System

Abstract

To provide a system that provides appropriate responses when children and young people seek consultations.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, an answering unit, and a solution unit. The reception unit receives contents of consultations from users. The generation unit analyzes the information received by the reception unit, and generates questions. The answering unit receives users' answers based on the questions generated by the generation unit. The solution unit provides solutions based on the answers received by the answering unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem that it was not possible to respond appropriately when children or young people sought advice, making it difficult to resolve their problems.

[0005] The system according to the embodiment aims to provide an appropriate response and resolve problems when children or young people seek advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an answering unit, and a resolution unit. The reception unit receives consultation details from a user. The generation unit analyzes the information received by the reception unit and generates a question. The answering unit receives an answer from the user based on the question generated by the generation unit. The resolution unit provides a solution based on the answer received by the answering unit. Effect of the Invention

[0007] The system according to the embodiment can provide an appropriate response and resolve problems when children or young people seek advice. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The counseling support system according to the embodiment of the present invention uses AI to reproduce the knowledge of professional counselors and the skillful way of listening to each individual, and is a system that carefully finds out the details and solves the problems of even young people who are not good at speaking. This counseling support system repeats the process of a user inputting the content of the consultation, an AI analyzing the content, generating appropriate questions, and the user inputting the answer, thereby digging deeper into the user's problem and ultimately providing a solution. For example, if a user inputs a consultation content such as "I am being bullied at school," the AI ​​analyzes the information and generates a question such as "What is the specific content of the bullying?" If the user answers "I am ignored by my classmates every day," the AI ​​digs deeper and ultimately provides a solution such as "I recommend consulting an appropriate expert." With this mechanism, even young people who are not good at speaking can carefully find out the details and solve the problem. This mechanism also solves the issues of services such as 24-hour children's SOS hotline, and helps change the world in which children are difficult to live. As a result, the counseling support system can efficiently accept and analyze the content of the user's consultation, generate appropriate questions, and provide a solution.

[0029] The counseling support system according to the embodiment includes a reception unit, a generation unit, an answering unit, and a resolution unit. The reception unit receives consultation contents from a user. The consultation contents from the user include, for example, technical consultation, business consultation, personal consultation, and the like, but are not limited to such examples. The reception unit, for example, receives the consultation contents input by the user as text data. The reception unit can also convert the consultation contents dictated by the user using voice input into text data and accept the text data. The generation unit uses AI to analyze the information received by the reception unit and generate an appropriate question. The generation unit uses, for example, AI that has learned the knowledge of a professional counselor to generate a question based on the consultation contents of the user. The generation unit uses, for example, natural language processing technology to analyze the consultation contents of the user and generate an appropriate question. The answering unit accepts the answer of the user based on the question generated by the generation unit. The answering unit accepts, for example, the answer input by the user as text data. The answering unit can also convert the answer dictated by the user using voice input into text data and accept the text data. The solving unit provides a solution based on the answer accepted by the answering unit. The solving unit provides, for example, a solution to the user's problem. The solving unit, for example, delves into the user's problem and finally provides a solution. In this way, the counseling support system according to the embodiment can efficiently accept and analyze the contents of the user's consultation, generate appropriate questions, and provide a solution.

[0030] The reception unit receives consultation contents from the user. The consultation contents from the user include, for example, technical consultation, business consultation, personal consultation, etc., but are not limited to such examples. The reception unit, for example, receives the consultation contents input by the user as text data. Specifically, the user inputs the consultation contents through a dedicated web form or application, and the data is transmitted to the server. The reception unit can also convert the consultation contents dictated by the user using voice input into text data and accept it. In the case of voice input, the user speaks the consultation contents through a microphone, and the voice data is converted into text data using voice recognition technology. Voice recognition technology includes a process of analyzing the characteristics of the voice and converting it into text using a language model. This allows the user to easily input the consultation contents, and the system can handle a variety of input methods. Furthermore, the reception unit temporarily stores the user's input data so that it can be accessed by subsequent processing departments. For example, the consultation contents stored in the database can be referenced by the generation unit or the answering unit as necessary. This allows the reception unit to efficiently accept the consultation contents from the user and smoothly hand them over to subsequent processing.

[0031] The generation unit uses AI to analyze the information received by the reception unit and generates appropriate questions. The generation unit uses, for example, AI that has learned the knowledge of professional counselors to generate questions based on the user's consultation content. Specifically, the generation unit uses natural language processing technology to analyze the user's consultation content and generate appropriate questions. Natural language processing technology includes morphological analysis, context analysis, semantic analysis, etc., and combines these to accurately understand the user's intention. For example, if a user consults about "having a lot of stress at work," the generation unit analyzes the context and generates questions such as "In what specific situations do you feel stressed?" and "Have you tried anything to reduce stress?" The generation unit uses an AI model to learn past counseling data and expert knowledge and generate questions that are optimal for the user's consultation content. This allows the generation unit to dig deeper into the user's problems and extract more specific information. Furthermore, the generation unit checks the appropriateness and grammar of the generated questions before presenting them to the user, and provides them in a form that is easy for the user to understand. This allows the generation unit to generate appropriate and effective questions for the user's consultation content and improve the quality of counseling.

[0032] The answering section accepts the user's answer based on the question generated by the generating section. The answering section, for example, accepts the answer entered by the user as text data. Specifically, the user enters the answer through a dedicated web form or application, and the data is sent to the server. The answering section can also convert the answer dictated by the user using voice input into text data and accept it. In the case of voice input, the user speaks the answer through a microphone, and the voice data is converted into text data using voice recognition technology. Voice recognition technology includes a process of analyzing the characteristics of the voice and converting it into text using a language model. This allows the user to easily enter the answer, and the system can handle a variety of input methods. Furthermore, the answering section temporarily stores the user's input data so that it can be accessed by subsequent processing departments. For example, the answer contents stored in the database can be referenced by the solving section as necessary. This allows the answering section to efficiently accept answers from the user and smoothly hand them over to subsequent processing.

[0033] The solving unit provides a solution based on the answer accepted by the answering unit. The solving unit provides, for example, a solution to the user's problem. Specifically, the solving unit delves into the user's problem and finally provides a solution. The solving unit uses AI to analyze the content of the user's answer and proposes an appropriate solution. For example, if the user answers that "I have a lot of stress at work," the solving unit analyzes the answer and provides stress management methods, relaxation techniques, time management advice, and the like. The solving unit proposes an optimal solution to the user's problem using an AI model that has learned past counseling data and expert knowledge. Furthermore, the solving unit collects user feedback and evaluates the effectiveness of the proposed solution. For example, the user inputs the results of trying out the proposed solution as feedback, and the accuracy and effectiveness of the solution are continuously improved based on the data. This allows the solving unit to provide an appropriate and effective solution to the user's problem and improve the quality of counseling. Furthermore, the solving unit provides continuous support until the user's problem is resolved, and suggests additional questions and solutions as necessary. This allows the solving unit to support the user in solving the user's problem and increase satisfaction.

[0034] The generation unit can generate questions using an AI that has learned the knowledge of professional counselors. The generation unit generates questions based on the content of the user's consultation using, for example, an AI that has learned the knowledge of professional counselors. The knowledge of professional counselors includes, for example, knowledge of psychology, counseling techniques, experience, etc., but is not limited to such examples. The AI ​​is realized using, for example, a machine learning model, a natural language processing technology, a neural network, etc. The generation unit analyzes the content of the user's consultation using, for example, a natural language processing technology, and generates appropriate questions. As a result, the generation unit can generate more appropriate questions by utilizing the knowledge of professional counselors.

[0035] The solving unit can provide a solution to the user's problem. The solving unit, for example, provides a solution to the user's problem. The solution includes, for example, a technical solution, a business solution, a psychological solution, and the like, but is not limited to such examples. The solving unit, for example, delves into the user's problem and finally provides a solution. In this way, the solving unit can provide an appropriate solution to the user's problem.

[0036] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, proposes the optimal reception method based on the content of the user's frequent consultation in the past. The reception unit can also select a preferential reception method for a specific problem from the user's past consultation history. Furthermore, the reception unit can analyze the user's past consultation history and select the most effective reception method. This makes it possible to select the optimal reception method based on the user's past consultation history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past consultation history data to the generation AI and cause the generation AI to select the optimal reception method.

[0037] The reception unit can preferentially receive highly relevant consultation contents in consideration of the geographical location information of the user when receiving the consultation contents. For example, when the user is in a specific area, the reception unit preferentially receives consultation contents related to the area. In addition, when the user is moving, the reception unit can also suggest optimal consultation contents based on the current location. Furthermore, the reception unit can preferentially receive consultation contents with high relevance based on the geographical location information of the user. This makes it possible to preferentially receive consultation contents with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select highly relevant consultation contents.

[0038] The reception unit can analyze the user's social media activity and receive related content when receiving the consultation content. For example, the reception unit preferentially receives related consultation content based on the content shared by the user on social media. The reception unit can also analyze the user's current interests from the user's social media activity and suggest related consultation content. Furthermore, the reception unit can filter optimal consultation content based on the user's social media activity. This makes it possible to receive related consultation content based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related consultation content.

[0039] The generation unit can adjust the level of detail of the question based on the importance of the consultation content when generating a question. For example, the generation unit generates a detailed question for an important consultation content. The generation unit can also generate a concise question for a general consultation content. Furthermore, the generation unit can also generate a question that can be answered quickly for a consultation content with high urgency. This makes it possible to adjust the level of detail of the question according to the importance of the consultation content. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input importance data of the consultation content to the generation AI and cause the generation AI to adjust the level of detail of the question.

[0040] The generation unit can apply different question generation algorithms depending on the category of the consultation content when generating a question. For example, the generation unit applies a question generation algorithm that asks about specific details of bullying to a consultation content about bullying. The generation unit can also apply a question generation algorithm that asks about specific academic issues to a consultation content about academics. Furthermore, the generation unit can apply a question generation algorithm that asks about specific family situations to a consultation content about family problems. This makes it possible to apply an appropriate question generation algorithm depending on the category of the consultation content. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the consultation content to the generation AI and cause the generation AI to apply the question generation algorithm.

[0041] The generation unit can determine the priority of questions based on the submission time of the consultation content when generating questions. For example, for consultation content with high urgency, the generation unit preferentially generates questions that can be answered quickly. The generation unit can also generate normal questions for general consultation content. Furthermore, the generation unit can also preferentially generate detailed questions for important consultation content. This makes it possible to determine the priority of questions according to the submission time of the consultation content. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the submission time data of the consultation content to the generation AI and cause the generation AI to determine the priority of questions.

[0042] The generation unit can adjust the order of questions based on the relevance of the consultation contents when generating questions. For example, for important consultation contents, the generation unit can generate detailed questions first. For general consultation contents, the generation unit can also generate normal questions. Furthermore, for consultation contents with high urgency, the generation unit can also generate questions that can be answered quickly first. This makes it possible to adjust the order of questions according to the relevance of the consultation contents. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the consultation contents to the generation AI and cause the generation AI to adjust the order of questions.

[0043] The answering section can select the optimal reception method by referring to the user's past answer history when accepting an answer. The answering section can, for example, suggest the optimal reception method based on the content of answers frequently given by the user in the past. The answering section can also select a preferred reception method for a specific problem from the user's past answer history. Furthermore, the answering section can analyze the user's past answer history and select the most effective reception method. This makes it possible to select the optimal reception method based on the user's past answer history. A part or all of the above-mentioned processing in the answering section can be performed, for example, using AI or can be performed without using AI. For example, the answering section can input the user's past answer history data to the generation AI and cause the generation AI to select the optimal reception method.

[0044] When accepting an answer, the answering unit can preferentially accept answers with high relevance in consideration of the geographical location information of the user. For example, when the user is in a specific area, the answering unit preferentially accepts answers related to that area. In addition, when the user is moving, the answering unit can also suggest an optimal answer based on the current location. Furthermore, the answering unit can preferentially accept answers with high relevance based on the geographical location information of the user. This makes it possible to preferentially accept answers with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI, or may be performed without using AI. For example, the answering unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select answers with high relevance.

[0045] The answering unit can analyze the user's social media activity and accept related answers when accepting an answer. The answering unit preferentially accepts related answers based on, for example, the content shared by the user on social media. The answering unit can also analyze the user's current interests from the user's social media activity and suggest related answers. Furthermore, the answering unit can filter optimal answers based on the user's social media activity. This makes it possible to accept related answers based on the user's social media activity. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's social media data to the generation AI and cause the generation AI to select related answers.

[0046] When providing a solution, the solving unit can select an optimal solution by analyzing the content of the user's past consultation. For example, the solving unit proposes an optimal solution based on the content of the user's past consultation. The solving unit can also select an optimal solution for a specific problem from the user's past consultation history. Furthermore, the solving unit can analyze the user's past consultation history and select the most effective solution. This makes it possible to select an optimal solution based on the user's past consultation content. A part or all of the above-mentioned processing in the solving unit may be performed, for example, using AI, or may be performed without using AI. For example, the solving unit can input the user's past consultation history data to the generating AI and cause the generating AI to select an optimal solution.

[0047] The solving unit can select the optimal solution in consideration of the geographical location information of the user when providing a solution. For example, when the user is in a specific area, the solving unit preferentially provides solutions related to that area. In addition, when the user is moving, the solving unit can also propose the optimal solution based on the current location. Furthermore, the solving unit can also preferentially provide solutions with high relevance based on the geographical location information of the user. This makes it possible to select the optimal solution based on the geographical location information of the user. A part or all of the above-mentioned processing in the solving unit may be performed, for example, using AI, or may be performed without using AI. For example, the solving unit can input the geographical location information data of the user to the generating AI and cause the generating AI to select the optimal solution.

[0048] When providing a solution, the solving unit can analyze the user's social media activity and propose a means for the solution. The solving unit can, for example, preferentially provide related solutions based on the content shared by the user on social media. The solving unit can also analyze the user's current interests from the user's social media activity and propose related solutions. Furthermore, the solving unit can filter optimal solutions based on the user's social media activity. This makes it possible to propose optimal solutions based on the user's social media activity. Some or all of the above-mentioned processing in the solving unit can be performed, for example, using AI or can be performed without using AI. For example, the solving unit can input the user's social media data into the generating AI and cause the generating AI to execute the proposal of a means for the solution.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The counseling support system may further include a hobby / interest acquisition unit for grasping the user's hobbies and interests. The hobby / interest acquisition unit may, for example, analyze information previously input by the user or social media data to identify the user's hobbies and interests. This allows the generation unit and the solution unit to provide questions and solutions based on the user's interests. For example, if the user is interested in music, the generation unit may generate questions related to music. Also, if the user is interested in sports, the solution unit may suggest a method of relieving stress through sports. Furthermore, if the user is interested in reading, the generation unit may generate questions related to reading.

[0051] The counseling support system may further include a lifestyle acquisition unit for grasping the lifestyle of the user. The lifestyle acquisition unit collects, for example, information such as daily activities, meals, and sleep patterns input by the user. This allows the generation unit and the solution unit to provide questions and solutions based on the lifestyle of the user. For example, if the user is sleep deprived, the generation unit may generate questions about sleep. Also, if the user has irregular meals, the solution unit may suggest a balanced diet. Furthermore, if the user is not exercising enough, the generation unit may generate questions about exercise.

[0052] The counseling support system may further include an anonymization unit that anonymizes the consultation contents of the user. The anonymization unit, for example, provides an environment in which the user can consult with confidence by deleting the user's personal information and anonymizing the consultation contents. This allows the user to input the consultation contents while protecting privacy. For example, the consultation contents can be anonymized by deleting personal information such as the user's name and address. In addition, the consultation contents input by voice can be anonymized by converting the user's voice. Furthermore, the security of the data can be ensured by encrypting the consultation contents of the user.

[0053] The counseling support system may further include a sharing unit that shares the contents of the user's consultation with other users. The sharing unit may share the contents of the consultation with other users only if the user agrees, for example, and may obtain sympathy and advice. This allows the user to receive support from other users and obtain hints for resolving the problem. For example, users with the same problem may share the contents of the consultation and provide each other with advice. In addition, experts may view the contents of the consultation and provide appropriate advice. Furthermore, by sharing the contents of the consultation anonymously, the user may receive support from other users while protecting their privacy.

[0054] The counseling support system may further include a classification unit that automatically classifies the consultation contents of the user. The classification unit, for example, analyzes the consultation contents of the user and automatically classifies them by category. This allows the generation unit and the solution unit to provide questions and solutions more efficiently. For example, if the consultation contents of the user are related to "bullying at school," the classification unit can classify the contents as "bullying." Furthermore, if the consultation contents of the user are related to "family problems," the classification unit can classify the contents as "family problems." Furthermore, if the consultation contents of the user are related to "stress at work," the classification unit can classify the contents as "workplace stress."

[0055] The counseling support system may further include a summarizing unit that automatically summarizes the consultation content of the user. The summarizing unit, for example, analyzes the consultation content of a long text input by the user, extracts important points, and summarizes them. This allows the generating unit and the solving unit to efficiently provide questions and solutions. For example, when the user inputs a long consultation content such as "I recently had a fight with a friend at school, and I'm really worried about it. What can I do to make up with him?", the summarizing unit can summarize it as "I had a fight with my friend and I'm worried about it. I want to know how to make up with him." Also, when the user inputs a consultation content such as "I'm stressed out at work, and every day is hard. What can I do to reduce my stress?", the summarizing unit can summarize it as "I want to reduce my work stress." Furthermore, when the user inputs a consultation content such as "I'm worried about problems at home, and I want to know how to build a good relationship with my family," the summarizing unit can summarize it as "I'm worried about problems at home. I want to build a good relationship with my family."

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit receives the consultation contents from the user. The consultation contents from the user include technical consultations, business consultations, personal consultations, etc. The reception unit not only receives the consultation contents input by the user as text data, but can also convert the consultation contents dictated by the user using voice input into text data and accept the same. Step 2: The generation unit uses AI to analyze the information received by the reception unit and generate appropriate questions. The generation unit uses AI that has learned the knowledge of professional counselors and natural language processing technology to generate questions based on the content of the user's consultation. Step 3: The answering unit accepts an answer from the user based on the question generated by the generating unit. The answering unit accepts the answer entered by the user as text data, and can also accept an answer dictated by the user using voice input by converting it into text data. Step 4: The solver provides a solution based on the answer accepted by the answerer. The solver provides a solution to the user's problem, delves into the user's problem and finally provides a solution.

[0058] (Example 2) The counseling support system according to the embodiment of the present invention uses AI to reproduce the knowledge of professional counselors and the skillful way of listening to each individual, and is a system that carefully finds out the details and solves the problems of even young people who are not good at speaking. This counseling support system repeats the process of a user inputting the content of the consultation, an AI analyzing the content, generating appropriate questions, and the user inputting the answer, thereby digging deeper into the user's problem and ultimately providing a solution. For example, if a user inputs a consultation content such as "I am being bullied at school," the AI ​​analyzes the information and generates a question such as "What is the specific content of the bullying?" If the user answers "I am ignored by my classmates every day," the AI ​​digs deeper and ultimately provides a solution such as "I recommend consulting an appropriate expert." With this mechanism, even young people who are not good at speaking can carefully find out the details and solve the problem. This mechanism also solves the issues of services such as 24-hour children's SOS hotline, and helps change the world in which children are difficult to live. As a result, the counseling support system can efficiently accept and analyze the content of the user's consultation, generate appropriate questions, and provide a solution.

[0059] The counseling support system according to the embodiment includes a reception unit, a generation unit, an answering unit, and a resolution unit. The reception unit receives consultation contents from a user. The consultation contents from the user include, for example, technical consultation, business consultation, personal consultation, and the like, but are not limited to such examples. The reception unit, for example, receives the consultation contents input by the user as text data. The reception unit can also convert the consultation contents dictated by the user using voice input into text data and accept the text data. The generation unit uses AI to analyze the information received by the reception unit and generate an appropriate question. The generation unit uses, for example, AI that has learned the knowledge of a professional counselor to generate a question based on the consultation contents of the user. The generation unit uses, for example, natural language processing technology to analyze the consultation contents of the user and generate an appropriate question. The answering unit accepts the answer of the user based on the question generated by the generation unit. The answering unit accepts, for example, the answer input by the user as text data. The answering unit can also convert the answer dictated by the user using voice input into text data and accept the text data. The solving unit provides a solution based on the answer accepted by the answering unit. The solving unit provides, for example, a solution to the user's problem. The solving unit, for example, delves into the user's problem and finally provides a solution. In this way, the counseling support system according to the embodiment can efficiently accept and analyze the contents of the user's consultation, generate appropriate questions, and provide a solution.

[0060] The reception unit receives consultation contents from the user. The consultation contents from the user include, for example, technical consultation, business consultation, personal consultation, etc., but are not limited to such examples. The reception unit, for example, receives the consultation contents input by the user as text data. Specifically, the user inputs the consultation contents through a dedicated web form or application, and the data is transmitted to the server. The reception unit can also convert the consultation contents dictated by the user using voice input into text data and accept it. In the case of voice input, the user speaks the consultation contents through a microphone, and the voice data is converted into text data using voice recognition technology. Voice recognition technology includes a process of analyzing the characteristics of the voice and converting it into text using a language model. This allows the user to easily input the consultation contents, and the system can handle a variety of input methods. Furthermore, the reception unit temporarily stores the user's input data so that it can be accessed by subsequent processing departments. For example, the consultation contents stored in the database can be referenced by the generation unit or the answering unit as necessary. This allows the reception unit to efficiently accept the consultation contents from the user and smoothly hand them over to subsequent processing.

[0061] The generation unit uses AI to analyze the information received by the reception unit and generates appropriate questions. The generation unit uses, for example, AI that has learned the knowledge of professional counselors to generate questions based on the user's consultation content. Specifically, the generation unit uses natural language processing technology to analyze the user's consultation content and generate appropriate questions. Natural language processing technology includes morphological analysis, context analysis, semantic analysis, etc., and combines these to accurately understand the user's intention. For example, if a user consults about "having a lot of stress at work," the generation unit analyzes the context and generates questions such as "In what specific situations do you feel stressed?" and "Have you tried anything to reduce stress?" The generation unit uses an AI model to learn past counseling data and expert knowledge and generate questions that are optimal for the user's consultation content. This allows the generation unit to dig deeper into the user's problems and extract more specific information. Furthermore, the generation unit checks the appropriateness and grammar of the generated questions before presenting them to the user, and provides them in a form that is easy for the user to understand. This allows the generation unit to generate appropriate and effective questions for the user's consultation content and improve the quality of counseling.

[0062] The answering section accepts the user's answer based on the question generated by the generating section. The answering section, for example, accepts the answer entered by the user as text data. Specifically, the user enters the answer through a dedicated web form or application, and the data is sent to the server. The answering section can also convert the answer dictated by the user using voice input into text data and accept it. In the case of voice input, the user speaks the answer through a microphone, and the voice data is converted into text data using voice recognition technology. Voice recognition technology includes a process of analyzing the characteristics of the voice and converting it into text using a language model. This allows the user to easily enter the answer, and the system can handle a variety of input methods. Furthermore, the answering section temporarily stores the user's input data so that it can be accessed by subsequent processing departments. For example, the answer contents stored in the database can be referenced by the solving section as necessary. This allows the answering section to efficiently accept answers from the user and smoothly hand them over to subsequent processing.

[0063] The solving unit provides a solution based on the answer accepted by the answering unit. The solving unit provides, for example, a solution to the user's problem. Specifically, the solving unit delves into the user's problem and finally provides a solution. The solving unit uses AI to analyze the content of the user's answer and proposes an appropriate solution. For example, if the user answers that "I have a lot of stress at work," the solving unit analyzes the answer and provides stress management methods, relaxation techniques, time management advice, and the like. The solving unit proposes an optimal solution to the user's problem using an AI model that has learned past counseling data and expert knowledge. Furthermore, the solving unit collects user feedback and evaluates the effectiveness of the proposed solution. For example, the user inputs the results of trying out the proposed solution as feedback, and the accuracy and effectiveness of the solution are continuously improved based on the data. This allows the solving unit to provide an appropriate and effective solution to the user's problem and improve the quality of counseling. Furthermore, the solving unit provides continuous support until the user's problem is resolved, and suggests additional questions and solutions as necessary. This allows the solving unit to support the user in solving the user's problem and increase satisfaction.

[0064] The generation unit can generate questions using an AI that has learned the knowledge of professional counselors. The generation unit generates questions based on the content of the user's consultation using, for example, an AI that has learned the knowledge of professional counselors. The knowledge of professional counselors includes, for example, knowledge of psychology, counseling techniques, experience, etc., but is not limited to such examples. The AI ​​is realized using, for example, a machine learning model, a natural language processing technology, a neural network, etc. The generation unit analyzes the content of the user's consultation using, for example, a natural language processing technology, and generates appropriate questions. As a result, the generation unit can generate more appropriate questions by utilizing the knowledge of professional counselors.

[0065] The solving unit can provide a solution to the user's problem. The solving unit, for example, provides a solution to the user's problem. The solution includes, for example, a technical solution, a business solution, a psychological solution, and the like, but is not limited to such examples. The solving unit, for example, delves into the user's problem and finally provides a solution. In this way, the solving unit can provide an appropriate solution to the user's problem.

[0066] The reception unit can estimate the user's emotion and adjust the timing of receiving the consultation content based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit immediately receives the consultation content. Also, when the user is relaxed, the reception unit can take a little time to receive detailed consultation content. Furthermore, when the user is in a hurry, the reception unit can preferentially receive brief consultation content. This makes it possible to receive the consultation content at an appropriate timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0067] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, proposes the optimal reception method based on the content of the user's frequent consultation in the past. The reception unit can also select a preferential reception method for a specific problem from the user's past consultation history. Furthermore, the reception unit can analyze the user's past consultation history and select the most effective reception method. This makes it possible to select the optimal reception method based on the user's past consultation history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past consultation history data to the generation AI and cause the generation AI to select the optimal reception method.

[0068] The reception unit can perform filtering based on the user's current psychological state and field of interest when receiving the consultation content. For example, when the user is feeling stressed, the reception unit preferentially receives consultation content related to stress reduction. The reception unit can also preferentially receive consultation content related to a field in which the user is interested. Furthermore, the reception unit can filter appropriate consultation content based on the user's current psychological state. This makes it possible to filter appropriate consultation content according to the user's psychological state and field of interest. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's psychological state data to the generation AI and cause the generation AI to execute filtering.

[0069] The reception unit can estimate the user's emotion and determine the priority of the consultation contents to be received based on the estimated user's emotion. For example, when the user has an urgent problem, the reception unit receives the consultation contents with the highest priority. In addition, when the user is relaxed, the reception unit can treat the consultation contents equally to other consultation contents. Furthermore, when the user is feeling stressed, the reception unit can receive the consultation contents with priority. This makes it possible to determine the priority of the consultation contents according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority.

[0070] The reception unit can preferentially receive highly relevant consultation contents in consideration of the geographical location information of the user when receiving the consultation contents. For example, when the user is in a specific area, the reception unit preferentially receives consultation contents related to the area. In addition, when the user is moving, the reception unit can also suggest optimal consultation contents based on the current location. Furthermore, the reception unit can preferentially receive consultation contents with high relevance based on the geographical location information of the user. This makes it possible to preferentially receive consultation contents with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select highly relevant consultation contents.

[0071] The reception unit can analyze the user's social media activity and receive related content when receiving the consultation content. For example, the reception unit preferentially receives related consultation content based on the content shared by the user on social media. The reception unit can also analyze the user's current interests from the user's social media activity and suggest related consultation content. Furthermore, the reception unit can filter optimal consultation content based on the user's social media activity. This makes it possible to receive related consultation content based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related consultation content.

[0072] The generation unit can estimate the user's emotion and adjust the way of expressing the question based on the estimated emotion of the user. For example, when the user is nervous, the generation unit generates a question in gentle language. Also, when the user is relaxed, the generation unit can generate a detailed question. Furthermore, when the user is stressed, the generation unit can generate a simple and easy-to-understand question. This makes it possible to adjust the way of expressing the question according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the question.

[0073] The generation unit can adjust the level of detail of the question based on the importance of the consultation content when generating a question. For example, the generation unit generates a detailed question for an important consultation content. The generation unit can also generate a concise question for a general consultation content. Furthermore, the generation unit can also generate a question that can be answered quickly for a consultation content with high urgency. This makes it possible to adjust the level of detail of the question according to the importance of the consultation content. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input importance data of the consultation content to the generation AI and cause the generation AI to adjust the level of detail of the question.

[0074] The generation unit can apply different question generation algorithms depending on the category of the consultation content when generating a question. For example, the generation unit applies a question generation algorithm that asks about specific details of bullying to a consultation content about bullying. The generation unit can also apply a question generation algorithm that asks about specific academic issues to a consultation content about academics. Furthermore, the generation unit can apply a question generation algorithm that asks about specific family situations to a consultation content about family problems. This makes it possible to apply an appropriate question generation algorithm depending on the category of the consultation content. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the consultation content to the generation AI and cause the generation AI to apply the question generation algorithm.

[0075] The generation unit can estimate the user's emotion and adjust the length of the question based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short and to the point question. The generation unit can also generate a detailed question when the user is relaxed. Furthermore, when the user is stressed, the generation unit can generate a concise and easy-to-understand question. This allows the length of the question to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the question.

[0076] The generation unit can determine the priority of questions based on the submission time of the consultation content when generating questions. For example, for consultation content with high urgency, the generation unit preferentially generates questions that can be answered quickly. The generation unit can also generate normal questions for general consultation content. Furthermore, the generation unit can also preferentially generate detailed questions for important consultation content. This makes it possible to determine the priority of questions according to the submission time of the consultation content. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the submission time data of the consultation content to the generation AI and cause the generation AI to determine the priority of questions.

[0077] The generation unit can adjust the order of questions based on the relevance of the consultation contents when generating questions. For example, for important consultation contents, the generation unit can generate detailed questions first. For general consultation contents, the generation unit can also generate normal questions. Furthermore, for consultation contents with high urgency, the generation unit can also generate questions that can be answered quickly first. This makes it possible to adjust the order of questions according to the relevance of the consultation contents. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the consultation contents to the generation AI and cause the generation AI to adjust the order of questions.

[0078] The answering unit can estimate the user's emotion and adjust the answer acceptance method based on the estimated user's emotion. For example, when the user is nervous, the answering unit accepts an answer in gentle language. Furthermore, when the user is relaxed, the answering unit can also accept a detailed answer. Furthermore, when the user is stressed, the answering unit can also accept a concise and easy-to-understand answer. This makes it possible to adjust the answer acceptance method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the answer acceptance method.

[0079] The answering section can select the optimal reception method by referring to the user's past answer history when accepting an answer. The answering section can, for example, suggest the optimal reception method based on the content of answers frequently given by the user in the past. The answering section can also select a preferred reception method for a specific problem from the user's past answer history. Furthermore, the answering section can analyze the user's past answer history and select the most effective reception method. This makes it possible to select the optimal reception method based on the user's past answer history. A part or all of the above-mentioned processing in the answering section can be performed, for example, using AI or can be performed without using AI. For example, the answering section can input the user's past answer history data to the generation AI and cause the generation AI to select the optimal reception method.

[0080] The answering unit can filter answers based on the user's current psychological state when accepting answers. For example, when the user is feeling stressed, the answering unit preferentially accepts answers related to stress reduction. The answering unit can also preferentially accept answers related to fields in which the user is interested. Furthermore, the answering unit can filter appropriate answers based on the user's current psychological state. This makes it possible to filter appropriate answers according to the user's psychological state. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's psychological state data to the generation AI and cause the generation AI to perform filtering.

[0081] The answering unit can estimate the user's emotion and determine the priority of the answers based on the estimated user's emotion. For example, when the user has an urgent problem, the answering unit accepts the answer with the highest priority. In addition, when the user is relaxed, the answering unit can also treat the answer as equal to other answers. Furthermore, when the user is stressed, the answering unit can preferentially accept the answer. This makes it possible to determine the priority of the answers according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority.

[0082] When accepting an answer, the answering unit can preferentially accept answers with high relevance in consideration of the geographical location information of the user. For example, when the user is in a specific area, the answering unit preferentially accepts answers related to that area. In addition, when the user is moving, the answering unit can also suggest an optimal answer based on the current location. Furthermore, the answering unit can preferentially accept answers with high relevance based on the geographical location information of the user. This makes it possible to preferentially accept answers with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI, or may be performed without using AI. For example, the answering unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select answers with high relevance.

[0083] The answering unit can analyze the user's social media activity and accept related answers when accepting an answer. The answering unit preferentially accepts related answers based on, for example, the content shared by the user on social media. The answering unit can also analyze the user's current interests from the user's social media activity and suggest related answers. Furthermore, the answering unit can filter optimal answers based on the user's social media activity. This makes it possible to accept related answers based on the user's social media activity. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's social media data to the generation AI and cause the generation AI to select related answers.

[0084] The solving unit can estimate the user's emotion and adjust the method of providing a solution based on the estimated user's emotion. For example, when the user is nervous, the solving unit provides a solution in gentle language. Also, when the user is relaxed, the solving unit can provide a detailed solution. Furthermore, when the user is stressed, the solving unit can provide a concise and easy-to-understand solution. This makes it possible to adjust the method of providing a solution according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the solving unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the solving unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the method of providing a solution.

[0085] When providing a solution, the solving unit can select an optimal solution by analyzing the content of the user's past consultation. For example, the solving unit proposes an optimal solution based on the content of the user's past consultation. The solving unit can also select an optimal solution for a specific problem from the user's past consultation history. Furthermore, the solving unit can analyze the user's past consultation history and select the most effective solution. This makes it possible to select an optimal solution based on the user's past consultation content. A part or all of the above-mentioned processing in the solving unit may be performed, for example, using AI, or may be performed without using AI. For example, the solving unit can input the user's past consultation history data to the generating AI and cause the generating AI to select an optimal solution.

[0086] The solving unit can customize the solution based on the user's current psychological state when providing the solution. For example, when the user is feeling stressed, the solving unit preferentially provides a solution related to stress reduction. The solving unit can also preferentially provide a solution related to a field in which the user is interested. Furthermore, the solving unit can customize an appropriate solution based on the user's current psychological state. This makes it possible to customize an appropriate solution according to the user's psychological state. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the solving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the solving unit can input the user's psychological state data to the generation AI and cause the generation AI to execute customization of the solution.

[0087] The solving unit can estimate the user's emotion and determine the priority of solutions based on the estimated user's emotion. For example, when the user has an urgent problem, the solving unit provides the solution with the highest priority. In addition, when the user is relaxed, the solving unit can treat the solution as equal to other solutions. Furthermore, when the user is stressed, the solving unit can provide the solution with priority. This makes it possible to determine the priority of solutions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the solving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the solving unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority.

[0088] The solving unit can select the optimal solution in consideration of the geographical location information of the user when providing a solution. For example, when the user is in a specific area, the solving unit preferentially provides solutions related to that area. In addition, when the user is moving, the solving unit can also propose the optimal solution based on the current location. Furthermore, the solving unit can also preferentially provide solutions with high relevance based on the geographical location information of the user. This makes it possible to select the optimal solution based on the geographical location information of the user. A part or all of the above-mentioned processing in the solving unit may be performed, for example, using AI, or may be performed without using AI. For example, the solving unit can input the geographical location information data of the user to the generating AI and cause the generating AI to select the optimal solution.

[0089] When providing a solution, the solving unit can analyze the user's social media activity and propose a means for the solution. The solving unit can, for example, preferentially provide related solutions based on the content shared by the user on social media. The solving unit can also analyze the user's current interests from the user's social media activity and propose related solutions. Furthermore, the solving unit can filter optimal solutions based on the user's social media activity. This makes it possible to propose optimal solutions based on the user's social media activity. Some or all of the above-mentioned processing in the solving unit can be performed, for example, using AI or can be performed without using AI. For example, the solving unit can input the user's social media data into the generating AI and cause the generating AI to execute the proposal of a means for the solution.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The counseling support system may further include a physiological data acquisition unit that acquires physiological data of the user. The physiological data acquisition unit acquires physiological data such as the user's heart rate, electrodermal activity, and body temperature. This allows the user's stress level and relaxation level to be grasped in real time, and the generation unit and the solution unit to provide more appropriate questions and solutions. For example, if the user's heart rate suddenly increases, the generation unit can generate questions to reduce stress. Also, if the electrodermal activity is high, the solution unit can provide advice to relax. Furthermore, if the body temperature is decreasing, the generation unit can determine that the user is calm and generate detailed questions.

[0092] The counseling support system may further include a hobby / interest acquisition unit for grasping the user's hobbies and interests. The hobby / interest acquisition unit may, for example, analyze information previously input by the user or social media data to identify the user's hobbies and interests. This allows the generation unit and the solution unit to provide questions and solutions based on the user's interests. For example, if the user is interested in music, the generation unit may generate questions related to music. Also, if the user is interested in sports, the solution unit may suggest a method of relieving stress through sports. Furthermore, if the user is interested in reading, the generation unit may generate questions related to reading.

[0093] The counseling support system may further include a lifestyle acquisition unit for grasping the lifestyle of the user. The lifestyle acquisition unit collects, for example, information such as daily activities, meals, and sleep patterns input by the user. This allows the generation unit and the solution unit to provide questions and solutions based on the lifestyle of the user. For example, if the user is sleep deprived, the generation unit may generate questions about sleep. Also, if the user has irregular meals, the solution unit may suggest a balanced diet. Furthermore, if the user is not exercising enough, the generation unit may generate questions about exercise.

[0094] The counseling support system may further include an emotion display unit that visually displays the user's emotions. The emotion display unit, for example, displays the user's emotions using colors or icons, allowing the user to visually understand his or her own emotions. This makes it easier for the user to objectively grasp his or her own emotions, improving the effectiveness of counseling. For example, if the user is feeling stressed, the emotion display unit may display a red icon. Also, if the user is relaxed, the emotion display unit may display a blue icon. Furthermore, if the user is happy, the emotion display unit may display a yellow icon.

[0095] The counseling support system may further include an anonymization unit that anonymizes the consultation contents of the user. The anonymization unit, for example, provides an environment in which the user can consult with confidence by deleting the user's personal information and anonymizing the consultation contents. This allows the user to input the consultation contents while protecting privacy. For example, the consultation contents can be anonymized by deleting personal information such as the user's name and address. In addition, the consultation contents input by voice can be anonymized by converting the user's voice. Furthermore, the security of the data can be ensured by encrypting the consultation contents of the user.

[0096] The counseling support system may further include a sharing unit that shares the contents of the user's consultation with other users. The sharing unit may share the contents of the consultation with other users only if the user agrees, for example, and may obtain sympathy and advice. This allows the user to receive support from other users and obtain hints for resolving the problem. For example, users with the same problem may share the contents of the consultation and provide each other with advice. In addition, experts may view the contents of the consultation and provide appropriate advice. Furthermore, by sharing the contents of the consultation anonymously, the user may receive support from other users while protecting their privacy.

[0097] The counseling support system may further include a voice feedback unit that provides voice feedback of the user's emotions. The voice feedback unit, for example, conveys the user's emotions by voice, allowing the user to understand his / her emotions more deeply. This makes it easier for the user to objectively grasp his / her emotions, improving the effectiveness of counseling. For example, if the user is feeling stressed, the voice feedback unit may convey by voice, "You are currently feeling stressed." Also, if the user is relaxed, the voice feedback unit may convey by voice, "You are currently relaxed." Furthermore, if the user is happy, the voice feedback unit may convey by voice, "You are currently happy."

[0098] The counseling support system may further include a classification unit that automatically classifies the consultation contents of the user. The classification unit, for example, analyzes the consultation contents of the user and automatically classifies them by category. This allows the generation unit and the solution unit to provide questions and solutions more efficiently. For example, if the consultation contents of the user are related to "bullying at school," the classification unit can classify the contents as "bullying." Furthermore, if the consultation contents of the user are related to "family problems," the classification unit can classify the contents as "family problems." Furthermore, if the consultation contents of the user are related to "stress at work," the classification unit can classify the contents as "workplace stress."

[0099] The counseling support system may further include an emotion monitoring unit that monitors the user's emotions in real time. The emotion monitoring unit, for example, analyzes the user's facial expressions, voice, and text data, and monitors the emotions in real time. This allows the generation unit and the solution unit to provide questions and solutions according to the user's emotions. For example, if the user suddenly feels sad during a consultation, the emotion monitoring unit can detect the emotion, and the generation unit can generate a question including words of comfort. Also, if the user feels anger during a consultation, the emotion monitoring unit can detect the emotion, and the solution unit can provide advice to calm down. Furthermore, if the user feels joy during a consultation, the emotion monitoring unit can detect the emotion, and the generation unit can generate a question that shares the joy.

[0100] The counseling support system may further include a summarizing unit that automatically summarizes the consultation content of the user. The summarizing unit, for example, analyzes the consultation content of a long text input by the user, extracts important points, and summarizes them. This allows the generating unit and the solving unit to efficiently provide questions and solutions. For example, when the user inputs a long consultation content such as "I recently had a fight with a friend at school, and I'm really worried about it. What can I do to make up with him?", the summarizing unit can summarize it as "I had a fight with my friend and I'm worried about it. I want to know how to make up with him." Also, when the user inputs a consultation content such as "I'm stressed out at work, and every day is hard. What can I do to reduce my stress?", the summarizing unit can summarize it as "I want to reduce my work stress." Furthermore, when the user inputs a consultation content such as "I'm worried about problems at home, and I want to know how to build a good relationship with my family," the summarizing unit can summarize it as "I'm worried about problems at home. I want to build a good relationship with my family."

[0101] The process flow of the second embodiment will be briefly described below.

[0102] Step 1: The reception unit receives the consultation contents from the user. The consultation contents from the user include technical consultations, business consultations, personal consultations, etc. The reception unit not only receives the consultation contents input by the user as text data, but can also convert the consultation contents dictated by the user using voice input into text data and accept the same. Step 2: The generation unit uses AI to analyze the information received by the reception unit and generate appropriate questions. The generation unit uses AI that has learned the knowledge of professional counselors and natural language processing technology to generate questions based on the content of the user's consultation. Step 3: The answering unit accepts an answer from the user based on the question generated by the generating unit. The answering unit accepts the answer entered by the user as text data, and can also accept an answer dictated by the user using voice input by converting it into text data. Step 4: The solver provides a solution based on the answer accepted by the answerer. The solver provides a solution to the user's problem, delves into the user's problem and finally provides a solution.

[0103] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0105] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0106] For example, the reception unit is realized by the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the solution unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0114] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0117] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the resolution unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the microphone 238 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the resolution unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] For example, the reception unit is realized by the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the solution unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0157] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0161] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] In addition, 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 upon request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0168] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0172] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0174] (Appendix 1) A reception unit that receives consultation contents from users; a generation unit that analyzes the information received by the reception unit and generates a question; an answering unit that receives an answer from a user based on the question generated by the generating unit; A solution unit that provides a solution based on the answer received by the answering unit. A system characterized by: (Appendix 2) The generation unit is Questions are generated using AI that has learned the knowledge of professional counselors 2. The system of claim 1, (Appendix 3) The resolution unit is Providing solutions to user problems 2. The system of claim 1, (Appendix 4) The reception unit is The system estimates the user's emotions and adjusts the timing of accepting consultation requests based on the estimated user emotions. 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past consultation history and select the reception method 2. The system of claim 1, (Appendix 6) The reception unit is When accepting consultation requests, the system filters them based on the user's current psychological state and areas of interest. 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and prioritize the consultation contents to be accepted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The reception unit is When accepting consultation requests, the system takes into account the user's geographic location information and prioritizes the most relevant requests. 2. The system of claim 1, (Appendix 9) The reception unit is When accepting a consultation request, analyze the user's social media activity and accept related content. 2. The system of claim 1, (Appendix 10) The generation unit is Infer user sentiment and adjust question wording based on the inferred sentiment 2. The system of claim 1, (Appendix 11) The generation unit is When generating questions, adjust the level of detail of the questions based on the importance of the topic 2. The system of claim 1, (Appendix 12) The generation unit is When generating questions, different question generation algorithms are applied depending on the category of the consultation content. 2. The system of claim 1, (Appendix 13) The generation unit is Infer user sentiment and adjust question length based on the estimated user sentiment 2. The system of claim 1, (Appendix 14) The generation unit is When questions are generated, they are prioritized based on when they were submitted 2. The system of claim 1, (Appendix 15) The generation unit is When generating questions, adjust the order of questions based on the relevance of the question 2. The system of claim 1, (Appendix 16) The answering section: Inferring user emotions and adjusting the way answers are accepted based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The answering section: When accepting answers, the system selects the optimal method of acceptance by referring to the user's past answer history. 2. The system of claim 1, (Appendix 18) The answering section: When accepting answers, filter answers based on the user's current psychological state 2. The system of claim 1, (Appendix 19) The answering section: Infer user sentiment and prioritize answers based on the inferred sentiment 2. The system of claim 1, (Appendix 20) The answering section: When accepting answers, the system takes into account the user's geographic location information and prioritizes answers that are more relevant to the user. 2. The system of claim 1, (Appendix 21) The answering section: When accepting responses, analyze the user's social media activity and accept relevant responses. 2. The system of claim 1, (Appendix 22) The resolution unit is Inferring user emotions and adjusting the way in which a solution is provided based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The resolution unit is When providing a solution, analyze the user's past inquiries to select the optimal solution. 2. The system of claim 1, (Appendix 24) The resolution unit is When providing a solution, customize the solution based on the user's current state of mind 2. The system of claim 1, (Appendix 25) The resolution unit is Estimate user sentiment and prioritize solutions based on the estimated user sentiment 2. The system of claim 1, (Appendix 26) The resolution unit is When providing solutions, the optimal solution is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 27) The resolution unit is When providing a solution, the system analyzes the user's social media activity to suggest a solution method. 2. The system of claim 1, [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives consultation contents from users; a generation unit that analyzes the information received by the reception unit and generates a question; an answering unit that receives an answer from a user based on the question generated by the generating unit; A solution unit that provides a solution based on the answer received by the answering unit. A system characterized by:

2. The generation unit is Generate questions using AI that has learned the knowledge of professional counselors 2. The system of claim 1 .

3. The solving unit, Providing solutions to user problems 2. The system of claim 1 .

4. The reception unit is The system estimates the user's emotions and adjusts the timing of accepting consultation requests based on the estimated user emotions.

2. The system of claim 1 .

5. The reception unit is Analyze the user's past consultation history and select the reception method 2. The system of claim 1 .

6. The reception unit is When accepting consultation requests, the system filters them based on the user's current psychological state and areas of interest.

2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and prioritize the consultation contents to be accepted based on the estimated user's emotions.

2. The system of claim 1 .

8. The reception unit is When accepting consultation requests, the system takes into account the user's geographic location information and prioritizes the most relevant requests.

2. The system of claim 1 .

9. The reception unit is When accepting a consultation request, analyze the user's social media activity and accept related content.

2. The system of claim 1 .

10. The generation unit is Infer user sentiment and adjust question wording based on the inferred sentiment 2. The system of claim 1 .

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