System

Abstract

An object of a system according to an embodiment is to ensure high security and anonymity in a user's consultation.SOLUTION: A system according to an embodiment includes an encryption unit, an input unit, a matching unit, and an anonymization unit. The encryption unit encrypts the user information. The input unit inputs the user's complaint based on the information encrypted by the encryption unit. The matching unit specifies a specific criterion for the AI to match an expert based on the complaint input by the input unit. The anonymous section makes a consultation with the expert matched by the matching section anonymously.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient security for users to seek advice on their problems, and there is a risk that anonymity will not be maintained.

[0005] The system according to the embodiment aims to ensure high security and anonymity when users seek advice on their worries. [Means for solving the problem]

[0006] The system according to the embodiment includes an encryption unit, an input unit, a matching unit, and an anonymization unit. The encryption unit encrypts user information. The input unit inputs the user's concerns based on the information encrypted by the encryption unit. The matching unit specifies specific criteria that the AI ​​uses to match an expert based on the concerns input by the input unit. The anonymization unit provides anonymous consultation with the expert matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure high security and anonymity when users seek advice on their worries. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The problem-solving platform according to an embodiment of the present invention is a highly secure system exclusively for women in their 50s. This system features a user-friendly interface and advanced security features, allowing users to share and seek advice on their problems with peace of mind. For example, users log in to the platform and enter their personal information. This information is protected by advanced encryption technology and will not be leaked to third parties. Next, users enter their concerns and select the topic they wish to discuss. For example, they can choose from categories such as health, home, and work. Once the user enters their concerns, the system automatically matches them with an appropriate specialist. This matching is performed using AI to select the specialist best suited to the user's concerns. Consultations with specialists can be conducted via text chat, voice call, video call, and other methods. All consultation content is encrypted, and interactions on the system are completely anonymous. Furthermore, consultation history is not saved without the user's permission. This allows users to discuss their concerns with peace of mind. Furthermore, the system collects user feedback to improve the service. New features and services are regularly added to increase user satisfaction. This allows the problem-solving platform to provide a highly secure system where women in their 50s can discuss their concerns with peace of mind. For example, the concerns written by users can be quickly and accurately matched with experts, and the consultation details can be shared safely.In addition, the service can be improved based on user feedback, increasing user satisfaction.

[0029] A problem consultation platform according to an embodiment includes an encryption unit, an input unit, a matching unit, and an anonymization unit. The encryption unit encrypts user information. The encryption unit protects the user information using advanced encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The encryption unit can also verify data integrity using SHA (Secure Hash Algorithm). The input unit inputs the user's problems based on the information encrypted by the encryption unit. The input unit provides an interface in which the user selects from categories such as health, home, and work and inputs their problems. The input unit can also encrypt the information input by the user and store it in a secure manner. The matching unit uses AI to match experts based on the problems input by the input unit. The matching unit selects the expert best suited to the user's problem using AI algorithms such as neural networks and support vector machines. The matching unit can perform matching based on criteria such as the expert's qualifications, experience, and evaluation. The anonymization unit provides anonymous consultations with the experts matched by the matching unit. The anonymity unit encrypts the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity of interactions on the platform. This enables encryption of user information, input of concerns, expert matching, and anonymous consultation. As a result, the problem consultation platform according to the embodiment allows users to safely share and consult about their concerns.

[0030] The encryption unit can protect user information by specifying a specific encryption technique. The encryption unit encrypts user information using, for example, AES (Advanced Encryption Standard). AES is a symmetric key encryption method that provides fast and secure encryption. The encryption unit can also encrypt user information using RSA (Rivest-Shamir-Adleman). RSA is a public key encryption method that provides high security. The encryption unit can also verify data integrity using SHA (Secure Hash Algorithm). SHA is used to generate a hash value of data and detect data tampering. This enhances protection of user information through advanced encryption techniques. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without AI. For example, the encryption unit can perform encryption using an AI model that receives user information as input and outputs encrypted data.

[0031] The matching unit can select an expert for the user's problem using an AI algorithm. The matching unit can select the expert best suited to the user's problem using, for example, a neural network. A neural network processes data using multiple layers of artificial neurons and can learn complex patterns. The matching unit can also select an expert using a support vector machine (SVM). An SVM is an algorithm for mapping data into a high-dimensional space and finding optimal classification boundaries. The matching unit can also recommend an expert based on the user's problem using a recommendation system. A recommendation system is an algorithm that recommends the most appropriate expert based on the user's past behavior and evaluations. This allows the AI ​​algorithm to select the most appropriate expert for the user's problem. Some or all of the above-mentioned processing in the matching unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the matching unit can select an expert using a generative AI model that receives the user's problem as input and outputs the most appropriate expert.

[0032] The anonymity unit can encrypt the consultation content and specify specific methods for ensuring anonymity of interactions on the platform. The anonymity unit encrypts the consultation content using, for example, AES (Advanced Encryption Standard). AES provides fast and secure encryption and protects the confidentiality of the consultation content. The anonymity unit can also encrypt the consultation content using RSA (Rivest-Shamir-Adleman). RSA is a public key cryptography method that provides high security. Furthermore, the anonymity unit ensures complete anonymity of interactions on the platform using methods such as concealing IP addresses and using pseudonyms. For example, the anonymity unit conceals the user's IP address and transmits the consultation content using a pseudonym. This encrypts the consultation content and enables complete anonymity of interactions. Some or all of the above-described processing in the anonymity unit may be performed using, for example, AI, or may be performed without AI. For example, the anonymity unit can perform encryption using an AI model that receives the consultation content as input and outputs encrypted data.

[0033] The system can specify a specific method for collecting user feedback and using it to improve the service. For example, the system collects user feedback using a questionnaire. The questionnaire includes questions for evaluating user satisfaction and areas for improvement. The system can also collect feedback using reviews. Reviews are a means for users to provide ratings and comments on a service. The system can also collect feedback using a rating system. The rating system allows users to assign scores to each element of a service. This allows user feedback to be collected and used to improve the service. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can analyze the feedback using an AI model that receives user feedback as input and outputs areas for improvement.

[0034] The system allows a user to consult with an expert via text chat, voice call, or video call. The system may, for example, use text chat to provide text-based communication, allowing for real-time message transmission and reception. The system may also use voice calls to provide consultation with an expert. Voice calls are a means of audio communication, similar to a telephone call. The system may also use video calls to provide consultation with an expert. Video calls can simultaneously transmit and receive video and audio, providing an experience similar to face-to-face communication. This enables consultation with an expert in a variety of ways. Some or all of the above-described processing in the system may be performed using, for example, AI, or may be performed without AI. For example, the system may select a consultation method using an AI model that receives the user's consultation content as input and outputs the optimal consultation method.

[0035] During encryption, the encryption unit can select an encryption algorithm by referring to the user's past security history. For example, if the user previously selected high security settings, the encryption unit applies a similar high-strength encryption algorithm. If the user previously selected standard security settings, the encryption unit can also apply a standard encryption algorithm. Furthermore, if the user previously selected low security settings, the encryption unit can also apply a low-strength encryption algorithm. This allows the optimal encryption algorithm to be selected based on the user's past security history. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can select an encryption algorithm using an AI model that receives the user's past security history as input and outputs the optimal encryption algorithm.

[0036] During encryption, the encryption unit can customize the encryption method by taking into account the user's device information. For example, if the user is using a smartphone, the encryption unit can apply an encryption method optimized for mobile devices. If the user is using a PC, the encryption unit can also apply an encryption method optimized for desktop devices. Furthermore, if the user is using a tablet, the encryption unit can also apply an encryption method optimized for tablet devices. This allows the encryption method to be customized based on the user's device information. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can customize the encryption method using an AI model that receives the user's device information as input and outputs the optimal encryption method.

[0037] During encryption, the encryption unit can select an encryption method according to the user's input method. For example, if the user is using voice input, the encryption unit applies an encryption method optimized for voice data. If the user is using text input, the encryption unit can also apply an encryption method optimized for text data. Furthermore, if the user is using image input, the encryption unit can also apply an encryption method optimized for image data. This allows the optimal encryption method to be selected according to the user's input method. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can select the encryption method using an AI model that receives the user's input method as input and outputs the optimal encryption method.

[0038] During encryption, the encryption unit can prioritize encryption of highly relevant information based on the user's geographical location information. For example, when the user is at home, the encryption unit prioritizes encryption of personal information. When the user is in a public place, the encryption unit can also prioritize encryption of communication content. Furthermore, when the user is traveling, the encryption unit can also prioritize encryption of location information. This allows highly relevant information to be prioritized encryption based on the user's geographical location information. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can determine the encryption priority using an AI model that receives the user's geographical location information as input and outputs highly relevant information.

[0039] During encryption, the encryption unit can analyze the user's social media activities and encrypt related information. For example, the encryption unit prioritizes encrypting information shared by the user on social media. The encryption unit can also analyze the user's social media activities and encrypt related information. The encryption unit can also encrypt the user's interactions with friends on social media. This makes it possible to encrypt related information based on the user's social media activities. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can perform encryption using an AI model that receives the user's social media activities as input and outputs related information.

[0040] The encryption unit can customize the encryption method by reflecting the user's past feedback during encryption. For example, if the user previously required high security, the encryption unit can apply a strong encryption method. If the user previously required fast processing, the encryption unit can also apply an encryption method that prioritizes processing speed. Furthermore, if the user previously preferred a specific encryption method, the encryption unit can preferentially apply that method. This allows the encryption method to be customized based on the user's past feedback. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can customize the encryption method using an AI model that receives the user's past feedback as input and outputs the optimal encryption method.

[0041] The input unit can suggest an input method by referring to the user's past input history when inputting. For example, the input unit automatically displays as candidates worry categories that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest the content of worries that will be input during a specific time period based on the user's past input history. This makes it possible to suggest an optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can suggest an input method using an AI model that receives the user's past input history as input and outputs an optimal input method.

[0042] The input unit can filter the input content based on the user's current living situation and areas of interest at the time of input. For example, if the user is interested in health, the input unit can prioritize support for input of health-related concerns. If the user is interested in family issues, the input unit can also prioritize support for input of family-related concerns. Furthermore, if the user is interested in work, the input unit can also prioritize support for input of work-related concerns. This allows the input content to be filtered based on the user's current living situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can filter the input content using an AI model that receives the user's living situation and areas of interest as input and outputs optimal input content.

[0043] The input unit can select an input means according to the user's input method at the time of input. For example, if the user is using voice input, the input unit provides an interface optimized for voice input. If the user is using text input, the input unit can also provide an interface optimized for text input. Furthermore, if the user is using image input, the input unit can also provide an interface optimized for image input. This makes it possible to select the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can select the input means using an AI model that receives the user's input method as input and outputs the optimal input means.

[0044] The input unit can prioritize acquiring highly relevant input content based on the user's geographical location information when inputting. For example, when the user is at home, the input unit can prioritize support for input of home-related concerns. When the user is at work, the input unit can also prioritize support for input of work-related concerns. Furthermore, when the user is in a public place, the input unit can provide an input method that takes privacy into consideration. This allows highly relevant input content to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can acquire input content using an AI model that receives the user's geographical location information as input and outputs highly relevant input content.

[0045] The input unit can analyze the user's social media activity at the time of input and acquire related input content. For example, the input unit can automatically acquire concerns shared by the user on social media as input content. The input unit can also analyze the user's social media activity and acquire related concerns as input content. The input unit can also acquire related concerns as input content by referring to the user's interactions with friends on social media. In this way, related input content can be acquired based on the user's social media activity. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can acquire input content using an AI model that receives the user's social media activity as input and outputs related input content.

[0046] The input unit can customize the input method by reflecting the user's past feedback when inputting. For example, the input unit preferentially provides an input method that the user has used favorably in the past. The input unit can also provide an input method that reflects functions that the user has previously requested in feedback. The input unit can also provide an optimal input method based on feedback the user has previously given on input content. This allows the input method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can customize the input method using an AI model that receives the user's past feedback as input and outputs the optimal input method.

[0047] During matching, the matching unit can select an expert based on the user's past consultation history. For example, the matching unit re-matches experts that the user has previously consulted. The matching unit can also select an expert who has dealt with a similar problem based on the user's past consultation history. The matching unit can also analyze the user's past consultation history and select the most suitable expert. This makes it possible to select the optimal expert based on the user's past consultation history. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives the user's past consultation history as input and outputs the optimal expert.

[0048] During matching, the matching unit can filter experts based on the user's current living situation and areas of interest. For example, if the user is interested in health, the matching unit can prioritize matching with health-related experts. If the user is interested in household issues, the matching unit can also prioritize matching with household-related experts. Furthermore, if the user is interested in work, the matching unit can also prioritize matching with work-related experts. This makes it possible to filter experts based on the user's current living situation and areas of interest. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can filter experts using an AI model that receives the user's living situation and areas of interest as input and outputs the most suitable expert.

[0049] The matching unit can select the most suitable expert depending on the user's input method during matching. For example, if the user uses voice input, the matching unit selects an expert who can handle voice consultations. If the user uses text input, the matching unit can also select an expert who can handle text consultations. Furthermore, if the user uses image input, the matching unit can also select an expert who can handle image-based consultations. This makes it possible to select the most suitable expert depending on the user's input method. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives the user's input method as input and outputs the most suitable expert.

[0050] During matching, the matching unit can prioritize selecting highly relevant experts based on the user's geographical location information. For example, when the user is at home, the matching unit prioritizes matching with nearby experts. When the user is at work, the matching unit can also prioritize matching with experts near the workplace. Furthermore, when the user is traveling, the matching unit can also match the most appropriate expert based on the user's current location. This allows highly relevant experts to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select experts using an AI model that receives the user's geographical location information as input and outputs highly relevant experts.

[0051] During matching, the matching unit can analyze the user's social media activity and select a relevant expert. For example, the matching unit selects an expert who can address a problem shared by the user on social media. The matching unit can also analyze the user's social media activity and select a relevant expert. The matching unit can also select a relevant expert by referring to the activity of the user's friends on social media. This makes it possible to select a relevant expert based on the user's social media activity. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives the user's social media activity as input and outputs a relevant expert.

[0052] The matching unit can customize the matching method by reflecting the user's past feedback during matching. For example, the matching unit prioritizes matching with experts who the user has given high ratings to in the past. The matching unit can also select experts by reflecting conditions requested by the user in past feedback. The matching unit can also provide an optimal matching method based on the user's past feedback. This allows the matching method to be customized based on the user's past feedback. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can customize the matching method using an AI model that receives the user's past feedback as input and outputs the optimal matching method.

[0053] The anonymization unit can select an anonymization method based on the user's past consultation history when anonymizing. For example, if the user has requested high anonymity in the past, the anonymization unit applies a similar high anonymization method. If the user has requested standard anonymity in the past, the anonymization unit can also apply a standard anonymization method. Furthermore, if the user has requested low anonymity in the past, the anonymization unit can also apply a low anonymization method. This makes it possible to select an optimal anonymization method based on the user's past consultation history. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can select an anonymization method using an AI model that receives the user's past consultation history as input and outputs an optimal anonymization method.

[0054] During anonymization, the anonymization unit can customize the anonymization method by taking into account the user's device information. For example, if the user is using a smartphone, the anonymization unit can apply an anonymization method optimized for mobile devices. If the user is using a personal computer, the anonymization unit can also apply an anonymization method optimized for desktop devices. Furthermore, if the user is using a tablet, the anonymization unit can also apply an anonymization method optimized for tablet devices. This allows the anonymization method to be customized based on the user's device information. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can customize the anonymization method using an AI model that receives the user's device information as input and outputs the optimal anonymization method.

[0055] The anonymization unit can select the optimal anonymization means depending on the user's input method during anonymization. For example, if the user uses voice input, the anonymization unit applies an anonymization means optimized for voice data. If the user uses text input, the anonymization unit can also apply an anonymization means optimized for text data. Furthermore, if the user uses image input, the anonymization unit can also apply an anonymization means optimized for image data. This allows the optimal anonymization means to be selected depending on the user's input method. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can select the anonymization means using an AI model that receives the user's input method as input and outputs the optimal anonymization means.

[0056] During anonymization, the anonymization unit can prioritize anonymizing highly relevant information based on the user's geographical location information. For example, when the user is at home, the anonymization unit prioritizes anonymizing personal information. When the user is in a public place, the anonymization unit can also prioritize anonymizing communication content. Furthermore, when the user is traveling, the anonymization unit can also prioritize anonymizing location information. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can determine the anonymization priority using an AI model that receives the user's geographical location information as input and outputs highly relevant information.

[0057] During anonymization, the anonymization unit can analyze the user's social media activities and anonymize related information. For example, the anonymization unit prioritizes anonymization of information shared by the user on social media. The anonymization unit can also analyze the user's social media activities and anonymize related information. The anonymization unit can also anonymize the user's interactions with friends on social media. This allows related information to be anonymized based on the user's social media activities. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can perform anonymization using an AI model that receives the user's social media activities as input and outputs related information.

[0058] The anonymization unit can customize the anonymization method by reflecting the user's past feedback during anonymization. For example, if the user previously required high anonymity, the anonymization unit can apply a strong anonymization method. If the user previously required fast processing, the anonymization unit can also apply an anonymization method that prioritizes processing speed. Furthermore, if the user previously preferred a specific anonymization method, the anonymization unit can preferentially apply that method. This allows the anonymization method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can customize the anonymization method using an AI model that receives the user's past feedback as input and outputs the optimal anonymization method.

[0059] When collecting feedback, the feedback collection unit can select a collection method based on the user's past feedback history. For example, the feedback collection unit preferentially provides a feedback collection method that the user has previously preferred. The feedback collection unit can also provide a collection method that reflects functions that the user has previously requested in feedback. The feedback collection unit can also provide an optimal collection method based on feedback that the user has previously provided on feedback content. This makes it possible to select an optimal collection method based on the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can select a collection method using an AI model that receives the user's past feedback history as input and outputs an optimal collection method.

[0060] The feedback collection unit can customize the collection method by taking into account the user's device information when collecting feedback. For example, if the user uses a smartphone, the feedback collection unit can provide a feedback collection method optimized for the mobile device. If the user uses a personal computer, the feedback collection unit can also provide a feedback collection method optimized for the desktop device. Furthermore, if the user uses a tablet, the feedback collection unit can also provide a feedback collection method optimized for the tablet device. This allows the collection method to be customized based on the user's device information. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can customize the collection method using an AI model that receives the user's device information as input and outputs the optimal collection method.

[0061] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback based on the user's geographical location information. For example, when the user is at home, the feedback collection unit prioritizes collecting feedback related to the home. When the user is at work, the feedback collection unit can also prioritize collecting feedback related to the work. Furthermore, when the user is in a public place, the feedback collection unit can also prioritize collecting feedback related to the public place. This makes it possible to prioritize collecting highly relevant feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can determine the priority of feedback collection using an AI model that receives the user's geographical location information as input and outputs highly relevant feedback.

[0062] When collecting feedback, the feedback collection unit can analyze the user's social media activities and collect relevant feedback. For example, the feedback collection unit automatically collects feedback shared by the user on social media. The feedback collection unit can also analyze the user's social media activities and collect relevant feedback. The feedback collection unit can also collect relevant feedback by referring to the user's interactions with friends on social media. This makes it possible to collect relevant feedback based on the user's social media activities. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can collect feedback using an AI model that receives the user's social media activities as input and outputs relevant feedback.

[0063] When selecting a consultation means, the consultation means unit can suggest the optimal means by referring to the user's past consultation history. For example, if the user has preferred text chat in the past, the consultation means unit can preferentially suggest text chat. If the user has preferred voice calls in the past, the consultation means unit can also preferentially suggest voice calls. Furthermore, if the user has preferred video calls in the past, the consultation means unit can also preferentially suggest video calls. This makes it possible to suggest the optimal means based on the user's past consultation history. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives the user's past consultation history as input and outputs the optimal consultation means.

[0064] When selecting a consultation means, the consultation means unit can customize the optimal means by taking into consideration the user's device information. For example, if the user is using a smartphone, the consultation means unit can suggest a consultation means optimized for the mobile device. If the user is using a PC, the consultation means unit can also suggest a consultation means optimized for the desktop device. Furthermore, if the user is using a tablet, the consultation means unit can also suggest a consultation means optimized for the tablet device. This allows the optimal means to be customized based on the user's device information. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can customize the consultation means using an AI model that receives the user's device information as input and outputs the optimal consultation means.

[0065] When selecting a consultation means, the consultation means unit can suggest a means based on the user's geographical location information. For example, when the user is at home, the consultation means unit can suggest a consultation means that is easy to use at home. When the user is at work, the consultation means unit can also suggest a consultation means that is easy to use at work. Furthermore, when the user is in a public place, the consultation means unit can also suggest a consultation means that takes privacy into consideration. This makes it possible to suggest the optimal means based on the user's geographical location information. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives the user's geographical location information as input and outputs the optimal consultation means.

[0066] When selecting a consultation means, the consultation means unit can analyze the user's social media activity and suggest relevant means. For example, the consultation means unit can suggest consultation means that can address concerns shared by the user on social media. The consultation means unit can also analyze the user's social media activity and suggest relevant consultation means. The consultation means unit can also suggest relevant consultation means by referring to the activity of the user's friends on social media. This makes it possible to suggest relevant means based on the user's social media activity. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives the user's social media activity as input and outputs relevant consultation means.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The problem consultation platform can analyze the user's past consultation history and suggest new consultation topics based on the past consultation topics. For example, if the user has frequently consulted about health in the past, new consultation topics related to health can be suggested. Also, if the user has previously consulted about family problems, new consultation topics related to family can be suggested. Furthermore, if the user has previously consulted about work, new consultation topics related to work can be suggested. In this way, optimal consultation topics can be suggested based on the user's past consultation history.

[0069] The problem-solving platform can match a local expert with the user by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, it can preferentially match the user with an expert familiar with that area. Also, if the user is traveling, it can match the user with an expert familiar with the area to which the user is traveling. Furthermore, if the user is planning to move, it can match the user with an expert familiar with the new area in advance. This makes it possible to match the most suitable expert based on the user's geographical location information.

[0070] The problem-solving platform can analyze a user's social media activity and automatically acquire related problem content. For example, it can automatically acquire problems shared by the user on social media and register them as problem content. It can also analyze the user's social media activity and suggest related problem content. It can also acquire related problem content by referring to the user's interactions with friends on social media. This makes it possible to acquire the most appropriate problem content based on the user's social media activity.

[0071] The problem-solving platform can customize the feedback collection method by reflecting the user's past feedback. For example, if the user has previously preferred simple questionnaire-style feedback, the platform can collect feedback in a similar format. Also, if the user has previously preferred detailed review-style feedback, the platform can collect detailed feedback. Furthermore, if the user has previously preferred a specific rating system, the platform can preferentially use that rating system. This makes it possible to provide the optimal feedback collection method based on the user's past feedback.

[0072] The problem consultation platform can suggest the optimal consultation means by taking into account the user's device information. For example, if the user is using a smartphone, it can suggest a consultation means optimized for mobile devices. Also, if the user is using a PC, it can suggest a consultation means optimized for desktop devices. Furthermore, if the user is using a tablet, it can suggest a consultation means optimized for tablet devices. In this way, it is possible to provide the optimal consultation means based on the user's device information.

[0073] The problem consultation platform can suggest the most suitable consultation means by referring to the user's past consultation history. For example, if the user has preferred text chat in the past, text chat can be suggested with priority. Also, if the user has preferred voice calls in the past, voice calls can be suggested with priority. Furthermore, if the user has preferred video calls in the past, video calls can be suggested with priority. In this way, the most suitable consultation means can be provided based on the user's past consultation history.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The encryption unit encrypts the user information. The encryption unit protects the user information using advanced encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). It can also verify the integrity of the data using SHA (Secure Hash Algorithm). Step 2: The input unit inputs the user's concerns based on the information encrypted by the encryption unit. The input unit provides an interface where the user can select from categories such as health, home, and work and input their concerns. Furthermore, the information entered by the user can be encrypted and stored in a secure manner. Step 3: The matching unit uses AI to match experts based on the concerns input by the input unit. The matching unit uses AI algorithms such as neural networks and support vector machines to select the expert best suited to the user's concerns. Matching is performed based on criteria such as the expert's qualifications, experience, and evaluation. Step 4: The anonymous section will anonymously consult with the expert matched by the matching section. The anonymous section will encrypt the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity of interactions on the platform.

[0076] (Example 2) The problem-solving platform according to an embodiment of the present invention is a highly secure system exclusively for women in their 50s. This system features a user-friendly interface and advanced security features, allowing users to share and seek advice on their problems with peace of mind. For example, users log in to the platform and enter their personal information. This information is protected by advanced encryption technology and will not be leaked to third parties. Next, users enter their concerns and select the topic they wish to discuss. For example, they can choose from categories such as health, home, and work. Once the user enters their concerns, the system automatically matches them with an appropriate specialist. This matching is performed using AI to select the specialist best suited to the user's concerns. Consultations with specialists can be conducted via text chat, voice call, video call, and other methods. All consultation content is encrypted, and interactions on the system are completely anonymous. Furthermore, consultation history is not saved without the user's permission. This allows users to discuss their concerns with peace of mind. Furthermore, the system collects user feedback to improve the service. New features and services are regularly added to increase user satisfaction. This allows the problem-solving platform to provide a highly secure system where women in their 50s can discuss their concerns with peace of mind. For example, the concerns written by users can be quickly and accurately matched with experts, and the consultation details can be shared safely.In addition, the service can be improved based on user feedback, increasing user satisfaction.

[0077] A problem consultation platform according to an embodiment includes an encryption unit, an input unit, a matching unit, and an anonymization unit. The encryption unit encrypts user information. The encryption unit protects the user information using advanced encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The encryption unit can also verify data integrity using SHA (Secure Hash Algorithm). The input unit inputs the user's problems based on the information encrypted by the encryption unit. The input unit provides an interface in which the user selects from categories such as health, home, and work and inputs their problems. The input unit can also encrypt the information input by the user and store it in a secure manner. The matching unit uses AI to match experts based on the problems input by the input unit. The matching unit selects the expert best suited to the user's problem using AI algorithms such as neural networks and support vector machines. The matching unit can perform matching based on criteria such as the expert's qualifications, experience, and evaluation. The anonymization unit provides anonymous consultations with the experts matched by the matching unit. The anonymity unit encrypts the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity of interactions on the platform. This enables encryption of user information, input of concerns, expert matching, and anonymous consultation. As a result, the problem consultation platform according to the embodiment allows users to safely share and consult about their concerns.

[0078] The encryption unit can protect user information by specifying a specific encryption technique. The encryption unit encrypts user information using, for example, AES (Advanced Encryption Standard). AES is a symmetric key encryption method that provides fast and secure encryption. The encryption unit can also encrypt user information using RSA (Rivest-Shamir-Adleman). RSA is a public key encryption method that provides high security. The encryption unit can also verify data integrity using SHA (Secure Hash Algorithm). SHA is used to generate a hash value of data and detect data tampering. This enhances protection of user information through advanced encryption techniques. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without AI. For example, the encryption unit can perform encryption using an AI model that receives user information as input and outputs encrypted data.

[0079] The matching unit can select an expert for the user's problem using an AI algorithm. The matching unit can select the expert best suited to the user's problem using, for example, a neural network. A neural network processes data using multiple layers of artificial neurons and can learn complex patterns. The matching unit can also select an expert using a support vector machine (SVM). An SVM is an algorithm for mapping data into a high-dimensional space and finding optimal classification boundaries. The matching unit can also recommend an expert based on the user's problem using a recommendation system. A recommendation system is an algorithm that recommends the most appropriate expert based on the user's past behavior and evaluations. This allows the AI ​​algorithm to select the most appropriate expert for the user's problem. Some or all of the above-mentioned processing in the matching unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the matching unit can select an expert using a generative AI model that receives the user's problem as input and outputs the most appropriate expert.

[0080] The anonymity unit can encrypt the consultation content and specify specific methods for ensuring anonymity of interactions on the platform. The anonymity unit encrypts the consultation content using, for example, AES (Advanced Encryption Standard). AES provides fast and secure encryption and protects the confidentiality of the consultation content. The anonymity unit can also encrypt the consultation content using RSA (Rivest-Shamir-Adleman). RSA is a public key cryptography method that provides high security. Furthermore, the anonymity unit ensures complete anonymity of interactions on the platform using methods such as concealing IP addresses and using pseudonyms. For example, the anonymity unit conceals the user's IP address and transmits the consultation content using a pseudonym. This encrypts the consultation content and enables complete anonymity of interactions. Some or all of the above-described processing in the anonymity unit may be performed using, for example, AI, or may be performed without AI. For example, the anonymity unit can perform encryption using an AI model that receives the consultation content as input and outputs encrypted data.

[0081] The system can specify a specific method for collecting user feedback and using it to improve the service. For example, the system collects user feedback using a questionnaire. The questionnaire includes questions for evaluating user satisfaction and areas for improvement. The system can also collect feedback using reviews. Reviews are a means for users to provide ratings and comments on a service. The system can also collect feedback using a rating system. The rating system allows users to assign scores to each element of a service. This allows user feedback to be collected and used to improve the service. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can analyze the feedback using an AI model that receives user feedback as input and outputs areas for improvement.

[0082] The system allows a user to consult with an expert via text chat, voice call, or video call. The system may, for example, use text chat to provide text-based communication, allowing for real-time message transmission and reception. The system may also use voice calls to provide consultation with an expert. Voice calls are a means of audio communication, similar to a telephone call. The system may also use video calls to provide consultation with an expert. Video calls can simultaneously transmit and receive video and audio, providing an experience similar to face-to-face communication. This enables consultation with an expert in a variety of ways. Some or all of the above-described processing in the system may be performed using, for example, AI, or may be performed without AI. For example, the system may select a consultation method using an AI model that receives the user's consultation content as input and outputs the optimal consultation method.

[0083] The encryption unit can estimate the user's emotions and adjust the encryption strength based on the estimated user emotions. For example, if the user is feeling anxious, the encryption unit can set the encryption strength to maximum, providing higher security. If the user is relaxed, the encryption unit can use standard encryption strength and prioritize processing speed. Also, if the user is in a hurry, the encryption unit can set the encryption strength to medium, performing quick processing. This allows the encryption strength to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without AI. For example, the encryption unit can adjust the encryption strength using an AI model that receives user emotion data as input and outputs encryption strength.

[0084] During encryption, the encryption unit can select an encryption algorithm by referring to the user's past security history. For example, if the user previously selected high security settings, the encryption unit applies a similar high-strength encryption algorithm. If the user previously selected standard security settings, the encryption unit can also apply a standard encryption algorithm. Furthermore, if the user previously selected low security settings, the encryption unit can also apply a low-strength encryption algorithm. This allows the optimal encryption algorithm to be selected based on the user's past security history. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can select an encryption algorithm using an AI model that receives the user's past security history as input and outputs the optimal encryption algorithm.

[0085] During encryption, the encryption unit can customize the encryption method by taking into account the user's device information. For example, if the user is using a smartphone, the encryption unit can apply an encryption method optimized for mobile devices. If the user is using a PC, the encryption unit can also apply an encryption method optimized for desktop devices. Furthermore, if the user is using a tablet, the encryption unit can also apply an encryption method optimized for tablet devices. This allows the encryption method to be customized based on the user's device information. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can customize the encryption method using an AI model that receives the user's device information as input and outputs the optimal encryption method.

[0086] During encryption, the encryption unit can select an encryption method according to the user's input method. For example, if the user is using voice input, the encryption unit applies an encryption method optimized for voice data. If the user is using text input, the encryption unit can also apply an encryption method optimized for text data. Furthermore, if the user is using image input, the encryption unit can also apply an encryption method optimized for image data. This allows the optimal encryption method to be selected according to the user's input method. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can select the encryption method using an AI model that receives the user's input method as input and outputs the optimal encryption method.

[0087] The encryption unit can estimate the user's emotions and determine encryption priorities based on the estimated user emotions. For example, if the user is feeling anxious, the encryption unit prioritizes encryption of the most important information. If the user is relaxed, the encryption unit can also encrypt all information equally. Furthermore, if the user is in a hurry, the encryption unit can prioritize encryption of less important information. This allows encryption priorities to be determined according to the user's emotions. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can determine encryption priorities using an AI model that receives user emotion data as input and outputs encryption priorities.

[0088] During encryption, the encryption unit can prioritize encryption of highly relevant information based on the user's geographical location information. For example, when the user is at home, the encryption unit prioritizes encryption of personal information. When the user is in a public place, the encryption unit can also prioritize encryption of communication content. Furthermore, when the user is traveling, the encryption unit can also prioritize encryption of location information. This allows highly relevant information to be prioritized encryption based on the user's geographical location information. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can determine the encryption priority using an AI model that receives the user's geographical location information as input and outputs highly relevant information.

[0089] During encryption, the encryption unit can analyze the user's social media activities and encrypt related information. For example, the encryption unit prioritizes encrypting information shared by the user on social media. The encryption unit can also analyze the user's social media activities and encrypt related information. The encryption unit can also encrypt the user's interactions with friends on social media. This makes it possible to encrypt related information based on the user's social media activities. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can perform encryption using an AI model that receives the user's social media activities as input and outputs related information.

[0090] The encryption unit can customize the encryption method by reflecting the user's past feedback during encryption. For example, if the user previously required high security, the encryption unit can apply a strong encryption method. If the user previously required fast processing, the encryption unit can also apply an encryption method that prioritizes processing speed. Furthermore, if the user previously preferred a specific encryption method, the encryption unit can preferentially apply that method. This allows the encryption method to be customized based on the user's past feedback. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can customize the encryption method using an AI model that receives the user's past feedback as input and outputs the optimal encryption method.

[0091] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user's emotions. For example, if the user is nervous, the input unit can provide an interface with calm colors to reduce visual stress. If the user is having fun, the input unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the input unit can provide an interface with simple and high visibility to make input work easier. This allows the design of the input interface to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the interface design using an AI model that receives user emotion data as input and outputs an optimal interface design.

[0092] The input unit can suggest an input method by referring to the user's past input history when inputting. For example, the input unit automatically displays as candidates worry categories that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest the content of worries that will be input during a specific time period based on the user's past input history. This makes it possible to suggest an optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can suggest an input method using an AI model that receives the user's past input history as input and outputs an optimal input method.

[0093] The input unit can filter the input content based on the user's current living situation and areas of interest at the time of input. For example, if the user is interested in health, the input unit can prioritize support for input of health-related concerns. If the user is interested in family issues, the input unit can also prioritize support for input of family-related concerns. Furthermore, if the user is interested in work, the input unit can also prioritize support for input of work-related concerns. This allows the input content to be filtered based on the user's current living situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can filter the input content using an AI model that receives the user's living situation and areas of interest as input and outputs optimal input content.

[0094] The input unit can select an input means according to the user's input method at the time of input. For example, if the user is using voice input, the input unit provides an interface optimized for voice input. If the user is using text input, the input unit can also provide an interface optimized for text input. Furthermore, if the user is using image input, the input unit can also provide an interface optimized for image input. This makes it possible to select the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can select the input means using an AI model that receives the user's input method as input and outputs the optimal input means.

[0095] The input unit can estimate the user's emotions and determine the priority of input contents based on the estimated user's emotions. For example, when the user is feeling anxious, the input unit prioritizes input of the most important concern. When the user is relaxed, the input unit can also input all concerns equally. Furthermore, when the user is in a hurry, the input unit can postpone input of less important concerns. This allows the priority of input contents to be determined according to the user's emotions. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can determine the priority of input contents using an AI model that receives user emotion data as input and outputs the priority of input contents.

[0096] The input unit can prioritize acquiring highly relevant input content based on the user's geographical location information when inputting. For example, when the user is at home, the input unit can prioritize support for input of home-related concerns. When the user is at work, the input unit can also prioritize support for input of work-related concerns. Furthermore, when the user is in a public place, the input unit can provide an input method that takes privacy into consideration. This allows highly relevant input content to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can acquire input content using an AI model that receives the user's geographical location information as input and outputs highly relevant input content.

[0097] The input unit can analyze the user's social media activity at the time of input and acquire related input content. For example, the input unit can automatically acquire concerns shared by the user on social media as input content. The input unit can also analyze the user's social media activity and acquire related concerns as input content. The input unit can also acquire related concerns as input content by referring to the user's interactions with friends on social media. In this way, related input content can be acquired based on the user's social media activity. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can acquire input content using an AI model that receives the user's social media activity as input and outputs related input content.

[0098] The input unit can customize the input method by reflecting the user's past feedback when inputting. For example, the input unit preferentially provides an input method that the user has used favorably in the past. The input unit can also provide an input method that reflects functions that the user has previously requested in feedback. The input unit can also provide an optimal input method based on feedback the user has previously given on input content. This allows the input method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can customize the input method using an AI model that receives the user's past feedback as input and outputs the optimal input method.

[0099] The matching unit can estimate the user's emotions and adjust the matching algorithm based on the estimated user's emotions. For example, if the user is feeling anxious, the matching unit can prioritize matching with experienced experts. If the user is relaxed, the matching unit can also provide options from a wide range of experts. Furthermore, if the user is in a hurry, the matching unit can prioritize matching with experts who can respond quickly. This allows the matching algorithm to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives user emotion data as input and adjusts the matching algorithm.

[0100] During matching, the matching unit can select an expert based on the user's past consultation history. For example, the matching unit re-matches experts that the user has previously consulted. The matching unit can also select an expert who has dealt with a similar problem based on the user's past consultation history. The matching unit can also analyze the user's past consultation history and select the most suitable expert. This makes it possible to select the optimal expert based on the user's past consultation history. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives the user's past consultation history as input and outputs the optimal expert.

[0101] During matching, the matching unit can filter experts based on the user's current living situation and areas of interest. For example, if the user is interested in health, the matching unit can prioritize matching with health-related experts. If the user is interested in household issues, the matching unit can also prioritize matching with household-related experts. Furthermore, if the user is interested in work, the matching unit can also prioritize matching with work-related experts. This makes it possible to filter experts based on the user's current living situation and areas of interest. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can filter experts using an AI model that receives the user's living situation and areas of interest as input and outputs the most suitable expert.

[0102] The matching unit can select the most suitable expert depending on the user's input method during matching. For example, if the user uses voice input, the matching unit selects an expert who can handle voice consultations. If the user uses text input, the matching unit can also select an expert who can handle text consultations. Furthermore, if the user uses image input, the matching unit can also select an expert who can handle image-based consultations. This makes it possible to select the most suitable expert depending on the user's input method. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives the user's input method as input and outputs the most suitable expert.

[0103] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated user emotions. For example, if the user is feeling anxious, the matching unit can prioritize matching with the most suitable expert. If the user is relaxed, the matching unit can also match with experts from a wide range of options. Furthermore, if the user is in a hurry, the matching unit can also prioritize matching with experts who can respond quickly. This makes it possible to determine matching priorities according to the user's emotions. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select experts using an AI model that receives user emotion data as input and outputs matching priorities.

[0104] During matching, the matching unit can prioritize selecting highly relevant experts based on the user's geographical location information. For example, when the user is at home, the matching unit prioritizes matching with nearby experts. When the user is at work, the matching unit can also prioritize matching with experts near the workplace. Furthermore, when the user is traveling, the matching unit can also match the most appropriate expert based on the user's current location. This allows highly relevant experts to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select experts using an AI model that receives the user's geographical location information as input and outputs highly relevant experts.

[0105] During matching, the matching unit can analyze the user's social media activity and select a relevant expert. For example, the matching unit selects an expert who can address a problem shared by the user on social media. The matching unit can also analyze the user's social media activity and select a relevant expert. The matching unit can also select a relevant expert by referring to the activity of the user's friends on social media. This makes it possible to select a relevant expert based on the user's social media activity. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can select an expert using an AI model that receives the user's social media activity as input and outputs a relevant expert.

[0106] The matching unit can customize the matching method by reflecting the user's past feedback during matching. For example, the matching unit prioritizes matching with experts who the user has given high ratings to in the past. The matching unit can also select experts by reflecting conditions requested by the user in past feedback. The matching unit can also provide an optimal matching method based on the user's past feedback. This allows the matching method to be customized based on the user's past feedback. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can customize the matching method using an AI model that receives the user's past feedback as input and outputs the optimal matching method.

[0107] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated user's emotions. For example, if the user is feeling anxious, the anonymization unit can perform maximum anonymization and completely hide personal information. If the user is relaxed, the anonymization unit can perform standard anonymization and hide the minimum necessary information. Furthermore, if the user is in a hurry, the anonymization unit can quickly perform anonymization to smoothly proceed with the consultation. This allows the anonymization method to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can adjust the anonymization method using an AI model that receives the user's emotional data as input and outputs an anonymization method.

[0108] The anonymization unit can select an anonymization method based on the user's past consultation history when anonymizing. For example, if the user has requested high anonymity in the past, the anonymization unit applies a similar high anonymization method. If the user has requested standard anonymity in the past, the anonymization unit can also apply a standard anonymization method. Furthermore, if the user has requested low anonymity in the past, the anonymization unit can also apply a low anonymization method. This makes it possible to select an optimal anonymization method based on the user's past consultation history. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can select an anonymization method using an AI model that receives the user's past consultation history as input and outputs an optimal anonymization method.

[0109] During anonymization, the anonymization unit can customize the anonymization method by taking into account the user's device information. For example, if the user is using a smartphone, the anonymization unit can apply an anonymization method optimized for mobile devices. If the user is using a personal computer, the anonymization unit can also apply an anonymization method optimized for desktop devices. Furthermore, if the user is using a tablet, the anonymization unit can also apply an anonymization method optimized for tablet devices. This allows the anonymization method to be customized based on the user's device information. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can customize the anonymization method using an AI model that receives the user's device information as input and outputs the optimal anonymization method.

[0110] The anonymization unit can select the optimal anonymization means depending on the user's input method during anonymization. For example, if the user uses voice input, the anonymization unit applies an anonymization means optimized for voice data. If the user uses text input, the anonymization unit can also apply an anonymization means optimized for text data. Furthermore, if the user uses image input, the anonymization unit can also apply an anonymization means optimized for image data. This allows the optimal anonymization means to be selected depending on the user's input method. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can select the anonymization means using an AI model that receives the user's input method as input and outputs the optimal anonymization means.

[0111] The anonymization unit can estimate the user's emotions and determine anonymization priorities based on the estimated user emotions. For example, if the user is feeling anxious, the anonymization unit can prioritize anonymizing the most important information. If the user is relaxed, the anonymization unit can also equally anonymize all information. Furthermore, if the user is in a hurry, the anonymization unit can postpone anonymizing less important information. This allows the anonymization priorities to be determined according to the user's emotions. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can determine the anonymization priorities using an AI model that receives user emotion data as input and outputs anonymization priorities.

[0112] During anonymization, the anonymization unit can prioritize anonymizing highly relevant information based on the user's geographical location information. For example, when the user is at home, the anonymization unit prioritizes anonymizing personal information. When the user is in a public place, the anonymization unit can also prioritize anonymizing communication content. Furthermore, when the user is traveling, the anonymization unit can also prioritize anonymizing location information. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can determine the anonymization priority using an AI model that receives the user's geographical location information as input and outputs highly relevant information.

[0113] During anonymization, the anonymization unit can analyze the user's social media activities and anonymize related information. For example, the anonymization unit prioritizes anonymization of information shared by the user on social media. The anonymization unit can also analyze the user's social media activities and anonymize related information. The anonymization unit can also anonymize the user's interactions with friends on social media. This allows related information to be anonymized based on the user's social media activities. Some or all of the above-described processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can perform anonymization using an AI model that receives the user's social media activities as input and outputs related information.

[0114] The anonymization unit can customize the anonymization method by reflecting the user's past feedback during anonymization. For example, if the user previously required high anonymity, the anonymization unit can apply a strong anonymization method. If the user previously required fast processing, the anonymization unit can also apply an anonymization method that prioritizes processing speed. Furthermore, if the user previously preferred a specific anonymization method, the anonymization unit can preferentially apply that method. This allows the anonymization method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can customize the anonymization method using an AI model that receives the user's past feedback as input and outputs the optimal anonymization method.

[0115] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is feeling anxious, the feedback collection unit can provide a simple and quick feedback collection method. If the user is relaxed, the feedback collection unit can also provide a detailed feedback collection method. Furthermore, if the user is in a hurry, the feedback collection unit can also provide a feedback collection method that focuses on the most important items. This makes it possible to adjust the feedback collection method according to the user's emotions. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI or without AI. For example, the feedback collection unit can adjust the feedback collection method using an AI model that receives user emotion data as input and outputs a feedback collection method.

[0116] When collecting feedback, the feedback collection unit can select a collection method based on the user's past feedback history. For example, the feedback collection unit preferentially provides a feedback collection method that the user has previously preferred. The feedback collection unit can also provide a collection method that reflects functions that the user has previously requested in feedback. The feedback collection unit can also provide an optimal collection method based on feedback that the user has previously provided on feedback content. This makes it possible to select an optimal collection method based on the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can select a collection method using an AI model that receives the user's past feedback history as input and outputs an optimal collection method.

[0117] The feedback collection unit can customize the collection method by taking into account the user's device information when collecting feedback. For example, if the user uses a smartphone, the feedback collection unit can provide a feedback collection method optimized for the mobile device. If the user uses a personal computer, the feedback collection unit can also provide a feedback collection method optimized for the desktop device. Furthermore, if the user uses a tablet, the feedback collection unit can also provide a feedback collection method optimized for the tablet device. This allows the collection method to be customized based on the user's device information. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can customize the collection method using an AI model that receives the user's device information as input and outputs the optimal collection method.

[0118] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated user's emotions. For example, when the user is feeling anxious, the feedback collection unit prioritizes feedback collection of the most important items. When the user is relaxed, the feedback collection unit can also collect feedback equally for all items. Furthermore, when the user is in a hurry, the feedback collection unit can postpone feedback collection of less important items. In this way, the priority of feedback collection can be determined according to the user's emotions. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI or without AI. For example, the feedback collection unit can determine the priority of feedback collection using an AI model that receives user emotion data as input and outputs the priority of feedback collection.

[0119] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback based on the user's geographical location information. For example, when the user is at home, the feedback collection unit prioritizes collecting feedback related to the home. When the user is at work, the feedback collection unit can also prioritize collecting feedback related to the work. Furthermore, when the user is in a public place, the feedback collection unit can also prioritize collecting feedback related to the public place. This makes it possible to prioritize collecting highly relevant feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can determine the priority of feedback collection using an AI model that receives the user's geographical location information as input and outputs highly relevant feedback.

[0120] When collecting feedback, the feedback collection unit can analyze the user's social media activities and collect relevant feedback. For example, the feedback collection unit automatically collects feedback shared by the user on social media. The feedback collection unit can also analyze the user's social media activities and collect relevant feedback. The feedback collection unit can also collect relevant feedback by referring to the user's interactions with friends on social media. This makes it possible to collect relevant feedback based on the user's social media activities. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can collect feedback using an AI model that receives the user's social media activities as input and outputs relevant feedback.

[0121] The consultation means unit can estimate the user's emotions and select a consultation means based on the estimated user's emotions. For example, if the user is feeling anxious, the consultation means unit can preferentially suggest text chat. If the user is relaxed, the consultation means unit can also preferentially suggest voice call. Furthermore, if the user is in a hurry, the consultation means unit can also preferentially suggest video call. This makes it possible to select a consultation means according to the user's emotions. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives user emotion data as input and outputs the optimal consultation means.

[0122] When selecting a consultation means, the consultation means unit can suggest the optimal means by referring to the user's past consultation history. For example, if the user has preferred text chat in the past, the consultation means unit can preferentially suggest text chat. If the user has preferred voice calls in the past, the consultation means unit can also preferentially suggest voice calls. Furthermore, if the user has preferred video calls in the past, the consultation means unit can also preferentially suggest video calls. This makes it possible to suggest the optimal means based on the user's past consultation history. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives the user's past consultation history as input and outputs the optimal consultation means.

[0123] When selecting a consultation means, the consultation means unit can customize the optimal means by taking into consideration the user's device information. For example, if the user is using a smartphone, the consultation means unit can suggest a consultation means optimized for the mobile device. If the user is using a PC, the consultation means unit can also suggest a consultation means optimized for the desktop device. Furthermore, if the user is using a tablet, the consultation means unit can also suggest a consultation means optimized for the tablet device. This allows the optimal means to be customized based on the user's device information. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can customize the consultation means using an AI model that receives the user's device information as input and outputs the optimal consultation means.

[0124] The consultation means unit can estimate the user's emotions and determine the priority of consultation means based on the estimated user's emotions. For example, if the user is feeling anxious, the consultation means unit preferentially suggests the most reassuring consultation means. If the user is relaxed, the consultation means unit can also suggest consultation means from a wide range of options. Furthermore, if the user is in a hurry, the consultation means unit can also preferentially suggest consultation means that can provide a quick response. This makes it possible to determine the priority of consultation means according to the user's emotions. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select consultation means using an AI model that receives user emotion data as input and outputs the priority of consultation means.

[0125] When selecting a consultation means, the consultation means unit can suggest a means based on the user's geographical location information. For example, when the user is at home, the consultation means unit can suggest a consultation means that is easy to use at home. When the user is at work, the consultation means unit can also suggest a consultation means that is easy to use at work. Furthermore, when the user is in a public place, the consultation means unit can also suggest a consultation means that takes privacy into consideration. This makes it possible to suggest the optimal means based on the user's geographical location information. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives the user's geographical location information as input and outputs the optimal consultation means.

[0126] When selecting a consultation means, the consultation means unit can analyze the user's social media activity and suggest relevant means. For example, the consultation means unit can suggest consultation means that can address concerns shared by the user on social media. The consultation means unit can also analyze the user's social media activity and suggest relevant consultation means. The consultation means unit can also suggest relevant consultation means by referring to the activity of the user's friends on social media. This makes it possible to suggest relevant means based on the user's social media activity. Some or all of the above-mentioned processing in the consultation means unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation means unit can select a consultation means using an AI model that receives the user's social media activity as input and outputs relevant consultation means. === Hard Collateral 1-1 === Each of the multiple elements, including the encryption unit, input unit, matching unit, and anonymity unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the encryption unit is implemented by the specific processing unit 290 of the data processing device 12 and protects user information using encryption technology such as AES or RSA. The input unit is implemented by the control unit 46A of the smart device 14 and provides an interface through which the user can select from categories such as health, home, and work and input their concerns. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and uses an AI algorithm to select the expert best suited to the user's concerns. The anonymity unit is implemented by the control unit 46A of the smart device 14 and encrypts the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity during interactions on the platform. === Hard Collateral 1-2 === Each of the multiple elements, including the encryption unit, input unit, matching unit, and anonymity unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the encryption unit is implemented by the specific processing unit 290 of the data processing device 12 and protects user information using encryption technology such as AES or RSA. The input unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface through which the user selects from categories such as health, home, and work and inputs their concerns. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and uses an AI algorithm to select the expert best suited to the user's concerns. The anonymity unit is implemented by the control unit 46A of the smart glasses 214 and encrypts the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity during interactions on the platform. === Hard Collateral 1-3 === Each of the multiple elements, including the encryption unit, input unit, matching unit, and anonymity unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the encryption unit is implemented by the specific processing unit 290 of the data processing device 12 and protects user information using encryption technology such as AES or RSA. The input unit is implemented by the control unit 46A of the headset-type terminal 314 and provides an interface through which the user can select from categories such as health, home, and work and input their concerns. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and uses an AI algorithm to select the expert best suited to the user's concerns. The anonymity unit is implemented by the control unit 46A of the headset-type terminal 314 and encrypts the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity during interactions on the platform. === Hard Collateral 1-4 === Each of the multiple elements, including the encryption unit, input unit, matching unit, and anonymity unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the encryption unit is implemented by the specific processing unit 290 of the data processing device 12 and protects user information using encryption technology such as AES or RSA. The input unit is implemented by the control unit 46A of the robot 414 and provides an interface through which the user can select from categories such as health, home, and work and input their concerns. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and uses an AI algorithm to select the expert best suited to the user's concerns. The anonymity unit is implemented by the control unit 46A of the robot 414 and encrypts the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity during interactions on the platform.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The problem consultation platform can estimate the user's emotions and prioritize the consultation contents based on the estimated emotions. For example, if the user is feeling very anxious, the most urgent consultation contents can be given priority. Also, if the user is relaxed, all consultation contents can be handled equally. Furthermore, if the user is in a hurry, less important consultation contents can be postponed. This makes it possible to flexibly adjust the priority of consultation contents according to the user's emotions.

[0129] The problem consultation platform can analyze the user's past consultation history and suggest new consultation topics based on the past consultation topics. For example, if the user has frequently consulted about health in the past, new consultation topics related to health can be suggested. Also, if the user has previously consulted about family problems, new consultation topics related to family can be suggested. Furthermore, if the user has previously consulted about work, new consultation topics related to work can be suggested. In this way, optimal consultation topics can be suggested based on the user's past consultation history.

[0130] The problem-solving platform can match a local expert with the user by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, it can preferentially match the user with an expert familiar with that area. Also, if the user is traveling, it can match the user with an expert familiar with the area to which the user is traveling. Furthermore, if the user is planning to move, it can match the user with an expert familiar with the new area in advance. This makes it possible to match the most suitable expert based on the user's geographical location information.

[0131] The problem-solving platform can analyze a user's social media activity and automatically acquire related problem content. For example, it can automatically acquire problems shared by the user on social media and register them as problem content. It can also analyze the user's social media activity and suggest related problem content. It can also acquire related problem content by referring to the user's interactions with friends on social media. This makes it possible to acquire the most appropriate problem content based on the user's social media activity.

[0132] The advice platform can estimate the user's emotions and adjust the interface design based on the estimated emotions. For example, if the user is nervous, it can provide a calming interface to reduce visual stress. If the user is having fun, it can provide a bright interface to make inputting tasks more enjoyable. Furthermore, if the user is tired, it can provide a simple, highly visible interface to make inputting tasks easier. This allows the interface design to be flexibly adjusted according to the user's emotions.

[0133] The problem-solving platform can customize the feedback collection method by reflecting the user's past feedback. For example, if the user has previously preferred simple questionnaire-style feedback, the platform can collect feedback in a similar format. Also, if the user has previously preferred detailed review-style feedback, the platform can collect detailed feedback. Furthermore, if the user has previously preferred a specific rating system, the platform can preferentially use that rating system. This makes it possible to provide the optimal feedback collection method based on the user's past feedback.

[0134] The problem-solving platform can estimate the user's emotions and determine the priority of feedback collection based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize feedback collection on the most important items. If the user is relaxed, it can collect feedback equally on all items. Furthermore, if the user is in a hurry, it can postpone feedback collection on less important items. This makes it possible to flexibly adjust the priority of feedback collection according to the user's emotions.

[0135] The problem consultation platform can suggest the optimal consultation means by taking into account the user's device information. For example, if the user is using a smartphone, it can suggest a consultation means optimized for mobile devices. Also, if the user is using a PC, it can suggest a consultation means optimized for desktop devices. Furthermore, if the user is using a tablet, it can suggest a consultation means optimized for tablet devices. In this way, it is possible to provide the optimal consultation means based on the user's device information.

[0136] The problem consultation platform can estimate the user's emotions and prioritize consultation methods based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize the most reassuring consultation method. Also, if the user is relaxed, it can suggest consultation methods from a wide range of options. Furthermore, if the user is in a hurry, it can prioritize the consultation method that can respond quickly. This makes it possible to flexibly adjust the priority of consultation methods according to the user's emotions.

[0137] The problem consultation platform can suggest the most suitable consultation means by referring to the user's past consultation history. For example, if the user has preferred text chat in the past, text chat can be suggested with priority. Also, if the user has preferred voice calls in the past, voice calls can be suggested with priority. Furthermore, if the user has preferred video calls in the past, video calls can be suggested with priority. In this way, the most suitable consultation means can be provided based on the user's past consultation history.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The encryption unit encrypts the user information. The encryption unit protects the user information using advanced encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). It can also verify the integrity of the data using SHA (Secure Hash Algorithm). Step 2: The input unit inputs the user's concerns based on the information encrypted by the encryption unit. The input unit provides an interface where the user can select from categories such as health, home, and work and input their concerns. Furthermore, the information entered by the user can be encrypted and stored in a secure manner. Step 3: The matching unit uses AI to match experts based on the concerns input by the input unit. The matching unit uses AI algorithms such as neural networks and support vector machines to select the expert best suited to the user's concerns. Matching is performed based on criteria such as the expert's qualifications, experience, and evaluation. Step 4: The anonymous section will anonymously consult with the expert matched by the matching section. The anonymous section will encrypt the consultation content using methods such as concealing IP addresses and using pseudonyms, ensuring complete anonymity of interactions on the platform.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0183] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] 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.

[0203] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0205] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0209] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0211] [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an encryption unit that encrypts user information; an input unit for inputting a user's worries based on the information encrypted by the encryption unit; A matching unit that specifies specific criteria for AI to match experts based on the concerns input by the input unit; an anonymity unit that anonymously consults with the expert matched by the matching unit; Equipped with A system characterized by:

2. The encryption unit Protect user information by specifying specific encryption techniques 2. The system of claim 1.

3. The matching unit Using AI algorithms to select experts for users' concerns 2. The system of claim 1.

4. The anonymity part is Encrypt the content of the consultation and clearly state how to keep interactions on the platform anonymous.

2. The system of claim 1.

5. The system comprises: Identify specific ways to collect user feedback and use it to improve your service 2. The system of claim 1.

6. The system comprises: Consult with experts via text chat, voice call, or video call 2. The system of claim 1.

7. The encryption unit Estimate user emotions and adjust encryption strength based on the estimated user emotions 2. The system of claim 1.

8. The encryption unit When encrypting, the encryption algorithm is selected based on the user's security history.

2. The system of claim 1.

Images