system

Abstract

The system according to this embodiment aims to provide concrete solutions for efficiently resolving users' problems. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a search unit, and a matching unit. The reception unit receives the user's problem. The generation unit analyzes the problem received by the reception unit and generates a specific solution. The search unit searches for an expert who has the solution generated by the generation unit. The matching unit matches the expert found by the search unit with the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to find a specific solution for efficiently solving the user's troubles.

[0005] The system according to the embodiment aims to provide a specific solution for efficiently solving the user's troubles.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a search unit, and a matching unit. The reception unit receives the user's problem. The generation unit analyzes the problem received by the reception unit and generates a specific solution. The search unit searches for an expert who has the solution generated by the generation unit. The matching unit matches the expert found by the search unit with the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide concrete solutions for efficiently resolving users' problems. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The problem-solving system according to an embodiment of the present invention is a system in which a user inputs their problem into a dedicated website, and a generating AI understands the problem and generates a specific solution. The problem-solving system is characterized by matching the user with an expert who possesses the generated solution. First, the user inputs their problem into the dedicated website. This problem can be of any kind, such as business, relationships, or health. Next, the problem-solving system's generating AI understands the problem and generates a specific solution. For example, if the problem is business, the problem-solving system conducts market analysis and competitor analysis and proposes a specific strategy. If the problem is relationships, the problem-solving system provides methods for improving communication and specific action plans for repairing relationships. If the problem is health, the problem-solving system provides suggestions for improving lifestyle habits and introduces appropriate medical institutions. Furthermore, in order to match the user with an expert who possesses the generated solution, the generating AI searches a database of experts and identifies the most suitable expert. For example, if the problem is legal, the problem-solving system searches for a legal expert and introduces them to the user. If the problem is tax, the problem-solving system searches for a tax accountant and introduces them to the user. If the problem is IT, the problem-solving system searches for an IT consultant and introduces them to the user. This service is a new business model that leverages the characteristics of modern society, such as advancements in generative AI technology, the spread of remote work, and the age of information overload, creating value in both solving users' problems and utilizing the skills of experts. Users can obtain concrete action plans to solve their problems, and experts can help others by utilizing their skills and knowledge. This makes it a beneficial service for both users and experts. As a result, the problem-solving system can efficiently receive, analyze, generate solutions for, and match users with experts regarding their problems.

[0029] The problem-solving system according to this embodiment comprises a reception unit, a generation unit, a search unit, and a matching unit. The reception unit receives the user's problems. User problems include, for example, business problems, health problems, relationship problems, etc., but are not limited to these examples. The reception unit allows users to input their problems, for example, on a dedicated website. The reception unit also allows users to input their problems using voice input. The generation unit uses a generation AI to analyze the problems received by the reception unit and generate specific solutions. For example, for business problems, the generation unit conducts market analysis and competitor analysis and proposes specific strategies. The generation unit can also provide methods for improving communication and specific action plans for repairing relationships for relationship problems. Furthermore, the generation unit can also provide suggestions for improving lifestyle habits and refer users to appropriate medical institutions for health problems. The search unit searches for experts who have solutions generated by the generation unit. The search unit, for example, searches a database of experts and identifies the most suitable expert. For example, for legal problems, the search unit searches for legal experts and introduces them to the user. Furthermore, the search unit can search for tax accountants to address tax-related concerns and introduce them to users. The search unit can also search for IT consultants to address IT-related concerns and introduce them to users. The matching unit matches users with the experts found by the search unit. The matching unit matches users with experts through means such as online chat or video calls. For example, the matching unit can match users with experts via online chat, enabling real-time consultations. The matching unit can also match users with experts using video calls, allowing for face-to-face consultations. Furthermore, the matching unit can match users with experts using email or messaging apps, enabling asynchronous consultations. As a result, the problem-solving system according to this embodiment can efficiently receive, analyze, generate solutions for, and match users with experts to address their concerns.

[0030] The reception desk receives users' concerns. These concerns include, but are not limited to, business problems, health problems, and relationship problems. Users can enter their concerns through a dedicated website, for example. They can also enter their concerns using voice input. Specifically, this can be done by entering text into a form on the website or by speaking into a microphone using speech recognition technology. In the case of voice input, the speech recognition engine converts the user's speech into text data and sends it to the system. Furthermore, users can also enter their concerns through a smartphone app, and a chatbot within the app can receive the user's concerns in a conversational format. This allows users to easily communicate their concerns to the system, and the reception desk can improve user convenience by providing diverse input methods.

[0031] The generation unit uses a generation AI to analyze problems received by the reception unit and generate concrete solutions. For example, for business problems, the generation unit conducts market and competitor analysis and proposes specific strategies. It can also provide methods for improving communication and concrete action plans for repairing relationships for relationship problems. Furthermore, for health problems, the generation unit can suggest lifestyle improvements and refer users to appropriate medical institutions. Specifically, the generation AI analyzes the user's problems using natural language processing technology to understand their content. For example, for business problems, the generation AI collects market and competitor information, performs data analysis, and proposes the optimal business strategy. For relationship problems, the generation AI uses a psychological approach to generate methods for improving communication and concrete action plans for repairing relationships. For health problems, the generation AI analyzes the user's lifestyle data and health status to suggest lifestyle improvements and refer users to appropriate medical institutions. Based on these analysis results, the generation AI can provide users with concrete and actionable solutions. This allows the generation unit to quickly and accurately generate solutions to users' problems and support them in solving their issues.

[0032] The search unit searches for experts who possess the solutions generated by the generation unit. For example, the search unit searches a database of experts to identify the most suitable expert. For example, the search unit can search for legal experts for legal concerns and introduce them to the user. It can also search for tax accountants for tax concerns and introduce them to the user. Furthermore, the search unit can search for IT consultants for IT concerns and introduce them to the user. Specifically, the search unit accesses a database of experts and identifies the most suitable expert based on information such as the expert's profile, past performance, and evaluations. Based on the content of the user's concerns and the solutions generated by the generation unit, the search unit evaluates the expert's skill set and experience to make the best match. For example, for legal concerns, the search unit searches for lawyers specializing in a specific field from among legal experts and introduces them to the user. For tax concerns, it searches for experts specializing in a specific tax field from among tax accountants and introduces them to the user. For IT concerns, it searches for experts specializing in a specific technology or industry from among IT consultants and introduces them to the user. This allows the search unit to quickly find the most suitable expert for the user's problem and introduce them to the user.

[0033] The matching unit matches users with experts found by the search unit. The matching unit matches users with experts through means such as online chat or video calls. For example, the matching unit can match users with experts via online chat, allowing for real-time consultations. The matching unit can also match users with experts using video calls, enabling face-to-face consultations. Furthermore, the matching unit can match users with experts using email or messaging apps, enabling asynchronous consultations. Specifically, the matching unit coordinates the schedules of users and experts to set the optimal consultation time. In the case of online chat, users and experts can exchange messages in real time, enabling quick consultations toward problem solving. In the case of video calls, users and experts can communicate more deeply by consulting while seeing each other's faces. In asynchronous consultations using email or messaging apps, users and experts can exchange messages at their own convenience without time constraints. In this way, the matching unit can provide the optimal consultation method according to the user's needs and achieve effective matching between users and experts. Furthermore, the matching unit can improve user satisfaction by monitoring the progress of consultations and providing follow-up as needed.

[0034] The generation unit can understand problems and generate concrete solutions using a generation AI. For example, the generation unit can use the generation AI to analyze the user's problem as text and understand its content. For example, the generation unit can have the generation AI receive a prompt such as "Please suggest a solution to this problem" and generate a concrete solution based on the content of the problem. The generation unit can also use the generation AI to emotionally analyze the user's problem and understand the user's emotional state. For example, the generation unit can have the generation AI analyze the text data of the user's problem and calculate an emotional score. Furthermore, the generation unit can use the generation AI to data mine the user's problem and refer to solutions for similar problems from the past. For example, the generation unit can have the generation AI search a past database, extract solutions for similar problems, and generate a new solution based on them. In this way, by using the generation AI, the understanding of problems and the generation of solutions can be performed with high accuracy. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can input the text data of the user's problem into the generation AI, and the generation AI can generate a solution.

[0035] The search unit can search a database of experts and identify the most suitable expert. For example, the search unit can search the database of experts and identify the most suitable expert based on criteria such as the degree of match in the field of expertise, past performance, and evaluations. The search unit can also search the database of experts and identify the most suitable expert based on the degree of match in the field of expertise. Furthermore, the search unit can also search the database of experts and identify the most suitable expert based on past performance. In addition, the search unit can search the database of experts and identify the most suitable expert based on evaluations. Thus, the most suitable expert can be identified by searching the database of experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the database of experts into AI, and the AI ​​can identify the most suitable expert.

[0036] The matching unit can match users with experts through means such as online chat or video calls. For example, the matching unit can match users with experts using online chat and conduct consultations in real time. For example, the matching unit can also match users with experts using video calls and conduct consultations while seeing each other's faces. Furthermore, the matching unit can match users with experts using email or messaging apps and conduct consultations asynchronously. This allows for smooth matching of users with experts by using online chat or video calls. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user and expert data into AI, which can then suggest the optimal matching method.

[0037] The generation unit can perform market and competitor analysis to address business challenges and propose concrete strategies. For example, the generation unit can use generation AI to perform market analysis and understand the current state of the business. For example, the generation unit's generation AI can analyze market data to grasp trends and competitor movements. The generation unit can also use generation AI to perform competitor analysis and evaluate the strengths and weaknesses of competitors. For example, the generation unit's generation AI can analyze competitor data and extract their strengths and weaknesses. Furthermore, the generation unit can use generation AI to propose concrete strategies. For example, the generation unit's generation AI can propose concrete business strategies based on the results of market and competitor analysis. In this way, by proposing concrete strategies to address business challenges, the user's business problems can be solved. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may not be performed using generation AI. For example, the generation unit can input market data and competitor data into the generation AI, and the generation AI can propose concrete strategies.

[0038] The generation unit can provide methods for improving communication and concrete action plans for repairing relationships in response to relationship problems. For example, the generation unit can use a generation AI to suggest methods for improving communication. For example, the generation unit's generation AI analyzes the user's problems and suggests improvement methods such as active listening and methods for providing feedback. The generation unit can also use a generation AI to provide concrete action plans for repairing relationships. For example, the generation unit's generation AI analyzes the user's problems and suggests action plans such as setting up opportunities for dialogue and problem-solving steps. In this way, by providing concrete action plans for relationship problems, the user's relationship challenges can be resolved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input text data of the user's problems into a generation AI, and the generation AI can generate a concrete action plan.

[0039] The generation unit can provide lifestyle improvements and referrals to appropriate medical institutions in response to health concerns. For example, the generation unit can use a generation AI to suggest methods for improving lifestyle habits. For example, the generation AI analyzes the user's concerns and suggests improvement methods such as dietary improvements, exercise recommendations, and improvements in sleep quality. The generation unit can also use the generation AI to refer users to appropriate medical institutions. For example, the generation AI analyzes the user's concerns and refers users to appropriate medical institutions based on criteria such as the degree of matching of specialty, evaluation of the medical institution, and ease of access. In this way, the user's health problems can be solved by providing lifestyle improvements and referrals to appropriate medical institutions in response to health concerns. Some or all of the above processing in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input the user's health data into a generation AI, which can then suggest lifestyle improvements and appropriate medical institutions.

[0040] The reception desk can analyze the user's past problem history and select the most suitable reception method. For example, the reception desk can automatically display categories of problems that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest categories of problems that the user will use at a specific time of day based on their past problem history. In this way, the reception desk can select the most suitable reception method by analyzing the past problem history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past problem history data into AI, and the AI ​​can select the most suitable reception method.

[0041] The reception desk can filter the submitted inquiries based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception desk can automatically display relevant categories of inquiries. For example, the reception desk can prioritize suggesting relevant categories of inquiries based on the user's areas of interest. The reception desk can also filter and display categories of inquiries related to a specific situation if the user is in that situation. This allows for priority reception of highly relevant inquiries by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current situation and areas of interest into an AI, which can then perform the filtering.

[0042] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inquiries related to that region. If the user is traveling, the reception desk will prioritize inquiries related to the travel destination. The reception desk can also prioritize inquiries related to an event if the user is participating in a specific event. In this way, by considering the user's geographical location, highly relevant inquiries can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant inquiries.

[0043] The reception desk can analyze a user's social media activity when a problem is received and accept relevant problems. For example, the reception desk may prioritize problems related to topics that the user frequently mentions on social media. For example, the reception desk may prioritize problems related to accounts that the user follows on social media. The reception desk may also prioritize problems related to groups that the user participates in on social media. In this way, by analyzing the user's social media activity, relevant problems can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into an AI, which can then accept relevant problems.

[0044] The generation unit can adjust the level of detail in a solution based on the importance of the problem when generating a solution. For example, the generation unit generates a detailed solution for a high-importance problem. For example, the generation unit generates a concise solution for a low-importance problem. The generation unit can also adjust the number of steps in the solution according to its importance. This allows for the provision of appropriate solutions by adjusting the level of detail in the solution based on the importance of the problem. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance data of the user's problem into a generation AI, which can then adjust the level of detail in the solution.

[0045] The generation unit can apply different generation algorithms depending on the category of the problem when generating solutions. For example, the generation unit can apply a business analysis algorithm to business problems. For example, it can apply a psychological approach to relationship problems. Furthermore, it can apply an algorithm based on medical data to health problems. By applying different generation algorithms depending on the category of the problem, it is possible to provide more appropriate solutions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's problem category data into a generation AI, and the generation AI can apply an appropriate generation algorithm.

[0046] The generation unit can determine the priority of solutions based on when the problem was submitted when generating solutions. For example, the generation unit may prioritize solving recently submitted problems. For example, the generation unit may prioritize solving problems that have been left unresolved for a long time. The generation unit can also adjust the order in which solutions are generated according to the submission date. This allows solutions to be provided in an appropriate order by determining the priority of solutions based on when the problem was submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user problem submission date data into the generation AI, and the generation AI can determine the priority of solutions.

[0047] The generation unit can adjust the order of solutions based on the relevance of the problems when generating solutions. For example, the generation unit will prioritize solving highly relevant problems. For example, the generation unit will postpone solving less relevant problems. The generation unit can also adjust the order of solution generation according to relevance. This allows for the priority provision of highly relevant solutions by adjusting the order of solutions based on the relevance of the problems. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance data of the user's problems into a generation AI, and the generation AI can adjust the order of solutions.

[0048] The search unit can improve the accuracy of its expert searches by considering the relationships between experts. For example, the search unit can search for the most suitable expert by considering their collaborative relationships. For example, the search unit can search for highly relevant experts based on their past collaborative projects. The search unit can also search for highly reliable experts based on their mutual evaluations. This improves the accuracy of the search by considering the relationships between experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert relationship data into AI, which can then improve the accuracy of the search.

[0049] The search unit can perform searches while considering the attribute information of experts. For example, the search unit can search for the most suitable expert based on the expert's field of expertise. For example, the search unit can search for a highly reliable expert based on the expert's years of experience. The search unit can also search for highly relevant experts based on the expert's past achievements. In this way, by considering the attribute information of experts, a more appropriate expert can be found. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert attribute information data into AI, and the AI ​​can perform the search.

[0050] The search unit can perform expert searches while considering the geographical distribution of experts. For example, the search unit can prioritize searching for experts who are near the user. For example, the search unit can search for experts specializing in a particular region. The search unit can also search for experts who are geographically accessible. This allows for the search of more appropriate experts by considering the geographical distribution of experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert geographical distribution data into AI, and the AI ​​can perform the search.

[0051] The search unit can improve the accuracy of its searches by referencing relevant literature of experts during expert searches. For example, the search unit can search for highly relevant experts based on papers written by experts. For example, the search unit can search for reliable experts based on literature cited by experts. The search unit can also search for highly relevant experts based on conference presentations attended by experts. This improves the accuracy of searches by referencing relevant literature of experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert-related literature data into AI, which can then improve the accuracy of the search.

[0052] The matching unit can select the optimal matching method by referring to the past matching history of the user and the expert during the matching process. For example, the matching unit may rematch the user with an expert to whom the user has previously given a good rating. For example, the matching unit may match the user with a relevant expert based on the field of expertise the user has previously used. The matching unit can also avoid experts to whom the user has been dissatisfied in the past and match them with new experts. In this way, the optimal matching method can be selected by referring to past matching history. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching history data of the user and the expert into the AI, which can then select the optimal matching method.

[0053] The matching unit can customize the matching method based on the attribute information of the user and the expert during the matching process. For example, the matching unit can match a suitable expert based on the user's age and gender. For example, the matching unit can match a relevant expert based on the user's occupation and interests. The matching unit can also match a suitable expert based on the user's language and cultural background. By customizing the matching method based on the attribute information of the user and the expert, more appropriate matching becomes possible. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user and expert attribute information data into the AI, which can then customize the matching method.

[0054] The matching unit can select the optimal matching method by considering the geographical location information of the user and the expert during the matching process. For example, the matching unit may prioritize matching with experts who are near the user. For example, the matching unit may match with experts who specialize in a particular region. The matching unit can also match with experts who are geographically accessible. This allows for more appropriate matching by considering the geographical location information of the user and the expert. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the geographical location data of the user and the expert into the AI, which can then select the optimal matching method.

[0055] The matching unit can analyze the social media activity of users and experts during the matching process and propose matching methods. For example, the matching unit can match users with experts related to topics they frequently mention on social media. For example, the matching unit can match users with experts related to accounts they follow on social media. The matching unit can also match users with experts related to groups they participate in on social media. This allows the system to propose more appropriate matching methods by analyzing the social media activity of users and experts. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user and expert social media activity data into an AI, which can then propose matching methods.

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

[0057] The reception desk can refer to the user's past problem history when receiving a user's problem and suggest the most suitable reception method. For example, it can automatically display categories of problems the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest problem categories to be used during specific time periods based on the user's past problem history. In this way, the optimal reception method can be selected by analyzing past problem history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past problem history data into AI, and the AI ​​can select the most suitable reception method.

[0058] The search unit can improve the accuracy of expert searches by considering the relationships between experts. For example, it can search for the most suitable expert by considering their collaborative relationships. It can also search for highly relevant experts based on their past collaborative projects. Furthermore, it can search for highly reliable experts based on their mutual evaluations. This improves the accuracy of searches by considering the relationships between experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert relationship data into AI, which can then improve the accuracy of the search.

[0059] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, inquiries related to that region will be prioritized. If the user is traveling, inquiries related to their travel destination will be prioritized. Furthermore, if the user is participating in a specific event, inquiries related to that event can also be prioritized. In this way, by considering the user's geographical location, highly relevant inquiries can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant inquiries.

[0060] The generation unit can adjust the level of detail in a solution based on the importance of the problem when generating a solution. For example, it can generate a detailed solution for high-importance problems and a concise solution for low-importance problems. It can also adjust the number of steps in the solution according to its importance. This allows for the provision of appropriate solutions by adjusting the level of detail based on the importance of the problem. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance data of the user's problem into a generation AI, which can then adjust the level of detail in the solution.

[0061] The matching unit can select the optimal matching method by referring to the past matching history of the user and the expert during the matching process. For example, it can rematch the user with an expert to whom it previously gave a good rating. It can also match the user with relevant experts based on the fields of expertise the user has used in the past. Furthermore, it can avoid experts to whom the user has been dissatisfied in the past and match them with new experts. In this way, the optimal matching method can be selected by referring to past matching history. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching history data of the user and the expert into the AI, which can then select the optimal matching method.

[0062] The search unit can perform expert searches while considering the geographical distribution of experts. For example, it can prioritize searching for experts near the user, search for experts specializing in a specific region, or search for experts who are geographically accessible. This allows for the search of more appropriate experts by considering their geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert geographical distribution data into AI, which can then perform the search.

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

[0064] Step 1: The reception desk receives user inquiries. These inquiries can include business problems, health problems, relationship problems, and more. Users can enter their inquiries through a dedicated website. They can also enter their inquiries using voice input. Step 2: The generation unit uses generation AI to analyze the problems received by the reception unit and generate specific solutions. For example, for business problems, it conducts market and competitor analysis and proposes specific strategies. For relationship problems, it provides methods for improving communication and specific action plans for repairing relationships. Furthermore, for health problems, it can provide suggestions for improving lifestyle habits and referrals to appropriate medical institutions. Step 3: The search unit searches for experts who possess the solutions generated by the generation unit. For example, it searches a database of experts to identify the most suitable one. It can search for legal experts for legal concerns and introduce them to the user. It can also search for tax accountants for tax concerns and introduce them to the user. Furthermore, it can search for IT consultants for IT concerns and introduce them to the user. Step 4: The matching unit matches users with experts found by the search unit. For example, it matches users with experts through means such as online chat or video calls. Users and experts can be matched via online chat and consult in real time. Users and experts can also be matched using video calls and consult while seeing each other's faces. Furthermore, users and experts can be matched using email or messaging apps and consult asynchronously.

[0065] (Example of form 2) The problem-solving system according to an embodiment of the present invention is a system in which a user inputs their problem into a dedicated website, and a generating AI understands the problem and generates a specific solution. The problem-solving system is characterized by matching the user with an expert who possesses the generated solution. First, the user inputs their problem into the dedicated website. This problem can be of any kind, such as business, relationships, or health. Next, the problem-solving system's generating AI understands the problem and generates a specific solution. For example, if the problem is business, the problem-solving system conducts market analysis and competitor analysis and proposes a specific strategy. If the problem is relationships, the problem-solving system provides methods for improving communication and specific action plans for repairing relationships. If the problem is health, the problem-solving system provides suggestions for improving lifestyle habits and introduces appropriate medical institutions. Furthermore, in order to match the user with an expert who possesses the generated solution, the generating AI searches a database of experts and identifies the most suitable expert. For example, if the problem is legal, the problem-solving system searches for a legal expert and introduces them to the user. If the problem is tax, the problem-solving system searches for a tax accountant and introduces them to the user. If the problem is IT, the problem-solving system searches for an IT consultant and introduces them to the user. This service is a new business model that leverages the characteristics of modern society, such as advancements in generative AI technology, the spread of remote work, and the age of information overload, creating value in both solving users' problems and utilizing the skills of experts. Users can obtain concrete action plans to solve their problems, and experts can help others by utilizing their skills and knowledge. This makes it a beneficial service for both users and experts. As a result, the problem-solving system can efficiently receive, analyze, generate solutions for, and match users with experts regarding their problems.

[0066] The problem-solving system according to this embodiment comprises a reception unit, a generation unit, a search unit, and a matching unit. The reception unit receives the user's problems. User problems include, for example, business problems, health problems, relationship problems, etc., but are not limited to these examples. The reception unit allows users to input their problems, for example, on a dedicated website. The reception unit also allows users to input their problems using voice input. The generation unit uses a generation AI to analyze the problems received by the reception unit and generate specific solutions. For example, for business problems, the generation unit conducts market analysis and competitor analysis and proposes specific strategies. The generation unit can also provide methods for improving communication and specific action plans for repairing relationships for relationship problems. Furthermore, the generation unit can also provide suggestions for improving lifestyle habits and refer users to appropriate medical institutions for health problems. The search unit searches for experts who have solutions generated by the generation unit. The search unit, for example, searches a database of experts and identifies the most suitable expert. For example, for legal problems, the search unit searches for legal experts and introduces them to the user. Furthermore, the search unit can search for tax accountants to address tax-related concerns and introduce them to users. The search unit can also search for IT consultants to address IT-related concerns and introduce them to users. The matching unit matches users with the experts found by the search unit. The matching unit matches users with experts through means such as online chat or video calls. For example, the matching unit can match users with experts via online chat, enabling real-time consultations. The matching unit can also match users with experts using video calls, allowing for face-to-face consultations. Furthermore, the matching unit can match users with experts using email or messaging apps, enabling asynchronous consultations. As a result, the problem-solving system according to this embodiment can efficiently receive, analyze, generate solutions for, and match users with experts to address their concerns.

[0067] The reception desk receives users' concerns. These concerns include, but are not limited to, business problems, health problems, and relationship problems. Users can enter their concerns through a dedicated website, for example. They can also enter their concerns using voice input. Specifically, this can be done by entering text into a form on the website or by speaking into a microphone using speech recognition technology. In the case of voice input, the speech recognition engine converts the user's speech into text data and sends it to the system. Furthermore, users can also enter their concerns through a smartphone app, and a chatbot within the app can receive the user's concerns in a conversational format. This allows users to easily communicate their concerns to the system, and the reception desk can improve user convenience by providing diverse input methods.

[0068] The generation unit uses a generation AI to analyze problems received by the reception unit and generate concrete solutions. For example, for business problems, the generation unit conducts market and competitor analysis and proposes specific strategies. It can also provide methods for improving communication and concrete action plans for repairing relationships for relationship problems. Furthermore, for health problems, the generation unit can suggest lifestyle improvements and refer users to appropriate medical institutions. Specifically, the generation AI analyzes the user's problems using natural language processing technology to understand their content. For example, for business problems, the generation AI collects market and competitor information, performs data analysis, and proposes the optimal business strategy. For relationship problems, the generation AI uses a psychological approach to generate methods for improving communication and concrete action plans for repairing relationships. For health problems, the generation AI analyzes the user's lifestyle data and health status to suggest lifestyle improvements and refer users to appropriate medical institutions. Based on these analysis results, the generation AI can provide users with concrete and actionable solutions. This allows the generation unit to quickly and accurately generate solutions to users' problems and support them in solving their issues.

[0069] The search unit searches for experts who possess the solutions generated by the generation unit. For example, the search unit searches a database of experts to identify the most suitable expert. For example, the search unit can search for legal experts for legal concerns and introduce them to the user. It can also search for tax accountants for tax concerns and introduce them to the user. Furthermore, the search unit can search for IT consultants for IT concerns and introduce them to the user. Specifically, the search unit accesses a database of experts and identifies the most suitable expert based on information such as the expert's profile, past performance, and evaluations. Based on the content of the user's concerns and the solutions generated by the generation unit, the search unit evaluates the expert's skill set and experience to make the best match. For example, for legal concerns, the search unit searches for lawyers specializing in a specific field from among legal experts and introduces them to the user. For tax concerns, it searches for experts specializing in a specific tax field from among tax accountants and introduces them to the user. For IT concerns, it searches for experts specializing in a specific technology or industry from among IT consultants and introduces them to the user. This allows the search unit to quickly find the most suitable expert for the user's problem and introduce them to the user.

[0070] The matching unit matches users with experts found by the search unit. The matching unit matches users with experts through means such as online chat or video calls. For example, the matching unit can match users with experts via online chat, allowing for real-time consultations. The matching unit can also match users with experts using video calls, enabling face-to-face consultations. Furthermore, the matching unit can match users with experts using email or messaging apps, enabling asynchronous consultations. Specifically, the matching unit coordinates the schedules of users and experts to set the optimal consultation time. In the case of online chat, users and experts can exchange messages in real time, enabling quick consultations toward problem solving. In the case of video calls, users and experts can communicate more deeply by consulting while seeing each other's faces. In asynchronous consultations using email or messaging apps, users and experts can exchange messages at their own convenience without time constraints. In this way, the matching unit can provide the optimal consultation method according to the user's needs and achieve effective matching between users and experts. Furthermore, the matching unit can improve user satisfaction by monitoring the progress of consultations and providing follow-up as needed.

[0071] The generation unit can understand problems and generate concrete solutions using a generation AI. For example, the generation unit can use the generation AI to analyze the user's problem as text and understand its content. For example, the generation unit can have the generation AI receive a prompt such as "Please suggest a solution to this problem" and generate a concrete solution based on the content of the problem. The generation unit can also use the generation AI to emotionally analyze the user's problem and understand the user's emotional state. For example, the generation unit can have the generation AI analyze the text data of the user's problem and calculate an emotional score. Furthermore, the generation unit can use the generation AI to data mine the user's problem and refer to solutions for similar problems from the past. For example, the generation unit can have the generation AI search a past database, extract solutions for similar problems, and generate a new solution based on them. In this way, by using the generation AI, the understanding of problems and the generation of solutions can be performed with high accuracy. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can input the text data of the user's problem into the generation AI, and the generation AI can generate a solution.

[0072] The search unit can search a database of experts and identify the most suitable expert. For example, the search unit can search the database of experts and identify the most suitable expert based on criteria such as the degree of match in the field of expertise, past performance, and evaluations. The search unit can also search the database of experts and identify the most suitable expert based on the degree of match in the field of expertise. Furthermore, the search unit can also search the database of experts and identify the most suitable expert based on past performance. In addition, the search unit can search the database of experts and identify the most suitable expert based on evaluations. Thus, the most suitable expert can be identified by searching the database of experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the database of experts into AI, and the AI ​​can identify the most suitable expert.

[0073] The matching unit can match users with experts through means such as online chat or video calls. For example, the matching unit can match users with experts using online chat and conduct consultations in real time. For example, the matching unit can also match users with experts using video calls and conduct consultations while seeing each other's faces. Furthermore, the matching unit can match users with experts using email or messaging apps and conduct consultations asynchronously. This allows for smooth matching of users with experts by using online chat or video calls. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user and expert data into AI, which can then suggest the optimal matching method.

[0074] The generation unit can perform market and competitor analysis to address business challenges and propose concrete strategies. For example, the generation unit can use generation AI to perform market analysis and understand the current state of the business. For example, the generation unit's generation AI can analyze market data to grasp trends and competitor movements. The generation unit can also use generation AI to perform competitor analysis and evaluate the strengths and weaknesses of competitors. For example, the generation unit's generation AI can analyze competitor data and extract their strengths and weaknesses. Furthermore, the generation unit can use generation AI to propose concrete strategies. For example, the generation unit's generation AI can propose concrete business strategies based on the results of market and competitor analysis. In this way, by proposing concrete strategies to address business challenges, the user's business problems can be solved. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may not be performed using generation AI. For example, the generation unit can input market data and competitor data into the generation AI, and the generation AI can propose concrete strategies.

[0075] The generation unit can provide methods for improving communication and concrete action plans for repairing relationships in response to relationship problems. For example, the generation unit can use a generation AI to suggest methods for improving communication. For example, the generation unit's generation AI analyzes the user's problems and suggests improvement methods such as active listening and methods for providing feedback. The generation unit can also use a generation AI to provide concrete action plans for repairing relationships. For example, the generation unit's generation AI analyzes the user's problems and suggests action plans such as setting up opportunities for dialogue and problem-solving steps. In this way, by providing concrete action plans for relationship problems, the user's relationship challenges can be resolved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input text data of the user's problems into a generation AI, and the generation AI can generate a concrete action plan.

[0076] The generation unit can provide lifestyle improvements and referrals to appropriate medical institutions in response to health concerns. For example, the generation unit can use a generation AI to suggest methods for improving lifestyle habits. For example, the generation AI analyzes the user's concerns and suggests improvement methods such as dietary improvements, exercise recommendations, and improvements in sleep quality. The generation unit can also use the generation AI to refer users to appropriate medical institutions. For example, the generation AI analyzes the user's concerns and refers users to appropriate medical institutions based on criteria such as the degree of matching of specialty, evaluation of the medical institution, and ease of access. In this way, the user's health problems can be solved by providing lifestyle improvements and referrals to appropriate medical institutions in response to health concerns. Some or all of the above processing in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input the user's health data into a generation AI, which can then suggest lifestyle improvements and appropriate medical institutions.

[0077] The reception desk can estimate the user's emotions and adjust the way the user submits their concerns based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the user is in a hurry, allowing them to submit their concerns quickly. This allows for more appropriate submission by adjusting the way the user submits their concerns according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotion and adjust the submission method.

[0078] The reception desk can analyze the user's past problem history and select the most suitable reception method. For example, the reception desk can automatically display categories of problems that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest categories of problems that the user will use at a specific time of day based on their past problem history. In this way, the reception desk can select the most suitable reception method by analyzing the past problem history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past problem history data into AI, and the AI ​​can select the most suitable reception method.

[0079] The reception desk can filter the submitted inquiries based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception desk can automatically display relevant categories of inquiries. For example, the reception desk can prioritize suggesting relevant categories of inquiries based on the user's areas of interest. The reception desk can also filter and display categories of inquiries related to a specific situation if the user is in that situation. This allows for priority reception of highly relevant inquiries by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current situation and areas of interest into an AI, which can then perform the filtering.

[0080] The reception desk can estimate the user's emotions and determine the priority of the problems to be received based on the estimated emotions. For example, if the user has an urgent problem, the reception desk will prioritize receiving that problem. For example, if the user has a long-term problem, the reception desk will prioritize receiving that problem. The reception desk can also prioritize receiving problems if the user is in an emotionally unstable state. In this way, by prioritizing problems according to the user's emotions, urgent problems can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate emotions and determine the priority of problems.

[0081] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inquiries related to that region. If the user is traveling, the reception desk will prioritize inquiries related to the travel destination. The reception desk can also prioritize inquiries related to an event if the user is participating in a specific event. In this way, by considering the user's geographical location, highly relevant inquiries can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant inquiries.

[0082] The reception desk can analyze a user's social media activity when a problem is received and accept relevant problems. For example, the reception desk may prioritize problems related to topics that the user frequently mentions on social media. For example, the reception desk may prioritize problems related to accounts that the user follows on social media. The reception desk may also prioritize problems related to groups that the user participates in on social media. In this way, by analyzing the user's social media activity, relevant problems can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into an AI, which can then accept relevant problems.

[0083] The generation unit can estimate the user's emotions and adjust the way the solution is presented based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and easy-to-understand solution. If the user is relaxed, for example, the generation unit will generate a detailed solution. The generation unit can also generate a quickly actionable solution if the user is in a hurry. This allows for the provision of more appropriate solutions by adjusting the way the solution is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can then adjust the way the solution is presented.

[0084] The generation unit can adjust the level of detail in a solution based on the importance of the problem when generating a solution. For example, the generation unit generates a detailed solution for a high-importance problem. For example, the generation unit generates a concise solution for a low-importance problem. The generation unit can also adjust the number of steps in the solution according to its importance. This allows for the provision of appropriate solutions by adjusting the level of detail in the solution based on the importance of the problem. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance data of the user's problem into a generation AI, which can then adjust the level of detail in the solution.

[0085] The generation unit can apply different generation algorithms depending on the category of the problem when generating solutions. For example, the generation unit can apply a business analysis algorithm to business problems. For example, it can apply a psychological approach to relationship problems. Furthermore, it can apply an algorithm based on medical data to health problems. By applying different generation algorithms depending on the category of the problem, it is possible to provide more appropriate solutions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's problem category data into a generation AI, and the generation AI can apply an appropriate generation algorithm.

[0086] The generation unit can estimate the user's emotions and adjust the length of the solution based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise solution. If the user is relaxed, the generation unit will generate a longer solution with detailed explanations. The generation unit can also generate a solution with visually stimulating effects if the user is excited. By adjusting the length of the solution according to the user's emotions, a more appropriate solution can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can then adjust the length of the solution.

[0087] The generation unit can determine the priority of solutions based on when the problem was submitted when generating solutions. For example, the generation unit may prioritize solving recently submitted problems. For example, the generation unit may prioritize solving problems that have been left unresolved for a long time. The generation unit can also adjust the order in which solutions are generated according to the submission date. This allows solutions to be provided in an appropriate order by determining the priority of solutions based on when the problem was submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user problem submission date data into the generation AI, and the generation AI can determine the priority of solutions.

[0088] The generation unit can adjust the order of solutions based on the relevance of the problems when generating solutions. For example, the generation unit will prioritize solving highly relevant problems. For example, the generation unit will postpone solving less relevant problems. The generation unit can also adjust the order of solution generation according to relevance. This allows for the priority provision of highly relevant solutions by adjusting the order of solutions based on the relevance of the problems. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance data of the user's problems into a generation AI, and the generation AI can adjust the order of solutions.

[0089] The search unit can estimate the user's emotions and adjust the search criteria for experts based on the estimated emotions. For example, if the user is stressed, the search unit will prioritize searching for experts who can respond quickly. If the user is relaxed, the search unit will prioritize searching for experts who can provide detailed information. The search unit can also prioritize searching for experts who can respond immediately if the user is in a hurry. In this way, by adjusting the search criteria for experts according to the user's emotions, a more appropriate expert can be found. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI, and the generative AI can adjust the search criteria for experts.

[0090] The search unit can improve the accuracy of its expert searches by considering the relationships between experts. For example, the search unit can search for the most suitable expert by considering their collaborative relationships. For example, the search unit can search for highly relevant experts based on their past collaborative projects. The search unit can also search for highly reliable experts based on their mutual evaluations. This improves the accuracy of the search by considering the relationships between experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert relationship data into AI, which can then improve the accuracy of the search.

[0091] The search unit can perform searches while considering the attribute information of experts. For example, the search unit can search for the most suitable expert based on the expert's field of expertise. For example, the search unit can search for a highly reliable expert based on the expert's years of experience. The search unit can also search for highly relevant experts based on the expert's past achievements. In this way, by considering the attribute information of experts, a more appropriate expert can be found. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert attribute information data into AI, and the AI ​​can perform the search.

[0092] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user has an urgent problem, the search unit will display experts who can respond quickly at the top of the results. If the user is relaxed, the search unit will display experts who can provide detailed information at the top of the results. The search unit can also display experts who can respond immediately at the top of the results if the user is in a hurry. In this way, by adjusting the display order of search results according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI, and the generative AI can adjust the display order of search results.

[0093] The search unit can perform expert searches while considering the geographical distribution of experts. For example, the search unit can prioritize searching for experts who are near the user. For example, the search unit can search for experts specializing in a particular region. The search unit can also search for experts who are geographically accessible. This allows for the search of more appropriate experts by considering the geographical distribution of experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert geographical distribution data into AI, and the AI ​​can perform the search.

[0094] The search unit can improve the accuracy of its searches by referencing relevant literature of experts during expert searches. For example, the search unit can search for highly relevant experts based on papers written by experts. For example, the search unit can search for reliable experts based on literature cited by experts. The search unit can also search for highly relevant experts based on conference presentations attended by experts. This improves the accuracy of searches by referencing relevant literature of experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert-related literature data into AI, which can then improve the accuracy of the search.

[0095] The matching unit can estimate the user's emotions and adjust the matching method based on the estimated emotions. For example, if the user is stressed, the matching unit will match them with a specialist who can respond quickly. For example, if the user is relaxed, the matching unit will match them with a specialist who can provide detailed information. The matching unit can also match the user with a specialist who can respond immediately if the user is in a hurry. This allows for more appropriate matching by adjusting the matching method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's emotion data into the generative AI, which can then adjust the matching method.

[0096] The matching unit can select the optimal matching method by referring to the past matching history of the user and the expert during the matching process. For example, the matching unit may rematch the user with an expert to whom the user has previously given a good rating. For example, the matching unit may match the user with a relevant expert based on the field of expertise the user has previously used. The matching unit can also avoid experts to whom the user has been dissatisfied in the past and match them with new experts. In this way, the optimal matching method can be selected by referring to past matching history. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching history data of the user and the expert into the AI, which can then select the optimal matching method.

[0097] The matching unit can customize the matching method based on the attribute information of the user and the expert during the matching process. For example, the matching unit can match a suitable expert based on the user's age and gender. For example, the matching unit can match a relevant expert based on the user's occupation and interests. The matching unit can also match a suitable expert based on the user's language and cultural background. By customizing the matching method based on the attribute information of the user and the expert, more appropriate matching becomes possible. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user and expert attribute information data into the AI, which can then customize the matching method.

[0098] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated emotions. For example, if the user has an urgent problem, the matching unit will prioritize matching that problem. For example, if the user has a long-term problem, the matching unit will prioritize matching that problem. The matching unit can also prioritize matching if the user is in an emotionally unstable state. In this way, by determining matching priorities according to the user's emotions, urgent problems can be matched preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user emotion data into a generative AI, and the generative AI can determine matching priorities.

[0099] The matching unit can select the optimal matching method by considering the geographical location information of the user and the expert during the matching process. For example, the matching unit may prioritize matching with experts who are near the user. For example, the matching unit may match with experts who specialize in a particular region. The matching unit can also match with experts who are geographically accessible. This allows for more appropriate matching by considering the geographical location information of the user and the expert. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the geographical location data of the user and the expert into the AI, which can then select the optimal matching method.

[0100] The matching unit can analyze the social media activity of users and experts during the matching process and propose matching methods. For example, the matching unit can match users with experts related to topics they frequently mention on social media. For example, the matching unit can match users with experts related to accounts they follow on social media. The matching unit can also match users with experts related to groups they participate in on social media. This allows the system to propose more appropriate matching methods by analyzing the social media activity of users and experts. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user and expert social media activity data into an AI, which can then propose matching methods.

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

[0102] The reception desk can refer to the user's past problem history when receiving a user's problem and suggest the most suitable reception method. For example, it can automatically display categories of problems the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest problem categories to be used during specific time periods based on the user's past problem history. In this way, the optimal reception method can be selected by analyzing past problem history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past problem history data into AI, and the AI ​​can select the most suitable reception method.

[0103] The generation unit can estimate the user's emotions and adjust the way solutions are presented based on the estimated emotions. For example, if the user is stressed, it can generate a simple and easy-to-understand solution. If the user is relaxed, it can generate a detailed solution. If the user is in a hurry, it can also generate a solution that can be quickly implemented. This allows for the provision of more appropriate solutions by adjusting the way solutions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can then adjust the way solutions are presented.

[0104] The search unit can improve the accuracy of expert searches by considering the relationships between experts. For example, it can search for the most suitable expert by considering their collaborative relationships. It can also search for highly relevant experts based on their past collaborative projects. Furthermore, it can search for highly reliable experts based on their mutual evaluations. This improves the accuracy of searches by considering the relationships between experts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert relationship data into AI, which can then improve the accuracy of the search.

[0105] The matching unit can estimate the user's emotions and adjust the matching method based on the estimated emotions. For example, if the user is stressed, it can match them with a specialist who can respond quickly. If the user is relaxed, it can match them with a specialist who can provide detailed information. If the user is in a hurry, it can also match them with a specialist who can respond immediately. By adjusting the matching method according to the user's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, or not using AI. For example, the matching unit can input user emotion data into the generative AI, which can then adjust the matching method.

[0106] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, inquiries related to that region will be prioritized. If the user is traveling, inquiries related to their travel destination will be prioritized. Furthermore, if the user is participating in a specific event, inquiries related to that event can also be prioritized. In this way, by considering the user's geographical location, highly relevant inquiries can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant inquiries.

[0107] The generation unit can adjust the level of detail in a solution based on the importance of the problem when generating a solution. For example, it can generate a detailed solution for high-importance problems and a concise solution for low-importance problems. It can also adjust the number of steps in the solution according to its importance. This allows for the provision of appropriate solutions by adjusting the level of detail based on the importance of the problem. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance data of the user's problem into a generation AI, which can then adjust the level of detail in the solution.

[0108] The search unit can estimate the user's emotions and adjust the search criteria for experts based on the estimated emotions. For example, if the user is stressed, it can prioritize searching for experts who can respond quickly. If the user is relaxed, it can prioritize searching for experts who can provide detailed information. If the user is in a hurry, it can also prioritize searching for experts who can respond immediately. In this way, by adjusting the search criteria for experts according to the user's emotions, a more appropriate expert can be found. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI, and the generative AI can adjust the search criteria for experts.

[0109] The matching unit can select the optimal matching method by referring to the past matching history of the user and the expert during the matching process. For example, it can rematch the user with an expert to whom it previously gave a good rating. It can also match the user with relevant experts based on the fields of expertise the user has used in the past. Furthermore, it can avoid experts to whom the user has been dissatisfied in the past and match them with new experts. In this way, the optimal matching method can be selected by referring to past matching history. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching history data of the user and the expert into the AI, which can then select the optimal matching method.

[0110] The generation unit can estimate the user's emotions and adjust the length of the solution based on the estimated emotions. For example, if the user is in a hurry, it can generate a short, concise solution. If the user is relaxed, it can generate a longer solution with detailed explanations. If the user is excited, it can also generate a solution with visually stimulating effects. By adjusting the length of the solution according to the user's emotions, a more appropriate solution can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can then adjust the length of the solution.

[0111] The search unit can perform expert searches while considering the geographical distribution of experts. For example, it can prioritize searching for experts near the user, search for experts specializing in a specific region, or search for experts who are geographically accessible. This allows for the search of more appropriate experts by considering their geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input expert geographical distribution data into AI, which can then perform the search.

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

[0113] Step 1: The reception desk receives user inquiries. These inquiries can include business problems, health problems, relationship problems, and more. Users can enter their inquiries through a dedicated website. They can also enter their inquiries using voice input. Step 2: The generation unit uses generation AI to analyze the problems received by the reception unit and generate specific solutions. For example, for business problems, it conducts market and competitor analysis and proposes specific strategies. For relationship problems, it provides methods for improving communication and specific action plans for repairing relationships. Furthermore, for health problems, it can provide suggestions for improving lifestyle habits and referrals to appropriate medical institutions. Step 3: The search unit searches for experts who possess the solutions generated by the generation unit. For example, it searches a database of experts to identify the most suitable one. It can search for legal experts for legal concerns and introduce them to the user. It can also search for tax accountants for tax concerns and introduce them to the user. Furthermore, it can search for IT consultants for IT concerns and introduce them to the user. Step 4: The matching unit matches users with experts found by the search unit. For example, it matches users with experts through means such as online chat or video calls. Users and experts can be matched via online chat and consult in real time. Users and experts can also be matched using video calls and consult while seeing each other's faces. Furthermore, users and experts can be matched using email or messaging apps and consult asynchronously.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, search unit, and matching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing users to input their problems on a dedicated website. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generation AI to analyze the problems received by the reception unit and generate specific solutions. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, which searches for experts who possess the generated solutions. The matching unit is implemented by the control unit 46A of the smart device 14, which matches users with experts through means such as online chat or video calls. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, search unit, and matching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input their concerns using voice input. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a generation AI to analyze the concerns received by the reception unit and generate specific solutions. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for experts who possess the generated solutions. The matching unit is implemented by the control unit 46A of the smart glasses 214, which matches the user with experts through means such as online chat or video call. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, search unit, and matching unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input their concerns using voice input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generation AI to analyze the concerns received by the reception unit and generate specific solutions. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, which searches for experts who possess the generated solutions. The matching unit is implemented by the control unit 46A of the headset terminal 314, which matches the user with experts through means such as online chat or video call. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, search unit, and matching unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input their concerns using voice input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generation AI to analyze the concerns received by the reception unit and generate specific solutions. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, which searches for experts who possess the generated solutions. The matching unit is implemented by the control unit 46A of the robot 414, which matches the user with experts through means such as online chat or video call. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A reception desk to handle user inquiries, A generation unit analyzes the problems received by the reception unit and generates specific solutions, A search unit that searches for experts who have solutions generated by the generation unit, The system includes a matching unit that matches experts found by the search unit with users. A system characterized by the following features. (Note 2) The generating unit is Generative AI understands problems and generates concrete solutions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Search the database of experts to identify the most suitable expert. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Matching users with experts through methods such as online chat and video calls. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We conduct market and competitor analysis to address business challenges and propose concrete strategies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We provide methods for improving communication and concrete action plans for repairing relationships to address relationship problems. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is We provide lifestyle improvements and referrals to appropriate medical institutions to address health concerns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving complaints based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is We analyze the user's past problem history and select the most suitable contact method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a user submits a problem, filtering is performed based on their current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the problems to be addressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a complaint, the system prioritizes accepting complaints that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving a complaint, the system analyzes the user's social media activity and accepts related complaints. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts how the solution is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating solutions, adjust the level of detail in the solutions based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating solutions, different generation algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the solution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating solutions, prioritize solutions based on when the problem was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating solutions, adjust the order of solutions based on the relevance of the problems. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, We estimate user sentiment and adjust expert search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When searching for experts, consider the relationships between experts to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, When searching for experts, the search should take into account the experts' attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching for experts, the search should take into account the geographical distribution of those experts. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, When searching for experts, referencing relevant literature by those experts improves the accuracy of the search. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is It estimates the user's emotions and adjusts the matching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the system selects the optimal matching method by referring to the past matching history of the user and the expert. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is During the matching process, the matching method is customized based on the attribute information of the user and the expert. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, the system selects the optimal matching method by considering the geographical location information of both the user and the expert. The system described in Appendix 1, characterized by the features described herein. (Note 31) The matching unit is During the matching process, we analyze the social media activity of both users and experts and propose matching methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk to handle user inquiries, A generation unit analyzes the problems received by the reception unit and generates specific solutions, A search unit that searches for experts who have solutions generated by the generation unit, The system includes a matching unit that matches experts found by the search unit with users. A system characterized by the following features.

2. The generating unit is The AI ​​generates solutions to understand your problems. The system according to feature 1.

3. The aforementioned search unit, Search the database of experts to identify the most suitable expert. The system according to feature 1.

4. The matching unit is Matching users with experts through methods such as online chat and video calls. The system according to feature 1.

5. The generating unit is We conduct market and competitor analysis to address business challenges and propose concrete strategies. The system according to feature 1.

6. The generating unit is We provide methods for improving communication and concrete action plans for repairing relationships to address relationship problems. The system according to feature 1.

7. The generating unit is We provide lifestyle improvements and referrals to appropriate medical institutions to address health concerns. The system according to feature 1.

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

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