system

Abstract

The system according to this embodiment aims to provide an AI counselor that can respond to the diverse backgrounds and needs of those seeking advice. [Solution] The system according to this embodiment comprises a reception unit, a selection unit, and a provision unit. The reception unit receives input for the consultation content. The selection unit selects the most suitable AI consultant based on the consultation content entered by the reception unit. The provision unit has the AI ​​consultant selected by the selection unit provide support for the consultation and offer specific advice and solutions.

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, the method including 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, since a single AI responds to consultations, there is a problem that it cannot sufficiently respond to the diverse backgrounds and needs of the counselors.

[0005] The system according to the embodiment aims to provide an AI counselor that can respond to the diverse backgrounds and needs of the counselors.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a selection unit, and a provision unit. The reception unit inputs the consultation content. The selection unit selects an optimal AI counselor based on the consultation content input by the reception unit. The provision unit has the selected AI counselor empathize with the consultation and provide specific advice and solutions. [Effects of the Invention]

[0007] The system according to this embodiment can provide an AI counselor that can respond to the diverse backgrounds and needs of those seeking advice. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 diverse AI counseling system according to the embodiment of the present invention is not a single, impersonal AI, but a system that provides an AI counseling service with a wide variety of backgrounds. In this system, the user inputs the content of their consultation, and the AI ​​selects the most suitable AI counselor based on that content. The selected AI counselor will listen to the user's concerns and provide highly empathetic support. Examples include an AI of an African man living in Africa, an AI of a Japanese high school student who is LGB, an AI of a priest or monk, and an AI of a historical figure. When the user inputs the content of their consultation, they input their worries and the content they want to discuss in detail. For example, possible consultation topics include "I'm being bullied at school," "I want to talk to my family and transfer schools," or "I want to get revenge." This information is input into the AI. Next, the AI ​​analyzes the input content of the consultation and selects the most suitable AI counselor. The AI ​​selects the AI ​​counselor that is most suitable for the consultation content based on past consultation data and solutions from other users. For example, if the user is being bullied at school, an AI counselor with similar experiences will be selected. This allows the user to receive highly empathetic support for their concerns. The selected AI counselor will listen to the user's concerns and provide specific advice and solutions. For example, it suggests various solutions such as talking to family and transferring schools, getting revenge, withdrawing from society, or cursing. It also provides solutions and actions taken by others based on data from past clients. For example, it gives specific examples such as "I talked to my family and transferred schools," "I got revenge," "I withdrew from society," and "I cursed." Furthermore, the AI ​​counselor provides highly empathetic responses that resonate with the client. For example, it conveys messages such as "Your parents will grieve until the day they die," "After graduation, I met my favorite idol, so I'm glad I didn't die back then," and "I cut ties with my bullies, got married, and am happy." This allows the client to have a positive outlook on their problems. This system allows the AI ​​counseling service to function effectively even in the current situation where there is a shortage of counselors to handle inquiries at counseling centers. In addition, anonymity is protected, so clients can consult with peace of mind. Moreover, since people can ask the AI ​​things that are difficult to ask or research with other people, it can handle a wide range of consultation topics.For example, it can handle consultations such as, "I want to escape from Japan," "How much money do I need to live in Africa?", "I only have this much savings right now, how much do people my age have saved?", and "I like people of the same sex, how do other people come out?". In this way, AI counseling services with diverse backgrounds can empathize with clients and provide highly empathetic support, helping them resolve their problems and give them a little more hope for the future. As a result, a wide variety of AI counseling systems can effectively resolve clients' problems and provide highly empathetic support.

[0029] The various AI counseling systems according to this embodiment include a reception unit, a selection unit, and a provision unit. The reception unit receives input from the user regarding their consultation. When the user inputs the consultation, they specifically enter their worries and the topics they wish to discuss. For example, possible consultation topics include "I'm being bullied at school," "I want to talk to my family and transfer schools," or "I want to get revenge." The reception unit may provide, for example, a text input interface, allowing the user to freely input their consultation. The reception unit may also provide a voice input interface, allowing the user to input their consultation by voice. For example, speech recognition technology may be used to convert the user's voice into text. Furthermore, the reception unit may also provide an image input interface, allowing the user to upload images to input their consultation. For example, the user can take a picture of a handwritten memo and upload it. The selection unit selects the most suitable AI counselor based on the consultation content entered by the reception unit. The selection unit may, for example, select the AI ​​counselor best suited to the consultation based on past consultation data and solutions from other users. For example, if the user is being bullied at school, an AI counselor with similar experience may be selected. The selection unit, for example, uses an AI model to match the consultation content with the background of an AI counselor. For instance, it uses natural language processing technology to extract keywords from the consultation content and selects the most suitable AI counselor based on them. Furthermore, the selection unit can also select the most suitable AI counselor by considering the user's attribute information (age, gender, region, etc.). For example, if the user is a Japanese high school student and identifies as LGB, an AI counselor with the same attributes will be selected. The provision unit has the AI ​​counselor selected by the selection unit listen to the consultation and provide specific advice and solutions. The provision unit suggests various solutions, such as how to talk to family and transfer schools, how to retaliate, how to withdraw from society, or how to curse. In addition, the provision unit can also tell users about solutions and actions taken by others based on data from past consultations. For example, specific examples include "I talked to my family and transferred schools," "I retaliated," "I withdrew from society," and "I cursed." Furthermore, the provision unit provides highly empathetic support that resonates with the consultation user.For example, it can convey messages such as, "Your parents will grieve until the day they die," "After graduation, I met my favorite idol, and I'm so glad I didn't die back then," or "I cut ties with my bullies, got married, and am now happy." This allows the diverse AI counseling systems, according to the embodiment, to effectively resolve the concerns of those seeking advice and provide highly empathetic support.

[0030] The reception desk allows users to input their consultation details. When users input their consultation details, they should specify their worries and what they want to discuss. For example, possible consultation topics include "I'm being bullied at school," "I want to talk to my family and transfer schools," or "I want to get revenge." The reception desk can provide, for example, a text input interface, allowing users to freely input their consultation details. The text input interface is designed to be easily accessible and intuitive for users. The reception desk can also provide a voice input interface, allowing users to input their consultation details by voice. For example, speech recognition technology can be used to convert the user's voice into text. The speech recognition technology uses the latest deep learning algorithms to achieve high-precision speech recognition and accurately transcribe the user's speech into text. Furthermore, the reception desk can provide an image input interface, allowing users to upload images to input their consultation details. For example, a user can take a picture of a handwritten memo and upload it. Image recognition technology can be used to convert the handwritten text into text and incorporate it as consultation details. In this way, the reception desk can allow users to input their consultation details in a variety of ways, improving user convenience. Furthermore, the reception department centrally manages the entered consultation details and stores them in a database. This allows for smoother subsequent processing and analysis.

[0031] The selection unit selects the most suitable AI counselor based on the consultation content entered by the reception unit. For example, the selection unit chooses the AI ​​counselor best suited to the consultation content based on past consultation data and solutions from other users. For example, if a user is being bullied at school, an AI counselor with similar experiences will be selected. The selection unit matches the consultation content with the background of the AI ​​counselor, for example, using an AI model. Specifically, it uses natural language processing technology to extract keywords from the consultation content and select the most suitable AI counselor based on them. The natural language processing technology uses the latest transformer models to perform highly accurate keyword extraction and accurately grasp the intent of the consultation content. Furthermore, the selection unit can also select the most suitable AI counselor by considering the user's attribute information (age, gender, region, etc.). For example, if the user is a Japanese high school student and is LGB, an AI counselor with the same attributes will be selected. The selection unit obtains the user's attribute information from a database and inputs it into the AI ​​model to achieve more personalized matching. As a result, the selection unit can select the most suitable AI counselor based on the user's consultation content and attribute information, and provide the user with the best possible support. Furthermore, the selection process ensures transparency and allows for explanations of the selection criteria to the user. This allows users to proceed with consultations with confidence.

[0032] The service provider assigns an AI counselor, selected by the selection department, to provide empathetic support, specific advice, and solutions. The service provider offers various solutions, such as talking to family and transferring schools, getting revenge, withdrawing from society, or cursing the bullies. Based on the user's consultation, the AI ​​counselor refers to past consultation data and solutions from other users to suggest the best solution. For example, if the user wants to talk to family and transfer schools, the service provider will suggest specific steps and precautions based on successful cases of users who have received similar consultations in the past. The service provider also provides information on solutions and actions taken by others based on data from past consultations. For example, specific examples include "I talked to family and transferred schools," "I got revenge," "I withdrew from society," and "I cursed the bullies." This allows users to find the best solution for themselves by referring to the experiences of others. Furthermore, the service provider provides highly empathetic responses that resonate with the consultation. For example, it conveys messages such as "Your parents will grieve until the day they die," "I met my favorite idol after graduation, so I'm glad I didn't die back then," and "I cut ties with the bullies, got married, and am happy now." The AI ​​counselor uses emotion analysis technology to understand the user's emotional state and generates appropriate empathetic messages. This allows the service provider to provide effective advice while being attentive to the user's feelings. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to always provide optimal support to users using the latest information and technology.

[0033] The service provider includes an empathy unit that utilizes past consultation data to provide highly empathetic responses. For example, the empathy unit analyzes past consultation data and learns how to express empathy for similar consultation content. For instance, it uses natural language processing technology to extract empathy expressions from past consultation data and uses them to provide highly empathetic responses. Furthermore, the empathy unit can use AI models to estimate the client's emotions and adjust its empathy expressions based on those emotions. For example, if the client is sad, it will show empathy with kind words. In addition, the empathy unit can optimize algorithms to provide the most appropriate empathy expressions for specific consultation content based on past consultation data. For example, it can select the most appropriate empathy expression for a specific emotion from past consultation data. This enables highly empathetic responses by utilizing past consultation data.

[0034] The service provider includes an anonymity section to protect anonymity. This section protects privacy by, for example, anonymizing user data. For instance, it uses data anonymization technology to prevent the identification of the user's personal information. The anonymity section can also provide an interface that allows users to consult anonymously. For example, it can provide a text input interface where users can anonymously enter their consultation details. Furthermore, the anonymity section can estimate the user's emotions and adjust the anonymity maintenance method based on those emotions. For example, if a user is feeling anxious, it can provide settings to enhance anonymity. This ensures that users can consult with peace of mind while maintaining anonymity.

[0035] The service provider includes a solution provider that offers specific solutions to specific consultation topics. For example, the solution provider proposes specific solutions based on the user's consultation topic. For example, it may suggest various solutions such as talking to family and changing schools, getting revenge, withdrawing from society, or cursing. The solution provider also provides information on solutions and actions taken by other users based on past consultation data. For example, it may provide specific examples such as "talking to family and changing schools," "getting revenge," "withdrawing from society," and "cursing." Furthermore, the solution provider can also use an AI model to select the most appropriate solution for the consultation topic. For example, it may use natural language processing technology to extract keywords from the consultation topic and propose the optimal solution based on them. In this way, by providing specific solutions, the service can effectively resolve the user's problems.

[0036] The service provider includes a support unit to address issues that are difficult to ask or research with others. This support unit provides an interface where users can consult about things they might find difficult to ask or research with others. For example, it provides a text input interface where users can anonymously enter their questions. Furthermore, the support unit can use AI models to suggest optimal solutions based on the user's questions. For example, it can use natural language processing technology to extract keywords from the questions and suggest the best solutions based on them. In addition, the support unit can provide information on solutions and actions taken by others based on past consultation data. Examples include questions like, "I want to escape Japan," "How much money do I need to live in Africa?", "I only have this much savings right now, how much do people my age have saved?", and "I like people of the same sex, how do other people come out?". This allows the service to address issues that are difficult to ask or research with others.

[0037] The service provider includes a message provider that delivers messages that resonate with the caller. This message provider delivers highly empathetic messages that resonate with the caller. For example, it might deliver messages such as, "Your parents will grieve until the day they die," "I met my idol after graduation, so I'm glad I didn't die back then," or "I cut ties with my bullies, got married, and am happy now." Furthermore, the message provider can use an AI model to estimate the caller's emotions and adjust the message's expression based on those emotions. For example, if the caller is sad, it will deliver a message using gentle words. In addition, the message provider can optimize its algorithm to deliver the most appropriate message for a specific emotion based on past message data. For example, it can select the most appropriate message for a specific emotion from past message data. This allows the service to deliver messages that resonate with the caller, helping them to feel more positive.

[0038] The service provider includes a versatility section to handle a wide range of consultation topics. The versatility section provides an interface that allows users to input diverse consultation topics, offering various input methods such as text, voice, and image input. Furthermore, the versatility section can use AI models to suggest optimal solutions based on the user's consultation content. For example, it can use natural language processing technology to extract keywords from the consultation and suggest the best solution based on those keywords. In addition, the versatility section can provide information on solutions and actions taken by other users based on past consultation data. Examples of this include handling a wide range of consultation topics such as "business consultations," "personal concerns," and "health-related consultations." This allows the service to address a variety of concerns by handling a broad range of consultation topics.

[0039] The reception desk assists users with inputting consultation details by referring to their past consultation history. For example, it can automatically display content the user has previously consulted, saving them the trouble of re-entering it. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict the content of consultations at specific time periods based on the user's past consultation history and provide input assistance accordingly. For example, it can suggest relevant input options based on content the user has consulted at a specific time period in the past. This makes input assistance smoother by referring to past consultation history.

[0040] The reception desk presents input suggestions based on the user's current situation and areas of interest when they enter their consultation details. For example, if the user enters their current situation, the reception desk will present suggestions for consultation topics related to that situation. The reception desk can also present suggestions for consultation topics related to the user's areas of interest. Furthermore, if the user enters a specific keyword, the reception desk can also present suggestions for consultation topics related to that keyword. For example, if the user enters "job change," the reception desk will present suggestions for consultation topics related to job changes. This makes the input process smoother by presenting input suggestions based on the user's current situation and areas of interest.

[0041] The reception desk considers the user's geographical location when they input their inquiry details and presents highly relevant input suggestions. For example, if the user is in a specific region, the reception desk will present inquiry suggestions related to that region. It can also present travel-related inquiries if the user is traveling. Furthermore, if the user is participating in a specific event, the reception desk can present inquiry suggestions related to that event. For example, if the user enters "traveling," it will present travel-related inquiries. This ensures that highly relevant input suggestions are presented by considering the user's geographical location.

[0042] The reception desk analyzes the user's social media activity when they enter their inquiry details and suggests relevant input options. For example, the reception desk can suggest relevant inquiry options based on what the user has shared on social media. It can also suggest relevant inquiry options based on the content of accounts the user follows on social media. Furthermore, it can suggest relevant inquiry options based on the content of groups the user participates in on social media. For example, if the user enters "content shared on social media," relevant inquiry options will be suggested. In this way, relevant input options are suggested by analyzing the user's social media activity.

[0043] The selection unit optimizes its selection algorithm by referring to past consultation data during the selection process. For example, the selection unit optimizes the algorithm for selecting the most suitable AI consultant based on past consultation data. Furthermore, the selection unit can select the most suitable AI consultant for a specific consultation topic from past consultation data. In addition, the selection unit can analyze past consultation data to improve the accuracy of its selection algorithm. For example, it optimizes the algorithm for selecting the most suitable AI consultant for a specific consultation topic based on past consultation data. Thus, the selection algorithm is optimized by referring to past consultation data.

[0044] The selection unit, when making a selection, chooses the most suitable AI counselor by considering the user's attribute information. For example, the selection unit can choose the most suitable AI counselor by considering the user's age and gender. It can also choose the most suitable AI counselor by considering the user's occupation and background. Furthermore, it can choose the most suitable AI counselor by considering the user's language and religion. For example, if the user enters "Japanese high school student, LGB," the system will select an AI counselor with the same attributes. In this way, the most suitable AI counselor is selected by considering the user's attribute information.

[0045] The selection function, when making a selection, takes into account the user's geographical location to choose the most suitable AI consultant. For example, if the user is in a specific region, the selection function will select an AI consultant related to that region. Furthermore, if the user is traveling, the selection function can select an AI consultant related to travel. Additionally, if the user is participating in a specific event, the selection function can select an AI consultant related to that event. For example, if the user enters "traveling," the system will select an AI consultant related to travel. This ensures that a highly relevant AI consultant is selected by considering the user's geographical location.

[0046] The selection unit analyzes the user's social media activity and selects a relevant AI consultant during the selection process. For example, the selection unit can select a relevant AI consultant based on the content the user has shared on social media. It can also select a relevant AI consultant based on the content of accounts the user follows on social media. Furthermore, it can select a relevant AI consultant based on the content of groups the user participates in on social media. For example, if the user enters "content shared on social media," it will select a relevant AI consultant. In this way, a relevant AI consultant is selected by analyzing the user's social media activity.

[0047] The service provider optimizes its service provision algorithm by referring to past consultation data at the time of provision. For example, the service provider optimizes the algorithm to provide the best advice and solutions based on past consultation data. The service provider can also provide the best advice and solutions for specific consultation topics based on past consultation data. Furthermore, the service provider can analyze past consultation data to improve the accuracy of its service provision algorithm. For example, it optimizes the algorithm to provide the best advice and solutions for specific consultation topics based on past consultation data. In this way, the service provision algorithm is optimized by referring to past consultation data.

[0048] The service provider will provide optimal advice and solutions by considering the user's attribute information at the time of delivery. For example, the service provider will provide optimal advice and solutions by considering the user's age and gender. Furthermore, the service provider can also provide optimal advice and solutions by considering the user's occupation and background. In addition, the service provider can also provide optimal advice and solutions by considering the user's language and religion. For example, if a user enters "Japanese high school student, LGB," the service will provide advice and solutions from an AI consultant with the same attributes. This ensures that optimal advice and solutions are provided by considering the user's attribute information.

[0049] The service provider takes the user's geographical location into consideration when providing advice and solutions. For example, if the user is in a specific region, the service provider will provide advice and solutions relevant to that region. Furthermore, if the user is traveling, the service provider can provide advice and solutions related to their trip. Additionally, if the user is participating in a specific event, the service provider can provide advice and solutions related to that event. For example, if the user enters "traveling," the service provider will provide travel-related advice and solutions. This ensures that highly relevant advice and solutions are provided by considering the user's geographical location.

[0050] The service provider analyzes the user's social media activity at the time of delivery and provides relevant advice and solutions. For example, the service provider provides relevant advice and solutions based on what the user has shared on social media. It can also provide relevant advice and solutions based on the content of accounts the user follows on social media. Furthermore, it can provide relevant advice and solutions based on the content of groups the user participates in on social media. For example, if the user enters "content shared on social media," relevant advice and solutions will be provided. In this way, relevant advice and solutions are provided by analyzing the user's social media activity.

[0051] The empathy unit optimizes the empathy algorithm by referring to past empathy data during empathy. For example, the empathy unit optimizes the algorithm to provide the most appropriate empathy expression based on past empathy data. The empathy unit can also select the most appropriate empathy expression for a specific emotion from past empathy data. Furthermore, the empathy unit can analyze past empathy data to improve the accuracy of the empathy algorithm. For example, it optimizes the algorithm to provide the most appropriate empathy expression for a specific emotion based on past empathy data. Thus, the empathy algorithm is optimized by referring to past empathy data.

[0052] The empathy function, when providing empathy, considers the user's attribute information to provide the most appropriate method of empathy. For example, it considers the user's age and gender to provide the most appropriate method. It can also consider the user's occupation and background to provide the most appropriate method. Furthermore, it can consider the user's language and religion to provide the most appropriate method of empathy. For example, if a user enters "Japanese high school student, LGB," it will provide an empathy method offered by an AI counselor with the same attributes. In this way, the most appropriate method of empathy is provided by considering the user's attribute information.

[0053] The anonymization component optimizes the anonymization algorithm by referencing past anonymized data when maintaining anonymity. For example, the anonymization component optimizes the algorithm to provide the optimal anonymity maintenance method based on past anonymized data. Furthermore, the anonymization component can select the optimal anonymity maintenance method for a specific situation from past anonymized data. In addition, the anonymization component can analyze past anonymized data to improve the accuracy of the anonymization algorithm. For example, it optimizes the algorithm to provide the optimal anonymity maintenance method for a specific situation based on past anonymized data. Thus, the anonymization algorithm is optimized by referencing past anonymized data.

[0054] The anonymization section provides the optimal anonymization method while maintaining anonymity, taking into account the user's attribute information. For example, the anonymization section provides the optimal anonymization method considering the user's age and gender. It can also provide the optimal anonymization method considering the user's occupation and background. Furthermore, it can provide the optimal anonymization method considering the user's language and religion. For example, if a user enters "Japanese high school student, LGB," the anonymization method provided by an AI counselor with the same attributes will be offered. In this way, the optimal anonymization method is provided by taking the user's attribute information into consideration.

[0055] The solution provisioning unit optimizes its solution algorithm by referring to past solution data when providing a solution. For example, the solution provisioning unit optimizes its algorithm to provide the optimal solution based on past solution data. Furthermore, the solution provisioning unit can provide the optimal solution for a specific consultation based on past solution data. In addition, the solution provisioning unit can analyze past solution data to improve the accuracy of its solution algorithm. For example, it optimizes its algorithm to provide the optimal solution for a specific consultation based on past solution data. Thus, the solution algorithm is optimized by referring to past solution data.

[0056] The solution provider unit provides the optimal solution by considering the user's attribute information when providing a solution. For example, the solution provider unit considers the user's age and gender to provide the optimal solution. It can also consider the user's occupation and background to provide the optimal solution. Furthermore, it can consider the user's language and religion to provide the optimal solution. For example, if a user enters "Japanese high school student, LGB," the solution provided will be from an AI consultant with the same attributes. In this way, the optimal solution is provided by considering the user's attribute information.

[0057] The response unit optimizes its response algorithm by referring to past response data during the response process. For example, the response unit optimizes an algorithm to provide the optimal response method based on past response data. Furthermore, the response unit can provide the optimal response method for a specific consultation based on past response data. In addition, the response unit can analyze past response data to improve the accuracy of its response algorithm. For example, it optimizes an algorithm to provide the optimal response method for a specific consultation based on past response data. Thus, the response algorithm is optimized by referring to past response data.

[0058] The support unit provides the optimal support method by considering the user's attribute information during support. For example, the support unit considers the user's age and gender to provide the optimal support method. It can also consider the user's occupation and background to provide the optimal support method. Furthermore, it can consider the user's language and religion to provide the optimal support method. For example, if a user enters "Japanese high school student, LGB," the support unit will provide a support method from an AI counselor with the same attributes. In this way, the optimal support method is provided by considering the user's attribute information.

[0059] The message delivery unit optimizes the message algorithm by referring to past message data when delivering a message. For example, the message delivery unit optimizes the algorithm to provide the optimal message based on past message data. The message delivery unit can also select the optimal message for a specific emotion from past message data. Furthermore, the message delivery unit can analyze past message data to improve the accuracy of the message algorithm. For example, it optimizes the algorithm to provide the optimal message for a specific emotion based on past message data. Thus, the message algorithm is optimized by referring to past message data.

[0060] The message provider takes user attribute information into consideration when providing a message to deliver the most suitable message. For example, it considers the user's age and gender to provide the most suitable message. It can also consider the user's occupation and background to provide the most suitable message. Furthermore, it can consider the user's language and religion to provide the most suitable message. For example, if a user enters "Japanese high school student, LGB," it will provide a message from an AI counselor with the same attributes. In this way, the most suitable message is delivered by considering the user's attribute information.

[0061] The diversity unit optimizes the diversity algorithm by referencing past diversity data when providing diversity. For example, the diversity unit optimizes the algorithm to provide the optimal diversity expression based on past diversity data. The diversity unit can also select the optimal diversity expression for a specific emotion from past diversity data. Furthermore, the diversity unit can analyze past diversity data to improve the accuracy of the diversity algorithm. For example, it optimizes the algorithm to provide the optimal diversity expression for a specific emotion based on past diversity data. In this way, the diversity algorithm is optimized by referring to past diversity data.

[0062] The diversity function provides the optimal diversity method when offering diversity, taking into account the user's attribute information. For example, the diversity function considers the user's age and gender to provide the optimal diversity method. It can also consider the user's occupation and background to provide the optimal diversity method. Furthermore, the diversity function can consider the user's language and religion to provide the optimal diversity method. For example, if a user enters "Japanese high school student, LGB," the system will provide a diversity method offered by an AI counselor with the same attributes. In this way, the optimal diversity method is provided by taking the user's attribute information into consideration.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception desk can provide input assistance by referring to the user's past consultation history. For example, it can automatically display the content of past consultations, saving the user the trouble of re-entering the information. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict the content of consultations at specific times based on the user's past consultation history and provide input assistance accordingly. In this way, input assistance becomes smoother by referring to past consultation history.

[0065] The selection unit can choose the most suitable AI consultant by considering the user's attribute information. For example, it can select the most suitable AI consultant by considering the user's age and gender. It can also select the most suitable AI consultant by considering the user's occupation and background. Furthermore, it can select the most suitable AI consultant by considering the user's language and religion. In this way, the most suitable AI consultant is selected by considering the user's attribute information.

[0066] The service provider can optimize its service provision algorithm by referring to past consultation data. For example, it can optimize the algorithm that provides the best advice and solutions based on past consultation data. It can also provide the best advice and solutions for specific consultation topics based on past consultation data. Furthermore, it can analyze past consultation data to improve the accuracy of the service provision algorithm. In this way, the service provision algorithm is optimized by referring to past consultation data.

[0067] The response unit can provide the optimal response method by considering the user's attribute information. For example, it can provide the optimal response method by considering the user's age and gender. It can also provide the optimal response method by considering the user's occupation and background. Furthermore, it can provide the optimal response method by considering the user's language and religion. In this way, the optimal response method is provided by considering the user's attribute information.

[0068] The message delivery unit can optimize its message algorithm by referring to past message data. For example, it can optimize the algorithm for providing the most suitable message based on past message data. It can also select the most suitable message for a specific emotion from past message data. Furthermore, it can analyze past message data to improve the accuracy of the message algorithm. In this way, the message algorithm is optimized by referring to past message data.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives the user's inquiry. Users can input their concerns and the details of their problems using a text input interface, voice input interface, or image input interface. For example, they can enter something like "I'm being bullied at school" using text input, convert their voice to text using speech recognition technology using voice input, or take a picture of a handwritten note and upload it using image input. Step 2: The selection unit selects the most suitable AI consultant based on the consultation content entered by the reception unit. The selection unit chooses the AI ​​consultant best suited to the consultation content based on past consultation data and solutions from other users. For example, it may use natural language processing technology to extract keywords from the consultation content and select the most suitable AI consultant based on that. It can also select the most suitable AI consultant by considering the user's attribute information (age, gender, region, etc.). Step 3: The Provisioning Department assigns an AI counselor selected by the Selection Department to the client, providing empathetic support, specific advice, and solutions. The Provisioning Department suggests various solutions, such as talking to family and changing schools, retaliating, withdrawing from society, or even cursing. It also provides solutions and actions based on data from past clients. Furthermore, it provides highly empathetic support that resonates with the client.

[0071] (Example of form 2) The diverse AI counseling system according to the embodiment of the present invention is not a single, impersonal AI, but a system that provides an AI counseling service with a wide variety of backgrounds. In this system, the user inputs the content of their consultation, and the AI ​​selects the most suitable AI counselor based on that content. The selected AI counselor will listen to the user's concerns and provide highly empathetic support. Examples include an AI of an African man living in Africa, an AI of a Japanese high school student who is LGB, an AI of a priest or monk, and an AI of a historical figure. When the user inputs the content of their consultation, they input their worries and the content they want to discuss in detail. For example, possible consultation topics include "I'm being bullied at school," "I want to talk to my family and transfer schools," or "I want to get revenge." This information is input into the AI. Next, the AI ​​analyzes the input content of the consultation and selects the most suitable AI counselor. The AI ​​selects the AI ​​counselor that is most suitable for the consultation content based on past consultation data and solutions from other users. For example, if the user is being bullied at school, an AI counselor with similar experiences will be selected. This allows the user to receive highly empathetic support for their concerns. The selected AI counselor will listen to the user's concerns and provide specific advice and solutions. For example, it suggests various solutions such as talking to family and transferring schools, getting revenge, withdrawing from society, or cursing. It also provides solutions and actions taken by others based on data from past clients. For example, it gives specific examples such as "I talked to my family and transferred schools," "I got revenge," "I withdrew from society," and "I cursed." Furthermore, the AI ​​counselor provides highly empathetic responses that resonate with the client. For example, it conveys messages such as "Your parents will grieve until the day they die," "After graduation, I met my favorite idol, so I'm glad I didn't die back then," and "I cut ties with my bullies, got married, and am happy." This allows the client to have a positive outlook on their problems. This system allows the AI ​​counseling service to function effectively even in the current situation where there is a shortage of counselors to handle inquiries at counseling centers. In addition, anonymity is protected, so clients can consult with peace of mind. Moreover, since people can ask the AI ​​things that are difficult to ask or research with other people, it can handle a wide range of consultation topics.For example, it can handle consultations such as, "I want to escape from Japan," "How much money do I need to live in Africa?", "I only have this much savings right now, how much do people my age have saved?", and "I like people of the same sex, how do other people come out?". In this way, AI counseling services with diverse backgrounds can empathize with clients and provide highly empathetic support, helping them resolve their problems and give them a little more hope for the future. As a result, a wide variety of AI counseling systems can effectively resolve clients' problems and provide highly empathetic support.

[0072] The various AI counseling systems according to this embodiment include a reception unit, a selection unit, and a provision unit. The reception unit receives input from the user regarding their consultation. When the user inputs the consultation, they specifically enter their worries and the topics they wish to discuss. For example, possible consultation topics include "I'm being bullied at school," "I want to talk to my family and transfer schools," or "I want to get revenge." The reception unit may provide, for example, a text input interface, allowing the user to freely input their consultation. The reception unit may also provide a voice input interface, allowing the user to input their consultation by voice. For example, speech recognition technology may be used to convert the user's voice into text. Furthermore, the reception unit may also provide an image input interface, allowing the user to upload images to input their consultation. For example, the user can take a picture of a handwritten memo and upload it. The selection unit selects the most suitable AI counselor based on the consultation content entered by the reception unit. The selection unit may, for example, select the AI ​​counselor best suited to the consultation based on past consultation data and solutions from other users. For example, if the user is being bullied at school, an AI counselor with similar experience may be selected. The selection unit, for example, uses an AI model to match the consultation content with the background of an AI counselor. For instance, it uses natural language processing technology to extract keywords from the consultation content and selects the most suitable AI counselor based on them. Furthermore, the selection unit can also select the most suitable AI counselor by considering the user's attribute information (age, gender, region, etc.). For example, if the user is a Japanese high school student and identifies as LGB, an AI counselor with the same attributes will be selected. The provision unit has the AI ​​counselor selected by the selection unit listen to the consultation and provide specific advice and solutions. The provision unit suggests various solutions, such as how to talk to family and transfer schools, how to retaliate, how to withdraw from society, or how to curse. In addition, the provision unit can also tell users about solutions and actions taken by others based on data from past consultations. For example, specific examples include "I talked to my family and transferred schools," "I retaliated," "I withdrew from society," and "I cursed." Furthermore, the provision unit provides highly empathetic support that resonates with the consultation user.For example, it can convey messages such as, "Your parents will grieve until the day they die," "After graduation, I met my favorite idol, and I'm so glad I didn't die back then," or "I cut ties with my bullies, got married, and am now happy." This allows the diverse AI counseling systems, according to the embodiment, to effectively resolve the concerns of those seeking advice and provide highly empathetic support.

[0073] The reception desk allows users to input their consultation details. When users input their consultation details, they should specify their worries and what they want to discuss. For example, possible consultation topics include "I'm being bullied at school," "I want to talk to my family and transfer schools," or "I want to get revenge." The reception desk can provide, for example, a text input interface, allowing users to freely input their consultation details. The text input interface is designed to be easily accessible and intuitive for users. The reception desk can also provide a voice input interface, allowing users to input their consultation details by voice. For example, speech recognition technology can be used to convert the user's voice into text. The speech recognition technology uses the latest deep learning algorithms to achieve high-precision speech recognition and accurately transcribe the user's speech into text. Furthermore, the reception desk can provide an image input interface, allowing users to upload images to input their consultation details. For example, a user can take a picture of a handwritten memo and upload it. Image recognition technology can be used to convert the handwritten text into text and incorporate it as consultation details. In this way, the reception desk can allow users to input their consultation details in a variety of ways, improving user convenience. Furthermore, the reception department centrally manages the entered consultation details and stores them in a database. This allows for smoother subsequent processing and analysis.

[0074] The selection unit selects the most suitable AI counselor based on the consultation content entered by the reception unit. For example, the selection unit chooses the AI ​​counselor best suited to the consultation content based on past consultation data and solutions from other users. For example, if a user is being bullied at school, an AI counselor with similar experiences will be selected. The selection unit matches the consultation content with the background of the AI ​​counselor, for example, using an AI model. Specifically, it uses natural language processing technology to extract keywords from the consultation content and select the most suitable AI counselor based on them. The natural language processing technology uses the latest transformer models to perform highly accurate keyword extraction and accurately grasp the intent of the consultation content. Furthermore, the selection unit can also select the most suitable AI counselor by considering the user's attribute information (age, gender, region, etc.). For example, if the user is a Japanese high school student and is LGB, an AI counselor with the same attributes will be selected. The selection unit obtains the user's attribute information from a database and inputs it into the AI ​​model to achieve more personalized matching. As a result, the selection unit can select the most suitable AI counselor based on the user's consultation content and attribute information, and provide the user with the best possible support. Furthermore, the selection process ensures transparency and allows for explanations of the selection criteria to the user. This allows users to proceed with consultations with confidence.

[0075] The service provider assigns an AI counselor, selected by the selection department, to provide empathetic support, specific advice, and solutions. The service provider offers various solutions, such as talking to family and transferring schools, getting revenge, withdrawing from society, or cursing the bullies. Based on the user's consultation, the AI ​​counselor refers to past consultation data and solutions from other users to suggest the best solution. For example, if the user wants to talk to family and transfer schools, the service provider will suggest specific steps and precautions based on successful cases of users who have received similar consultations in the past. The service provider also provides information on solutions and actions taken by others based on data from past consultations. For example, specific examples include "I talked to family and transferred schools," "I got revenge," "I withdrew from society," and "I cursed the bullies." This allows users to find the best solution for themselves by referring to the experiences of others. Furthermore, the service provider provides highly empathetic responses that resonate with the consultation. For example, it conveys messages such as "Your parents will grieve until the day they die," "I met my favorite idol after graduation, so I'm glad I didn't die back then," and "I cut ties with the bullies, got married, and am happy now." The AI ​​counselor uses emotion analysis technology to understand the user's emotional state and generates appropriate empathetic messages. This allows the service provider to provide effective advice while being attentive to the user's feelings. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to always provide optimal support to users using the latest information and technology.

[0076] The service provider includes an empathy unit that utilizes past consultation data to provide highly empathetic responses. For example, the empathy unit analyzes past consultation data and learns how to express empathy for similar consultation content. For instance, it uses natural language processing technology to extract empathy expressions from past consultation data and uses them to provide highly empathetic responses. Furthermore, the empathy unit can use AI models to estimate the client's emotions and adjust its empathy expressions based on those emotions. For example, if the client is sad, it will show empathy with kind words. In addition, the empathy unit can optimize algorithms to provide the most appropriate empathy expressions for specific consultation content based on past consultation data. For example, it can select the most appropriate empathy expression for a specific emotion from past consultation data. This enables highly empathetic responses by utilizing past consultation data.

[0077] The service provider includes an anonymity section to protect anonymity. This section protects privacy by, for example, anonymizing user data. For instance, it uses data anonymization technology to prevent the identification of the user's personal information. The anonymity section can also provide an interface that allows users to consult anonymously. For example, it can provide a text input interface where users can anonymously enter their consultation details. Furthermore, the anonymity section can estimate the user's emotions and adjust the anonymity maintenance method based on those emotions. For example, if a user is feeling anxious, it can provide settings to enhance anonymity. This ensures that users can consult with peace of mind while maintaining anonymity.

[0078] The service provider includes a solution provider that offers specific solutions to specific consultation topics. For example, the solution provider proposes specific solutions based on the user's consultation topic. For example, it may suggest various solutions such as talking to family and changing schools, getting revenge, withdrawing from society, or cursing. The solution provider also provides information on solutions and actions taken by other users based on past consultation data. For example, it may provide specific examples such as "talking to family and changing schools," "getting revenge," "withdrawing from society," and "cursing." Furthermore, the solution provider can also use an AI model to select the most appropriate solution for the consultation topic. For example, it may use natural language processing technology to extract keywords from the consultation topic and propose the optimal solution based on them. In this way, by providing specific solutions, the service can effectively resolve the user's problems.

[0079] The service provider includes a support unit to address issues that are difficult to ask or research with others. This support unit provides an interface where users can consult about things they might find difficult to ask or research with others. For example, it provides a text input interface where users can anonymously enter their questions. Furthermore, the support unit can use AI models to suggest optimal solutions based on the user's questions. For example, it can use natural language processing technology to extract keywords from the questions and suggest the best solutions based on them. In addition, the support unit can provide information on solutions and actions taken by others based on past consultation data. Examples include questions like, "I want to escape Japan," "How much money do I need to live in Africa?", "I only have this much savings right now, how much do people my age have saved?", and "I like people of the same sex, how do other people come out?". This allows the service to address issues that are difficult to ask or research with others.

[0080] The service provider includes a message provider that delivers messages that resonate with the caller. This message provider delivers highly empathetic messages that resonate with the caller. For example, it might deliver messages such as, "Your parents will grieve until the day they die," "I met my idol after graduation, so I'm glad I didn't die back then," or "I cut ties with my bullies, got married, and am happy now." Furthermore, the message provider can use an AI model to estimate the caller's emotions and adjust the message's expression based on those emotions. For example, if the caller is sad, it will deliver a message using gentle words. In addition, the message provider can optimize its algorithm to deliver the most appropriate message for a specific emotion based on past message data. For example, it can select the most appropriate message for a specific emotion from past message data. This allows the service to deliver messages that resonate with the caller, helping them to feel more positive.

[0081] The service provider includes a versatility section to handle a wide range of consultation topics. The versatility section provides an interface that allows users to input diverse consultation topics, offering various input methods such as text, voice, and image input. Furthermore, the versatility section can use AI models to suggest optimal solutions based on the user's consultation content. For example, it can use natural language processing technology to extract keywords from the consultation and suggest the best solution based on those keywords. In addition, the versatility section can provide information on solutions and actions taken by other users based on past consultation data. Examples of this include handling a wide range of consultation topics such as "business consultations," "personal concerns," and "health-related consultations." This allows the service to address a variety of concerns by handling a broad range of consultation topics.

[0082] The reception desk estimates the user's emotions and adjusts the input interface for the consultation content based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the consultation content. For example, speech recognition technology is used to convert the user's voice into text. This makes inputting the consultation content smoother by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception desk assists users with inputting consultation details by referring to their past consultation history. For example, it can automatically display content the user has previously consulted, saving them the trouble of re-entering it. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict the content of consultations at specific time periods based on the user's past consultation history and provide input assistance accordingly. For example, it can suggest relevant input options based on content the user has consulted at a specific time period in the past. This makes input assistance smoother by referring to past consultation history.

[0084] The reception desk presents input suggestions based on the user's current situation and areas of interest when they enter their consultation details. For example, if the user enters their current situation, the reception desk will present suggestions for consultation topics related to that situation. The reception desk can also present suggestions for consultation topics related to the user's areas of interest. Furthermore, if the user enters a specific keyword, the reception desk can also present suggestions for consultation topics related to that keyword. For example, if the user enters "job change," the reception desk will present suggestions for consultation topics related to job changes. This makes the input process smoother by presenting input suggestions based on the user's current situation and areas of interest.

[0085] The reception desk estimates the user's emotions and prioritizes input based on those emotions. For example, if the user has an urgent inquiry, the reception desk will prioritize that information. It can also prioritize detailed input if the user is relaxed. Furthermore, if the user is stressed, the reception desk can prioritize simple input. For example, if the user enters "I'm in a hurry," the reception desk will prioritize simple input. This ensures that important information is processed first by prioritizing input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The reception desk considers the user's geographical location when they input their inquiry details and presents highly relevant input suggestions. For example, if the user is in a specific region, the reception desk will present inquiry suggestions related to that region. It can also present travel-related inquiries if the user is traveling. Furthermore, if the user is participating in a specific event, the reception desk can present inquiry suggestions related to that event. For example, if the user enters "traveling," it will present travel-related inquiries. This ensures that highly relevant input suggestions are presented by considering the user's geographical location.

[0087] The reception desk analyzes the user's social media activity when they enter their inquiry details and suggests relevant input options. For example, the reception desk can suggest relevant inquiry options based on what the user has shared on social media. It can also suggest relevant inquiry options based on the content of accounts the user follows on social media. Furthermore, it can suggest relevant inquiry options based on the content of groups the user participates in on social media. For example, if the user enters "content shared on social media," relevant inquiry options will be suggested. In this way, relevant input options are suggested by analyzing the user's social media activity.

[0088] The selection unit estimates the user's emotions and adjusts the selection criteria for the most suitable AI counselor based on the estimated emotions. For example, if the user is stressed, the selection unit will prioritize selecting an AI counselor with high empathy. If the user is relaxed, the selection unit can also prioritize selecting an AI counselor with specialized knowledge. Furthermore, if the user is in a hurry, the selection unit can also prioritize selecting an AI counselor that can respond quickly. For example, if the user enters "I'm in a hurry," the selection unit will prioritize selecting an AI counselor that can respond quickly. In this way, the most suitable AI counselor is selected by adjusting the selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The selection unit optimizes its selection algorithm by referring to past consultation data during the selection process. For example, the selection unit optimizes the algorithm for selecting the most suitable AI consultant based on past consultation data. Furthermore, the selection unit can select the most suitable AI consultant for a specific consultation topic from past consultation data. In addition, the selection unit can analyze past consultation data to improve the accuracy of its selection algorithm. For example, it optimizes the algorithm for selecting the most suitable AI consultant for a specific consultation topic based on past consultation data. Thus, the selection algorithm is optimized by referring to past consultation data.

[0090] The selection unit, when making a selection, chooses the most suitable AI counselor by considering the user's attribute information. For example, the selection unit can choose the most suitable AI counselor by considering the user's age and gender. It can also choose the most suitable AI counselor by considering the user's occupation and background. Furthermore, it can choose the most suitable AI counselor by considering the user's language and religion. For example, if the user enters "Japanese high school student, LGB," the system will select an AI counselor with the same attributes. In this way, the most suitable AI counselor is selected by considering the user's attribute information.

[0091] The selection section estimates the user's emotions and adjusts the display method of the selection results based on the estimated emotions. For example, if the user is nervous, the selection section provides a simple and highly visible display method. If the user is relaxed, the selection section can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the selection section can provide a concise display method. For example, if the user enters "hurry," a concise display method will be provided. This allows for a highly visible display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The selection function, when making a selection, takes into account the user's geographical location to choose the most suitable AI consultant. For example, if the user is in a specific region, the selection function will select an AI consultant related to that region. Furthermore, if the user is traveling, the selection function can select an AI consultant related to travel. Additionally, if the user is participating in a specific event, the selection function can select an AI consultant related to that event. For example, if the user enters "traveling," the system will select an AI consultant related to travel. This ensures that a highly relevant AI consultant is selected by considering the user's geographical location.

[0093] The selection unit analyzes the user's social media activity and selects a relevant AI consultant during the selection process. For example, the selection unit can select a relevant AI consultant based on the content the user has shared on social media. It can also select a relevant AI consultant based on the content of accounts the user follows on social media. Furthermore, it can select a relevant AI consultant based on the content of groups the user participates in on social media. For example, if the user enters "content shared on social media," it will select a relevant AI consultant. In this way, a relevant AI consultant is selected by analyzing the user's social media activity.

[0094] The service provider estimates the user's emotions and adjusts the way advice and solutions are delivered based on the estimated emotions. For example, if the user is stressed, the service provider will offer advice in gentle language. If the user is relaxed, the service provider can also offer detailed advice. Furthermore, if the user is in a hurry, the service provider can provide solutions quickly. For example, if the user types "hurry," the service provider will provide a solution quickly. This allows for more appropriate advice and solutions to be provided by adjusting the delivery method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The service provider optimizes its service provision algorithm by referring to past consultation data at the time of provision. For example, the service provider optimizes the algorithm to provide the best advice and solutions based on past consultation data. The service provider can also provide the best advice and solutions for specific consultation topics based on past consultation data. Furthermore, the service provider can analyze past consultation data to improve the accuracy of its service provision algorithm. For example, it optimizes the algorithm to provide the best advice and solutions for specific consultation topics based on past consultation data. In this way, the service provision algorithm is optimized by referring to past consultation data.

[0096] The service provider will provide optimal advice and solutions by considering the user's attribute information at the time of delivery. For example, the service provider will provide optimal advice and solutions by considering the user's age and gender. Furthermore, the service provider can also provide optimal advice and solutions by considering the user's occupation and background. In addition, the service provider can also provide optimal advice and solutions by considering the user's language and religion. For example, if a user enters "Japanese high school student, LGB," the service will provide advice and solutions from an AI consultant with the same attributes. This ensures that optimal advice and solutions are provided by considering the user's attribute information.

[0097] The service provider estimates the user's emotions and prioritizes the content of its offerings based on those emotions. For example, if the user is seeking urgent advice, the service provider will prioritize that content. It can also prioritize detailed advice if the user is relaxed. Furthermore, if the user is stressed, the service provider can prioritize simple advice. For example, if the user enters "I'm in a hurry," simple advice will be prioritized. This ensures that important content is processed first by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The service provider takes the user's geographical location into consideration when providing advice and solutions. For example, if the user is in a specific region, the service provider will provide advice and solutions relevant to that region. Furthermore, if the user is traveling, the service provider can provide advice and solutions related to their trip. Additionally, if the user is participating in a specific event, the service provider can provide advice and solutions related to that event. For example, if the user enters "traveling," the service provider will provide travel-related advice and solutions. This ensures that highly relevant advice and solutions are provided by considering the user's geographical location.

[0099] The service provider analyzes the user's social media activity at the time of delivery and provides relevant advice and solutions. For example, the service provider provides relevant advice and solutions based on what the user has shared on social media. It can also provide relevant advice and solutions based on the content of accounts the user follows on social media. Furthermore, it can provide relevant advice and solutions based on the content of groups the user participates in on social media. For example, if the user enters "content shared on social media," relevant advice and solutions will be provided. In this way, relevant advice and solutions are provided by analyzing the user's social media activity.

[0100] The empathy unit estimates the user's emotions and adjusts its empathy expression based on the estimated emotions. For example, if the user is sad, the empathy unit will show empathy with gentle words. If the user is angry, the empathy unit can also use calm and understanding words. Furthermore, if the user is feeling anxious, the empathy unit can show empathy with reassuring words. For example, if the user inputs "sad," the empathy unit will show empathy with gentle words. This allows for more appropriate empathy to be expressed by adjusting the empathy expression based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The empathy unit optimizes the empathy algorithm by referring to past empathy data during empathy. For example, the empathy unit optimizes the algorithm to provide the most appropriate empathy expression based on past empathy data. The empathy unit can also select the most appropriate empathy expression for a specific emotion from past empathy data. Furthermore, the empathy unit can analyze past empathy data to improve the accuracy of the empathy algorithm. For example, it optimizes the algorithm to provide the most appropriate empathy expression for a specific emotion based on past empathy data. Thus, the empathy algorithm is optimized by referring to past empathy data.

[0102] The empathy unit estimates the user's emotions and determines the priority of empathy based on the estimated emotions. For example, if the user is seeking urgent advice, the empathy unit will prioritize showing empathy for that emotion. The empathy unit can also prioritize providing detailed empathy expressions if the user is relaxed. Furthermore, if the user is stressed, the empathy unit can prioritize providing simple empathy expressions. For example, if the user enters "I'm in a hurry," a simple empathy expression will be prioritized. This ensures that important emotions are prioritized through empathy prioritization based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The empathy function, when providing empathy, considers the user's attribute information to provide the most appropriate method of empathy. For example, it considers the user's age and gender to provide the most appropriate method. It can also consider the user's occupation and background to provide the most appropriate method. Furthermore, it can consider the user's language and religion to provide the most appropriate method of empathy. For example, if a user enters "Japanese high school student, LGB," it will provide an empathy method offered by an AI counselor with the same attributes. In this way, the most appropriate method of empathy is provided by considering the user's attribute information.

[0104] The anonymity section estimates the user's emotions and adjusts the anonymity maintenance method based on the estimated emotions. For example, if the user is feeling anxious, the anonymity section provides settings to enhance anonymity. It can also provide detailed information while maintaining anonymity if the user is relaxed. Furthermore, if the user is in a hurry, the anonymity section can provide an interface to easily adjust anonymity. For example, if the user types "hurry," an interface to easily adjust anonymity is provided. This allows for more appropriate anonymity by adjusting the anonymity maintenance method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The anonymization component optimizes the anonymization algorithm by referencing past anonymized data when maintaining anonymity. For example, the anonymization component optimizes the algorithm to provide the optimal anonymity maintenance method based on past anonymized data. Furthermore, the anonymization component can select the optimal anonymity maintenance method for a specific situation from past anonymized data. In addition, the anonymization component can analyze past anonymized data to improve the accuracy of the anonymization algorithm. For example, it optimizes the algorithm to provide the optimal anonymity maintenance method for a specific situation based on past anonymized data. Thus, the anonymization algorithm is optimized by referencing past anonymized data.

[0106] The anonymity section estimates the user's emotions and determines the priority of anonymity based on the estimated emotions. For example, if the user is feeling anxious, the anonymity section will set anonymity as the highest priority. Conversely, if the user is relaxed, the anonymity section may prioritize providing detailed information. Furthermore, if the user is in a hurry, the anonymity section can provide an interface to easily set anonymity. For example, if the user types "hurry," an interface to easily set anonymity will be provided. This ensures that important anonymity is prioritized by determining the priority of anonymity based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The anonymization section provides the optimal anonymization method while maintaining anonymity, taking into account the user's attribute information. For example, the anonymization section provides the optimal anonymization method considering the user's age and gender. It can also provide the optimal anonymization method considering the user's occupation and background. Furthermore, it can provide the optimal anonymization method considering the user's language and religion. For example, if a user enters "Japanese high school student, LGB," the anonymization method provided by an AI counselor with the same attributes will be offered. In this way, the optimal anonymization method is provided by taking the user's attribute information into consideration.

[0108] The solution provider estimates the user's emotions and adjusts the method of providing solutions based on the estimated emotions. For example, if the user is stressed, the solution provider will provide solutions in gentle language. If the user is relaxed, the solution provider can also provide detailed solutions. Furthermore, if the user is in a hurry, the solution provider can provide solutions quickly. For example, if the user enters "hurry," the solution will be provided quickly. This ensures that more appropriate solutions are provided by adjusting the method of delivery based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The solution provisioning unit optimizes its solution algorithm by referring to past solution data when providing a solution. For example, the solution provisioning unit optimizes its algorithm to provide the optimal solution based on past solution data. Furthermore, the solution provisioning unit can provide the optimal solution for a specific consultation based on past solution data. In addition, the solution provisioning unit can analyze past solution data to improve the accuracy of its solution algorithm. For example, it optimizes its algorithm to provide the optimal solution for a specific consultation based on past solution data. Thus, the solution algorithm is optimized by referring to past solution data.

[0110] The solution provider estimates the user's emotions and prioritizes solutions based on those emotions. For example, if the user has an urgent request, the solution provider will prioritize that request. If the user is relaxed, the solution provider can also prioritize providing detailed solutions. Furthermore, if the user is stressed, the solution provider can prioritize providing simpler solutions. For example, if the user enters "I'm in a hurry," a simpler solution will be prioritized. This ensures that important solutions are prioritized by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The solution provider unit provides the optimal solution by considering the user's attribute information when providing a solution. For example, the solution provider unit considers the user's age and gender to provide the optimal solution. It can also consider the user's occupation and background to provide the optimal solution. Furthermore, it can consider the user's language and religion to provide the optimal solution. For example, if a user enters "Japanese high school student, LGB," the solution provided will be from an AI consultant with the same attributes. In this way, the optimal solution is provided by considering the user's attribute information.

[0112] The response unit estimates the user's emotions and adjusts its response based on those emotions. For example, if the user is stressed, the response unit will respond using gentle language. If the user is relaxed, the response unit can also provide more detailed responses. Furthermore, if the user is in a hurry, the response unit can respond quickly. For example, if the user types "hurry," it will respond quickly. This allows for more appropriate responses by adjusting the response based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The response unit optimizes its response algorithm by referring to past response data during the response process. For example, the response unit optimizes an algorithm to provide the optimal response method based on past response data. Furthermore, the response unit can provide the optimal response method for a specific consultation based on past response data. In addition, the response unit can analyze past response data to improve the accuracy of its response algorithm. For example, it optimizes an algorithm to provide the optimal response method for a specific consultation based on past response data. Thus, the response algorithm is optimized by referring to past response data.

[0114] The response unit estimates the user's emotions and determines the priority of responses based on the estimated emotions. For example, if the user has an urgent request, the response unit will prioritize processing that request. Furthermore, if the user is relaxed, the response unit can prioritize providing detailed solutions. Additionally, if the user is stressed, the response unit can prioritize providing simple solutions. For example, if the user enters "I'm in a hurry," a simple solution will be prioritized. This ensures that important responses are prioritized by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The support unit provides the optimal support method by considering the user's attribute information during support. For example, the support unit considers the user's age and gender to provide the optimal support method. It can also consider the user's occupation and background to provide the optimal support method. Furthermore, it can consider the user's language and religion to provide the optimal support method. For example, if a user enters "Japanese high school student, LGB," the support unit will provide a support method from an AI counselor with the same attributes. In this way, the optimal support method is provided by considering the user's attribute information.

[0116] The message provider estimates the user's emotions and adjusts the message's wording based on the estimated emotions. For example, if the user is sad, the message provider will deliver a message using gentle words. If the user is angry, the message provider can also use calm and understanding language. Furthermore, if the user is feeling anxious, the message provider can deliver a message using reassuring words. For example, if the user enters "sad," the message provider will deliver a message using gentle words. This allows for the delivery of more appropriate messages by adjusting the message's wording based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The message delivery unit optimizes the message algorithm by referring to past message data when delivering a message. For example, the message delivery unit optimizes the algorithm to provide the optimal message based on past message data. The message delivery unit can also select the optimal message for a specific emotion from past message data. Furthermore, the message delivery unit can analyze past message data to improve the accuracy of the message algorithm. For example, it optimizes the algorithm to provide the optimal message for a specific emotion based on past message data. Thus, the message algorithm is optimized by referring to past message data.

[0118] The message delivery unit estimates the user's emotions and prioritizes messages based on those emotions. For example, if a user is seeking urgent advice, the message delivery unit will prioritize messages that match that emotion. It can also prioritize detailed messages if the user is relaxed. Furthermore, if the user is stressed, the message delivery unit can prioritize simple messages. For example, if the user types "I'm in a hurry," a simple message will be prioritized. This ensures that important messages are delivered preferentially by prioritizing based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The message provider takes user attribute information into consideration when providing a message to deliver the most suitable message. For example, it considers the user's age and gender to provide the most suitable message. It can also consider the user's occupation and background to provide the most suitable message. Furthermore, it can consider the user's language and religion to provide the most suitable message. For example, if a user enters "Japanese high school student, LGB," it will provide a message from an AI counselor with the same attributes. In this way, the most suitable message is delivered by considering the user's attribute information.

[0120] The diversity unit estimates the user's emotions and adjusts how diversity is expressed based on the estimated emotions. For example, if the user is sad, the diversity unit will express diversity using gentle words. If the user is angry, the diversity unit can also use calm and understanding words. Furthermore, if the user is feeling anxious, the diversity unit can express diversity using reassuring words. For example, if the user inputs "sad," the diversity will be expressed using gentle words. This allows for more appropriate diversity to be provided by adjusting how diversity is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The diversity unit optimizes the diversity algorithm by referencing past diversity data when providing diversity. For example, the diversity unit optimizes the algorithm to provide the optimal diversity expression based on past diversity data. The diversity unit can also select the optimal diversity expression for a specific emotion from past diversity data. Furthermore, the diversity unit can analyze past diversity data to improve the accuracy of the diversity algorithm. For example, it optimizes the algorithm to provide the optimal diversity expression for a specific emotion based on past diversity data. In this way, the diversity algorithm is optimized by referring to past diversity data.

[0122] The diversity unit estimates the user's emotions and determines the priority of diversity based on the estimated emotions. For example, if the user is seeking urgent advice, the diversity unit will prioritize expressing diversity in line with that emotion. It can also prioritize detailed diversity expressions if the user is relaxed. Furthermore, if the user is stressed, the diversity unit can prioritize simple diversity expressions. For example, if the user enters "I'm in a hurry," it will prioritize simple diversity expressions. This ensures that important diversity is prioritized by determining priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The diversity function provides the optimal diversity method when offering diversity, taking into account the user's attribute information. For example, the diversity function considers the user's age and gender to provide the optimal diversity method. It can also consider the user's occupation and background to provide the optimal diversity method. Furthermore, the diversity function can consider the user's language and religion to provide the optimal diversity method. For example, if a user enters "Japanese high school student, LGB," the system will provide a diversity method offered by an AI counselor with the same attributes. In this way, the optimal diversity method is provided by taking the user's attribute information into consideration.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The reception desk can estimate the user's emotions and adjust the input interface based on those estimates. For example, if the user is stressed, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of the consultation details. In this way, providing an interface that responds to the user's emotions makes the input of consultation details smoother.

[0126] The selection unit can estimate the user's emotions and adjust the selection criteria for the most suitable AI counselor based on those estimated emotions. For example, if the user is stressed, it can prioritize selecting an AI counselor with high empathy. If the user is relaxed, it can prioritize selecting an AI counselor with specialized knowledge. Furthermore, if the user is in a hurry, it can prioritize selecting an AI counselor that can respond quickly. In this way, the most suitable AI counselor is selected by adjusting the selection criteria based on the user's emotions.

[0127] The service provider can estimate the user's emotions and adjust the way advice and solutions are delivered based on those estimates. For example, if the user is stressed, it can offer advice in gentle language. If the user is relaxed, it can offer more detailed advice. Furthermore, if the user is in a hurry, it can provide solutions quickly. By adjusting the delivery method based on the user's emotions, it can provide more appropriate advice and solutions.

[0128] The empathy function can estimate the user's emotions and adjust how it expresses empathy based on those emotions. For example, if the user is sad, it can show empathy with kind words. If the user is angry, it can use calm and understanding words. Furthermore, if the user is feeling anxious, it can show empathy with reassuring words. In this way, by adjusting how empathy is expressed based on the user's emotions, more appropriate empathy can be provided.

[0129] The anonymity section can estimate the user's emotions and adjust how anonymity is maintained based on those emotions. For example, if the user is feeling anxious, it can provide settings to enhance anonymity. Conversely, if the user is relaxed, it can provide detailed information while maintaining anonymity. Furthermore, if the user is in a hurry, it can provide an interface that allows them to easily adjust anonymity settings. This allows for more appropriate anonymity by adjusting how anonymity is maintained based on the user's emotions.

[0130] The reception desk can provide input assistance by referring to the user's past consultation history. For example, it can automatically display the content of past consultations, saving the user the trouble of re-entering the information. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict the content of consultations at specific times based on the user's past consultation history and provide input assistance accordingly. In this way, input assistance becomes smoother by referring to past consultation history.

[0131] The selection unit can choose the most suitable AI consultant by considering the user's attribute information. For example, it can select the most suitable AI consultant by considering the user's age and gender. It can also select the most suitable AI consultant by considering the user's occupation and background. Furthermore, it can select the most suitable AI consultant by considering the user's language and religion. In this way, the most suitable AI consultant is selected by considering the user's attribute information.

[0132] The service provider can optimize its service provision algorithm by referring to past consultation data. For example, it can optimize the algorithm that provides the best advice and solutions based on past consultation data. It can also provide the best advice and solutions for specific consultation topics based on past consultation data. Furthermore, it can analyze past consultation data to improve the accuracy of the service provision algorithm. In this way, the service provision algorithm is optimized by referring to past consultation data.

[0133] The response unit can provide the optimal response method by considering the user's attribute information. For example, it can provide the optimal response method by considering the user's age and gender. It can also provide the optimal response method by considering the user's occupation and background. Furthermore, it can provide the optimal response method by considering the user's language and religion. In this way, the optimal response method is provided by considering the user's attribute information.

[0134] The message delivery unit can optimize its message algorithm by referring to past message data. For example, it can optimize the algorithm for providing the most suitable message based on past message data. It can also select the most suitable message for a specific emotion from past message data. Furthermore, it can analyze past message data to improve the accuracy of the message algorithm. In this way, the message algorithm is optimized by referring to past message data.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk receives the user's inquiry. Users can input their concerns and the details of their problems using a text input interface, voice input interface, or image input interface. For example, they can enter something like "I'm being bullied at school" using text input, convert their voice to text using speech recognition technology using voice input, or take a picture of a handwritten note and upload it using image input. Step 2: The selection unit selects the most suitable AI consultant based on the consultation content entered by the reception unit. The selection unit chooses the AI ​​consultant best suited to the consultation content based on past consultation data and solutions from other users. For example, it may use natural language processing technology to extract keywords from the consultation content and select the most suitable AI consultant based on that. It can also select the most suitable AI consultant by considering the user's attribute information (age, gender, region, etc.). Step 3: The Provisioning Department assigns an AI counselor selected by the Selection Department to the client, providing empathetic support, specific advice, and solutions. The Provisioning Department suggests various solutions, such as talking to family and changing schools, retaliating, withdrawing from society, or even cursing. It also provides solutions and actions based on data from past clients. Furthermore, it provides highly empathetic support that resonates with the client.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, selection unit, and provision 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 reception device 38 of the smart device 14 and provides an interface for the user to input the consultation details. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the most suitable AI consultant based on the input consultation details. The provision unit is implemented by the control unit 46A of the smart device 14 and the selected AI consultant provides specific advice and solutions. 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.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, selection unit, and provision 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 microphone 238 of the smart glasses 214 and provides an interface for the user to input consultation details by voice. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the most suitable AI consultant based on the input consultation details. The provision unit is implemented by the control unit 46A of the smart glasses 214 and the selected AI consultant provides specific advice and solutions. 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.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, selection unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input consultation details by voice. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects the most suitable AI consultant based on the input consultation details. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and the selected AI consultant provides specific advice and solutions. 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.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the reception unit, selection unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input consultation details by voice. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects the most suitable AI consultant based on the input consultation details. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and the selected AI consultant provides specific advice and solutions. 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The reception area where you enter the details of your inquiry, A selection unit that selects the most suitable AI consultant based on the consultation content entered by the reception unit, The system comprises a provisioning unit in which an AI counselor selected by the aforementioned selection unit provides support to the consultation and offers specific advice and solutions. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We have an empathy department that utilizes past consultation data to provide highly empathetic support. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Equipped with an anonymity section to protect anonymity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We have a solutions department that provides specific solutions to specific consultation issues. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It is equipped with a section for handling things that are difficult to ask others about or research. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Equipped with a message delivery department that delivers messages that resonate with the hearts of those seeking advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Equipped with a diversity department to handle a wide range of consultation topics. 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 input interface for consultation content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their consultation details, the system provides input assistance by referring to their past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter their consultation details, the system will suggest input options based on their current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content 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 users enter their inquiry details, the system will consider their geographical location to suggest highly relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users enter their inquiry details, the system analyzes their social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is It estimates the user's emotions and adjusts the selection criteria for the optimal AI consultant based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is When making a selection, the selection algorithm is optimized by referring to past consultation data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is When selecting a user, the system will choose the most suitable AI consultant by considering the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is It estimates the user's emotions and adjusts how the selection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When selecting an AI consultant, the system will consider the user's geographical location to choose the most suitable AI consultant. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is During the selection process, the system analyzes the user's social media activity and selects a relevant AI consultant. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how advice and solutions are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the service algorithm is optimized by referring to past consultation data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing services, we take into account the user's attribute information to offer the most suitable advice and solutions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing services, we take the user's geographical location into consideration to offer the most appropriate advice and solutions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and offer relevant advice and solutions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned empathy section is, It estimates the user's emotions and adjusts how empathy is expressed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned empathy section is, When empathizing, the empathy algorithm is optimized by referring to past empathy data. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned empathy section is, It estimates the user's emotions and determines empathy priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned empathy section is, When empathizing, we provide the optimal empathy method by considering the user's attribute information. The system described in Appendix 2, characterized by the features described herein. (Note 30) The anonymized portion is, We estimate the user's sentiment and adjust the method of maintaining anonymity based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 31) The anonymized portion is, When maintaining anonymity, the anonymity algorithm is optimized by referring to past anonymized data. The system described in Appendix 3, characterized by the features described herein. (Note 32) The anonymized portion is, The system estimates the user's emotions and determines the priority of anonymity based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The anonymized portion is, When maintaining anonymity, the system provides the optimal anonymization method, taking into account the user's attribute information. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned solution provider unit, It estimates the user's emotions and adjusts how solutions are delivered based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned solution provider unit, When providing a solution, we optimize the solution algorithm by referring to past solution data. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned solution provider unit, It estimates the user's emotions and determines the priority of solutions based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned solution provider unit, When providing solutions, we consider the user's attribute information to provide the most suitable solution. The system described in Appendix 4, characterized by the features described herein. (Note 38) The corresponding part is, It estimates the user's emotions and adjusts its response based on those emotions. The system described in Appendix 5, characterized by the features described herein. (Note 39) The corresponding part is, When responding, the response algorithm is optimized by referring to past response data. The system described in Appendix 5, characterized by the features described herein. (Note 40) The corresponding part is, It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 41) The corresponding part is, When responding, we provide the optimal response method by considering the user's attribute information. The system described in Appendix 5, characterized by the features described herein. (Note 42) The message provider unit, It estimates the user's emotions and adjusts the way messages are expressed based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 43) The message provider unit, When providing a message, the message algorithm is optimized by referring to past message data. The system described in Appendix 6, characterized by the features described herein. (Note 44) The message provider unit, It estimates the user's emotions and prioritizes messages based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 45) The message provider unit, When delivering a message, we consider the user's attribute information to provide the most suitable message. The system described in Appendix 6, characterized by the features described herein. (Note 46) The aforementioned diversity section is It estimates the user's emotions and adjusts the way diversity is represented based on the estimated user emotions. The system described in Appendix 7, characterized by the features described herein. (Note 47) The aforementioned diversity section is When providing diversity, the diversity algorithm is optimized by referring to past diversity data. The system described in Appendix 7, characterized by the features described herein. (Note 48) The aforementioned diversity section is It estimates user sentiment and determines diversity priorities based on the estimated user sentiment. The system described in Appendix 7, characterized by the features described herein. (Note 49) The aforementioned diversity section is When providing diversity, we consider user attribute information to provide the optimal diversity method. The system described in Appendix 7, characterized by the features described herein. [Explanation of Symbols]

[0209] 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. The reception area where you enter the details of your inquiry, A selection unit that selects the most suitable AI consultant based on the consultation content entered by the reception unit, The system comprises a provisioning unit in which an AI counselor selected by the aforementioned selection unit provides support to the client and offers specific advice and solutions. A system characterized by the following features.

2. The aforementioned supply unit is, We have an empathy department that utilizes past consultation data to provide highly empathetic support. The system according to feature 1.

3. The aforementioned supply unit is, Equipped with an anonymity section to protect anonymity. The system according to feature 1.

4. The aforementioned supply unit is, We have a solutions department that provides specific solutions to specific consultation issues. The system according to feature 1.

5. The aforementioned supply unit is, It is equipped with a section for handling things that are difficult to ask others about or research. The system according to feature 1.

6. The aforementioned supply unit is, Equipped with a message delivery department that delivers messages that resonate with the hearts of those seeking advice. The system according to feature 1.

7. The aforementioned supply unit is, Equipped with a diversity department to handle a wide range of consultation topics. The system according to feature 1.

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

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