a counselor if the situation is urgent. Furthermore, based on the user's emotions, the notification unit can simultaneously send SOS notifications to multiple counselors. This allows for more appropriate notifications by adjusting the method of sending SOS notifications based on emotions.

[0106] The reception desk can prioritize receiving consultations that are highly relevant to the user, taking into account the user's geographical location. For example, the reception desk can use generative AI to analyze the user's geographical location and prioritize consultations related to a specific region. Furthermore, the reception desk can suggest consultation content tailored to the characteristics and problems of the region, based on the user's geographical location. In addition, the reception desk can collaborate with local counselors, taking the user's geographical location into consideration. This allows for the priority of receiving consultations related to the region by considering geographical location.

[0107] The detection unit can analyze a user's social media activity and detect relevant words. For example, it can use generative AI to analyze a user's social media posts and detect relevant words. Furthermore, the detection unit can prioritize the detection of appropriate words based on the frequency and content of the user's social media activity. In addition, the detection unit can consider the user's social media friendships and communities to detect relevant words. Thus, by analyzing social media activity, relevant words can be detected.

[0108] The support department can customize support methods based on the user's current living situation. For example, the support department can use generative AI to analyze the user's living situation (work, family, health, etc.) and suggest appropriate support methods. It can also analyze the user's areas of interest (hobbies, interests, learning content, etc.) and provide relevant support methods. Furthermore, the support department can select the most suitable counselor based on the user's living situation and areas of interest. This allows for the provision of more appropriate support by customizing support methods based on the user's living situation.

[0109] The notification unit can determine the urgency of a situation by referring to the user's past consultation history. For example, the notification unit can use a generation AI to analyze the user's past consultation history and prioritize notifications for highly urgent consultations. Furthermore, the notification unit can consider the user's compatibility with a specific counselor based on their past consultation history and send notifications accordingly. In addition, the notification unit can analyze the user's past consultation history and suggest the most effective notification method. This allows for the provision of highly urgent notifications by referencing past consultation history.

[0110] The detection unit can estimate the user's emotions and determine the priority of words to detect based on the estimated emotions. For example, the detection unit can analyze the user's voice or text using generative AI to estimate the user's emotions. Furthermore, the detection unit can dynamically adjust the priority of words to detect based on the user's emotions. In addition, the detection unit can determine the priority of words to detect based on the user's emotions. This allows for the priority detection of important words by prioritizing them based on emotions.

[0111] The support department can estimate the user's emotions and determine the priority of support based on those emotions. For example, the support department can use generative AI to analyze the user's voice or text and estimate their emotions. Furthermore, the support department can prioritize providing urgent support based on the user's emotions. In addition, the support department can dynamically adjust the priority of support based on the user's emotions. This allows for a quicker response to urgent support requests by prioritizing support based on emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives inquiries from users. For example, users can input their inquiries through the app, and the system supports multiple input methods, such as voice input and text input. It can also use speech recognition technology to convert the user's voice into text and receive the inquiry. Furthermore, it can refer to the user's past inquiry history to select the most appropriate response method. Step 2: The detection unit analyzes the consultation content received by the reception unit and detects specific words. For example, it can use a generation AI to analyze the consultation content and detect specific words such as "I want to commit suicide," and issue an alert. It can also analyze the user's statements and search history to detect specific words. Furthermore, it can manage a list of specific words and detect words based on that list. Step 3: The support unit provides support via push notifications based on specific words detected by the detection unit. For example, it can use generative AI to provide appropriate advice and support to the user. It can also analyze the user's statements and search history to provide appropriate advice. Furthermore, it can analyze the user's psychological state and adjust the way support is expressed based on their emotions. Step 4: The notification unit sends an SOS notification to a counselor if the support unit is unable to handle the situation. For example, it can use a generation AI to analyze the user's condition and send an SOS notification to a counselor if it determines that the situation is urgent. It can also analyze the user's statements and search history and send an SOS notification to a counselor if it determines that the situation is urgent. Furthermore, it can estimate the user's emotions and immediately send a notification to a counselor if the situation is urgent.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, detection unit, support unit, and notification 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 users. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects specific words. The support unit is implemented by the control unit 46A of the smart device 14 and provides appropriate advice and support to the user. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an SOS notification to a counselor in cases of high urgency. 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.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, detection unit, support unit, and notification 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 detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects specific words. The support unit is implemented by the control unit 46A of the smart glasses 214 and provides appropriate advice and support to the user. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an SOS notification to a counselor in cases of high urgency. 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.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, detection unit, support unit, and notification 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 detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects specific words. The support unit is implemented by the control unit 46A of the headset terminal 314 and provides appropriate advice and support to the user. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an SOS notification to a counselor in cases of high urgency. 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.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, detection unit, support unit, and notification 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 users. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects specific words. The support unit is implemented by the control unit 46A of the robot 414 and provides appropriate advice and support to the user. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an SOS notification to a counselor in cases of high urgency. 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception desk that handles inquiries from users, A detection unit analyzes the content of consultations received by the reception unit and detects specific words, A support unit that provides support via push notification based on a specific word detected by the aforementioned detection unit, The system includes a notification unit that sends an SOS notification to a counselor when the support unit is unable to resolve the issue. A system characterized by the following features. (Note 2) The detection unit, Manage a list of specific words The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit, Analyze user comments and search history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is Analyze the user's condition and provide appropriate advice and support. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, If it is determined to be an urgent matter, an SOS notification will be sent to the counselor. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We accept consultations 24 hours a day. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Supporting multiple users simultaneously 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 consultation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During the registration process, the system will refer to the user's past consultation history to select the most appropriate registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During registration, filtering is performed based on the user's current lifestyle 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 determines the priority of inquiries to be accepted 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 a user submits a request, the system prioritizes accepting inquiries that are highly relevant to their situation, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and accepts related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit, The system estimates the user's emotions and adjusts the detection method for specific words based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit, When detection occurs, the accuracy of the detection is improved by referring to the user's past statements and search history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit, Upon detection, the list of specific words is dynamically updated to reflect the latest trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit, It estimates the user's emotions and determines the priority of words to detect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit, During detection, the system prioritizes detecting highly relevant words by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, Upon detection, the system analyzes the user's social media activity and identifies relevant keywords. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is It estimates the user's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is When providing support, we refer to the user's past inquiries to offer the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) 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 24) 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 25) The aforementioned support unit is During support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts how SOS notifications are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, the urgency of the situation is determined by referring to the user's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity and sends relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 desk that handles inquiries from users, A detection unit analyzes the content of consultations received by the reception unit and detects specific words, A support unit that provides support via push notification based on a specific word detected by the aforementioned detection unit, The system includes a notification unit that sends an SOS notification to a counselor when the support unit is unable to resolve the issue. A system characterized by the following features.

2. The detection unit, Manage a list of specific words The system according to feature 1.

3. The detection unit, Analyze user comments and search history. The system according to feature 1.

4. The aforementioned support unit is Analyze the user's condition and provide appropriate advice and support. The system according to feature 1.

5. The aforementioned notification unit, If it is determined to be an urgent matter, an SOS notification will be sent to the counselor. The system according to feature 1.

6. The aforementioned reception unit is We accept consultations 24 hours a day. The system according to feature 1.

7. The aforementioned reception unit is Supporting multiple users simultaneously The system according to feature 1.

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

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