system

Abstract

We provide a problem-solving system that uses AI for generating moderators, AI for generating questions, and AI for generating secretaries. [Solution] A system including: a means for a user to input a problem through a terminal; a means for a server to receive the problem input by the user; a means for a moderator generation AI to analyze the problem and generate a solution topic; a means for a question generation AI to generate questions based on the solution topic; a means for users to answer the questions generated through their terminal; a means for a scribe generation AI to receive users' answers and record and organize the content of the discussion; a means for the moderator generation AI to progress the discussion based on users' answers and derive a solution; and a means for presenting the final solution to the user's terminal.

Description

[Technical Field]

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

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

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

[0004] Conventional problem-solving systems make it difficult for users to efficiently and effectively advance discussions and find final solutions. In particular, organizing discussions, generating appropriate questions, and incorporating user feedback are time-consuming and complicated processes. Another issue is that discussions tend to stall due to a lack of proper recording and summarization. [Means for solving the problem]

[0005] The present invention relates to a problem-solving system that uses a user, a moderator generation AI, a question generation AI, and a secretary generation AI. The above-mentioned problem can be solved by a system that includes the following means.

[0006] A means for a user to input a task through a terminal;

[0007] a means for the server to receive a challenge input by a user;

[0008] A means for the moderator's generation AI to analyze the issues and generate solution topics,

[0009] A means for the question generation AI to generate questions based on the solution topic;

[0010] means for a user to answer questions generated through a terminal;

[0011] A means for the transcription AI to receive user responses and record and organize the discussion content;

[0012] A generative AI moderator will lead the discussion based on the user's answers and derive a solution.

[0013] a means for presenting the final solution to a user's terminal;

[0014] By integrating these methods, we provide a system that allows users to efficiently advance discussions and quickly find final solutions.

[0015] "User" refers to the person or organization that uses the system to input the problem, answer the questions generated by the AI, and verify the final solution.

[0016] A "terminal" is a device used by a user to access the system, and includes hardware such as a computer, smartphone, or tablet.

[0017] A "server" refers to a computer system that runs various AI processes at the back end of the system and manages user input and feedback.

[0018] "Challenge" refers to a specific problem or theme that a user inputs into the system to seek a solution.

[0019] "Generative AI for moderators" refers to artificial intelligence that analyzes the issues entered by users, generates solution topics to advance the discussion, and directs the progress of the discussion.

[0020] "Question generation AI" refers to artificial intelligence that generates appropriate questions for users based on the solution topic instructed by the moderator generation AI.

[0021] "Generative AI for transcription" refers to artificial intelligence that records and organizes user responses and the progress of discussions.

[0022] "Solution topics" refer to specific themes or segments for advancing the discussion, which are generated by the moderator generation AI by analyzing the issue.

[0023] "Questions" refer to inquiries generated by the question generation AI based on the solution topic, in order to dig deeper into the user's understanding and opinions.

[0024] A "solution" refers to a specific measure or means that is ultimately arrived at through discussion and presented to the user. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0026] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0027] First, the terms used in the following description will be explained.

[0028] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0029] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0030] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0031] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0032] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0037] 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.

[0038] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0039] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0040] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0043] 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.

[0044] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0045] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0046] The problem-solving system of the present invention is implemented using a server, a terminal, and various AIs. By using this system, users can solve problems efficiently and effectively. Below, the processing of the system's program is explained in detail in natural language.

[0047] overview

[0048] This system supports the process in which users input a problem via a terminal, and multiple generative AIs derive appropriate questions and solutions for that problem. The server plays a central role in the system, managing the processing of various AIs and the user interface.

[0049] Program processing

[0050] 1. Initial Setup:

[0051] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI.

[0052] 2. Enter your assignment:

[0053] The user inputs the problem to be solved in natural language through the terminal. For example, the user might input "How to consider a marketing strategy for new product development."

[0054] 3. Receiving assignments:

[0055] The device sends the user's input to the server, which receives it and passes it to the moderator generation AI.

[0056] 4. Issue analysis and topic generation:

[0057] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, topics such as "current situation analysis" and "target demographic selection" are generated.

[0058] 5. Question generation:

[0059] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0060] 6. Posing and answering questions:

[0061] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[0062] 7. Recording and Organizing:

[0063] The server passes the user's answers to the scribe-generative AI, which records and organizes them, creating a progress and detailed record of the discussion.

[0064] 8. Discussion process:

[0065] The moderator AI will advance the discussion based on the user's answers. It will then have the question generator AI generate questions based on the next topic. For example, the next question generated would be, "What type of demographic do you envision as the target demographic for your new product?"

[0066] 9. Restatement and Answer of Question:

[0067] The user again enters the answer to the question and sends it to the server via the terminal. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[0068] 10. Record and organize again:

[0069] The server receives the second response, and the transcription generation AI records it and organizes the progress of the discussion.

[0070] 11. Producing the final result:

[0071] The moderator AI generates the final solution, and the secretary AI organizes it to create the final result document to be presented to the user.

[0072] 12. Presentation of results:

[0073] The server sends the final result to the user's terminal, where the user can view it.

[0074] Specific examples

[0075] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[0076] 1. Initial Setup:

[0077] The server initializes the system and starts each AI.

[0078] 2. Enter your assignment:

[0079] The user inputs the assignment through the terminal.

[0080] 3. Receiving and analyzing assignments:

[0081] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[0082] 4. Question generation and answering:

[0083] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[0084] 5. Recording and Proceedings:

[0085] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[0086] 6. Re-answers and final results:

[0087] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[0088] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[0089] The processing flow will be explained below.

[0090] Step 1:

[0091] The server starts up and initializes the moderator generation AI, question generation AI, and secretary generation AI. This completes preparation for each AI to operate.

[0092] Step 2:

[0093] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[0094] Step 3:

[0095] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[0096] Step 4:

[0097] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[0098] Step 5:

[0099] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[0100] Step 6:

[0101] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[0102] Step 7:

[0103] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[0104] Step 8:

[0105] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0106] Step 9:

[0107] The server sends the generated question to the user's terminal, which displays the question to the user.

[0108] Step 10:

[0109] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[0110] Step 11:

[0111] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI.

[0112] Step 12:

[0113] The transcription-generating AI receives the user's responses, records and organizes them, and the organized discussion content is obtained.

[0114] Step 13:

[0115] The server feeds the user's answers back to the moderator generation AI and instructs it to move the discussion forward to the next topic.

[0116] Step 14:

[0117] The moderator generation AI instructs the question generation AI to generate questions to advance the discussion on the next topic (e.g., "target demographic selection").

[0118] Step 15:

[0119] A question generation AI generates new questions, such as, "What kind of target demographic do you envision for your new product?"

[0120] Step 16:

[0121] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[0122] Step 17:

[0123] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[0124] Step 18:

[0125] The device sends the user's answer to the server, which then passes the answer back to the scribe generation AI.

[0126] Step 19:

[0127] A generative AI scribe records new responses from users and updates the progress of the discussion.

[0128] Step 20:

[0129] A moderator generative AI will conclude discussions on all topics and come up with a final solution.

[0130] Step 21:

[0131] A generative AI transcription system will summarize all discussions and document the final solution.

[0132] Step 22:

[0133] The server sends the final result to the user's terminal, where the user can view the result.

[0134] The above are the specific processing steps of the system, which allow users to solve problems efficiently by following a series of steps.

[0135] Example 1

[0136] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] Today's users spend a lot of time and effort solving complex problems. Solving these problems requires extensive knowledge and experience, and in many cases, the advice and cooperation of experts is essential. Furthermore, the process of finding a solution requires efficient discussion and organization. However, many users do not have the means to receive appropriate assistance. As a result, problem solving is not carried out efficiently and effectively, and the burden on users increases.

[0138] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0139] In this invention, the server includes means for a user to input a problem through a terminal, means for the server to receive the problem input by the user, means for a moderator AI to analyze the problem and generate a solution topic, means for a question generator AI to generate a question based on the solution topic, means for the server to send the generated question to the user's terminal, means for the user to answer the question through the terminal, means for the server to send the user's answer to the clerk generator AI, means for the clerk generator AI to record and organize the user's answer, means for the moderator AI to proceed with the discussion based on the user's answer and derive a solution, and means for presenting the final solution to the user's terminal, thereby enabling users to solve problems efficiently and effectively.

[0140] "User" means an individual or entity that uses the system to enter a problem and request a solution.

[0141] A "terminal" is an electronic device that allows a user to input tasks and answer questions from the system.

[0142] A "problem" is a problem or question that a user wants solved.

[0143] The "server" is a central computer that manages the entire system and processes various generative AIs.

[0144] "Generative AI for hosting" is an artificial intelligence that analyzes users' issues and generates appropriate solution topics.

[0145] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[0146] "Generative AI for transcription" is an artificial intelligence that records and organizes users' responses.

[0147] "Solution topics" are important themes or items for discussion and solutions that the moderator generation AI has derived by analyzing the issue.

[0148] A "question" is a specific problem or inquiry generated by the question generation AI.

[0149] An "answer" is a response or opinion that a user enters in response to a question.

[0150] A "discussion" is a discussion for solving a problem that progresses based on the user's answers.

[0151] A "solution" is the final solution or proposal for solving a problem.

[0152] The problem-solving system of the present invention is implemented using a server, a terminal, and various generative AIs. By using this system, users can solve problems efficiently and effectively.

[0153] System configuration

[0154] The system consists of a terminal operated by the user, a server, and multiple generative AIs (moderator AI, question generator AI, and secretary generator AI). The server plays a central role in the system, managing the processing of the various generative AIs and the user interface.

[0155] Hardware and Software Use

[0156] The server is a high-performance computing device used to process each generative AI at high speed. For example, it is recommended that the server be equipped with a powerful CPU and GPU. Machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch are used to process the AI ​​models.

[0157] The terminal is used by the user to input tasks and answer generated questions. The terminal can be a PC, smartphone, tablet, etc. The software used can be a web browser or a dedicated application.

[0158] Program processing

[0159] The program processes as follows: First, the server initializes the system and starts the moderator generation AI, question generation AI, and clerk generation AI. Next, the user inputs the problem they want solved in natural language through their device. The device sends this input to the server, which receives it and passes it on to the moderator generation AI.

[0160] The moderator AI analyzes the problem and generates a solution topic. The question generator AI then generates specific questions based on the solution topic, which are sent to the user's device via the server. The user enters answers to the generated questions and sends them to the server via their device. The server receives these and passes them to the scribe generator AI for recording and organizing.

[0161] Specific examples

[0162] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be executed.

[0163] 1. Enter your assignment:

[0164] The user inputs "How to consider a marketing strategy for new product development" through the terminal.

[0165] 2. Receiving and analyzing assignments:

[0166] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[0167] 3. Question generation and answering:

[0168] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[0169] 4. Recording and Proceedings:

[0170] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[0171] 5. Re-answer and final result:

[0172] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[0173] The above is a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI. The AI ​​models and configuration files are loaded, and each AI module becomes ready for use.

[0177] Input: server

[0178] Output: Initialized AI for each generator

[0179] Step 2:

[0180] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[0181] Input: user

[0182] Output: The assignment text typed into the terminal

[0183] Step 3:

[0184] The terminal sends the assignment text entered by the user to the server. The terminal converts the input data into an appropriate format (e.g., JSON) and prepares it for transmission to the server.

[0185] Input: The assignment text entered into the terminal

[0186] Output: Issue data sent to the server

[0187] Step 4:

[0188] The server analyzes the received assignment data, extracts the JSON format data, and passes it to the moderator generation AI. During the analysis process, the necessary data fields are extracted and the format is organized.

[0189] Input: Issue data sent to the server

[0190] Output: Organized data passed to the moderator generation AI

[0191] Step 5:

[0192] The moderator's generative AI analyzes the problem and generates a solution topic. It uses natural language processing technology to break down the problem and extract the main themes and topics in text format.

[0193] Input: Organized data passed to the host generation AI

[0194] Output: Generated resolution topics

[0195] Step 6:

[0196] The question generation AI generates specific questions based on the solution topic generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[0197] Input: Generated resolution topic

[0198] Output: Generated questions

[0199] Step 7:

[0200] The server sends the questions generated by the question generation AI to the user's device, where the questions are formatted appropriately (e.g., text or JSON) and presented on the device.

[0201] Input: Generated question

[0202] Output: The question sent to the user's device

[0203] Step 8:

[0204] The user inputs answers to questions displayed on the terminal. For example, they might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[0205] Input: The answer entered by the user

[0206] Output: Answers typed into the terminal

[0207] Step 9:

[0208] The terminal sends the user's answer to the server, which converts the answer data into an appropriate format and sends it to the server.

[0209] Input: Answer typed into the terminal

[0210] Output: Response data sent to the server

[0211] Step 10:

[0212] The server receives the user's answer and passes it to the clerk's AI generator, which records the answer as text data and stores it in a database.

[0213] Input: Response data sent to the server

[0214] Output: Answer data passed to the transcription generation AI

[0215] Step 11:

[0216] The transcription AI records the response data and organizes the content of the responses, creating a detailed record of the progress of the discussion.

[0217] Input: Answer data passed to the transcription generation AI

[0218] Output: Recorded and organized response data

[0219] Step 12:

[0220] The moderator AI will proceed with the discussion based on the recorded and organized response data. The question generator AI will generate new questions based on the next topic. For example, a question might be generated: "What type of demographic do you envision as the target demographic for your new product?"

[0221] Input: Recorded and organized response data

[0222] Output: Next question

[0223] Step 13:

[0224] The server then sends the newly generated question to the user's terminal, and the user again enters an answer to the question and sends it again to the server via the terminal.

[0225] Input: The newly generated question

[0226] Output: The new question sent to the user's device.

[0227] Step 14:

[0228] The server again receives the user's input answers and sends them to the transcription AI for recording and organizing, thereby keeping the progress of the discussion up to date.

[0229] Input: Answer data sent by the user again

[0230] Output: Answer data sent again to the transcription generation AI

[0231] Step 15:

[0232] After the discussion progresses and a final solution is reached, the moderator AI documents it, and the scribe AI organizes this document and presents it to the user as the final result.

[0233] Input: Solution data recorded by the transcription-generative AI

[0234] Output: A cleaned up final result document

[0235] Step 16:

[0236] The server sends the final document to the user's device, where the user can download or view it for review.

[0237] Input: Final result document

[0238] Output: The final result document sent to the user's device.

[0239] (Application example 1)

[0240] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0241] There is a need to quickly and accurately resolve problems that occur on production lines. In particular, there is a problem in that it is difficult for production line operators to immediately identify the cause of the problem and find the optimal solution, so the problem is left unresolved for a long time, resulting in a decline in production efficiency.

[0242] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0243] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for presenting the final solution to the user's terminal; and means for supporting problem solving on the production line, including means for analyzing data from production equipment and proposing optimal solutions in real time. This makes it possible to quickly identify the cause of a problem when it occurs on the production line and obtain an optimal solution.

[0244] "User" refers to the person who operates the system, inputs problems, and receives solutions.

[0245] A "terminal" is a device used by a user to input tasks and check solutions, and includes PCs, tablets, smartphones, etc.

[0246] "Server" refers to the central system that manages the entire system, receives and analyzes data, and initializes and operates the generative AI.

[0247] "Generative AI for moderators" refers to an artificial intelligence system that analyzes input issues, generates solution topics, and supports the progress of discussions.

[0248] "Question generation AI" refers to an artificial intelligence system that generates appropriate questions based on the solution topic generated by the moderator generation AI.

[0249] "Generative AI for transcription" refers to an artificial intelligence system that receives user responses and records and organizes the content of the discussion.

[0250] "Solution topics" refer to specific items for consideration toward solutions that are generated as a result of the moderator generation AI analyzing the issue.

[0251] "Production equipment" refers to the machines and devices used on the production line, and refers to the hardware used to carry out the production process.

[0252] "Real-time" refers to the instantaneous receipt of data, analysis, and provision of solutions.

[0253] "Production line" refers to the series of processes that produce a product throughout the manufacturing process.

[0254] An "optimal solution" refers to the most effective and efficient way to address a problem, including suggestions derived by generative AI.

[0255] "Means to support problem-solving" refers to methods and technologies that use AI to analyze and propose solutions to various problems that arise on production lines.

[0256] In order to implement the present invention, the following system is constructed.

[0257] First, the server manages the entire system and initializes each generative AI. The server uses generative AI models such as GPT-4 (registered trademark) to analyze problems, generate solution topics, and generate questions.

[0258] Users input their issues through their devices, and this input data is sent to the server. The input issues are analyzed by a moderator generation AI, which generates a solution topic. For example, if the issue is "How to quickly resolve a malfunction that occurred on the production line," the generated solution topics include "adjusting the speed of the equipment" and "reviewing maintenance procedures."

[0259] Next, the question generation AI creates a question to be presented to the user based on the generated solution topic. For example, a specific question such as "What is the current speed setting of the conveyor belt?" is generated. This question is presented to the user via their terminal, and the user inputs an answer.

[0260] The user's answers are sent back to the server, where they are recorded and organized by the transcription generation AI. This process ensures that the progress of the discussion is properly recorded and necessary information is accumulated.

[0261] The moderator AI advances the discussion based on the user's answers, generates more detailed questions, and arrives at a final solution, which it then organizes in cooperation with the secretary AI. The final solution is then presented to the user via their device.

[0262] As a specific example, if the problem is an unstable conveyor belt speed, the moderator AI generates a topic about adjusting the conveyor belt speed, and the question generator AI generates the question, "What is the current speed setting?" After the user answers, "The current speed is 120 meters per minute," the scribe AI records this. Ultimately, the moderator AI derives the solution, "Calibrate the speed control unit," and presents this to the user.

[0263] An example prompt is:

[0264] "Problem: Conveyor belt speed is unstable.

[0265] Q: What is the current speed setting for the conveyor belt?

[0266] Answer: The current speed is 120 meters per minute.

[0267] By using this system, when a problem occurs on the production line, it becomes possible to quickly identify the cause and find the optimal solution.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] The server initializes the system and starts the moderator generation AI, question generation AI, and scribe generation AI. This prepares each AI to perform processes from problem analysis to presenting solutions. When the server performs its initial setup, it reads the initialization data, loads the AI ​​model, and sets it up so that it can be executed.

[0271] Step 2:

[0272] The user inputs the problem they want solved in natural language through a terminal. For example, they might input a problem such as "How can we quickly solve a problem that occurred on a production line?" This input data is then sent to the server by the terminal.

[0273] Step 3:

[0274] The server receives the task entered by the user and passes it to the moderator generation AI. The server converts the data into an appropriate format and provides it as input data to the moderator generation AI. This enables task analysis in the next step.

[0275] Step 4:

[0276] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, it generates topics such as "adjusting the speed of the equipment" or "reviewing maintenance procedures." Here, it performs natural language analysis of the input data and extracts and generates related topics.

[0277] Step 5:

[0278] The question generation AI generates questions based on the generated solution topic and sends them to the user's device via the server. For example, a question might be generated such as "What is the current speed setting of the conveyor belt?" In this step, the topic is explored in depth and appropriate questions are generated to gather more detailed information.

[0279] Step 6:

[0280] The user answers the questions generated through the terminal and sends the answer to the server. For example, the user might answer "Current speed is 120 meters per minute." The user's answer data is sent to the server and used for recording and organizing in the next step.

[0281] Step 7:

[0282] The scribe-generative AI receives the user's answers and records and organizes the discussion content. The server passes the answer data to the scribe-generative AI, which records the progress of the discussion and generates a log to move forward. For example, if the answer is "The current speed is 120 meters per minute," the content is recorded as text data.

[0283] Step 8:

[0284] The moderator AI advances the discussion based on the user's answers and generates questions based on the next topic. For example, the next question generated might be, "Have you calibrated the speed control unit?" Here, the user's answer data is analyzed and a process is performed to generate questions for the specific issues that need to be resolved next.

[0285] Step 9:

[0286] The user then enters the answer to the question again and sends it to the server via their device. For example, they might reply, "Yes, I have performed calibration. However, the speed is still unstable." This information is then sent to the server and analyzed again.

[0287] Step 10:

[0288] The server receives the second response, and the transcription AI records it and organizes the progress of the discussion. As mentioned above, the user's response is recorded as text data and used to advance the discussion in the next step.

[0289] Step 11:

[0290] The moderator AI generates a final solution, and the scribe AI organizes it and creates a final result document to present to the user. For example, a proposed solution might be, "Since the speed control unit remains unstable even after calibration, consider replacing the control unit."

[0291] Step 12:

[0292] The server sends the final results to the user's device, where the user can view them, thereby obtaining specific solutions and putting them into practice.

[0293] By following these steps, you can quickly find an effective solution to any problem that arises.

[0294] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0295] The problem-solving system of the present invention combines a server, terminals, various generative AIs, and an emotion engine to adjust the progress of appropriate questions and discussions based on the user's emotional state. The system's program processing is described in detail below.

[0296] overview

[0297] In this system, users input a task via a terminal, and the process involves a moderator AI, a question generator AI, a secretary AI, and an emotion engine working together to help solve the task. The server oversees the processing of these AIs and the emotion engine, and manages the user interface.

[0298] Program processing

[0299] 1. Initial Setup:

[0300] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine.

[0301] 2. Enter your assignment:

[0302] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[0303] 3. Receiving assignments:

[0304] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[0305] 4. Issue analysis and topic generation:

[0306] The moderator generation AI analyzes the problem and generates a solution topic, such as "current situation analysis" or "target demographic selection."

[0307] 5. Question generation:

[0308] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0309] 6. Posing and answering questions:

[0310] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[0311] 7. Emotion Analysis:

[0312] The server passes the user's response to the emotion engine, which analyzes the user's response and determines their emotion. For example, if the user is feeling anxious, the engine recognizes that emotion.

[0313] 8. Recording and Organizing:

[0314] The transcription-generating AI records and organizes the user's responses and analyzed emotions, resulting in organized discussion content and emotional data.

[0315] 9. Moderation of discussion:

[0316] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[0317] 10. Generate the following question:

[0318] The question generator AI generates new questions based on tailored instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[0319] 11. Posting and answering further questions:

[0320] The server sends the newly generated question to the user's device. The user then enters the answer to the question again and sends it to the server via their device. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[0321] 12. Emotion analysis and recording again:

[0322] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[0323] 13. Producing the final result:

[0324] The moderator's generative AI completes discussions on all topics and derives a final solution while taking into account emotional data.

[0325] 14. Presentation of results:

[0326] The transcription AI summarizes all the discussions and documents the final solution, which the server then sends to the user's device, where the user can review it.

[0327] Specific examples

[0328] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[0329] 1. Initial Setup:

[0330] The server initializes the system and starts each AI and emotion engine.

[0331] 2. Enter your assignment:

[0332] The user inputs the assignment through the terminal.

[0333] 3. Receiving and analyzing assignments:

[0334] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[0335] 4. Question generation and answering:

[0336] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[0337] 5. Sentiment analysis and discussion moderation:

[0338] The emotion engine analyzes the user's responses and recognizes, for example, whether they are feeling anxious. Based on this, the moderator generation AI adjusts the progress of the discussion and appropriately lowers the difficulty of the questions.

[0339] 6. Re-questioning and final results:

[0340] To the follow-up question, "What demographic do you envision as the target demographic for your new product?", the user answers, "We envision a demographic with a high affinity with young people." Taking into account the emotional data, the moderator's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people." The scribe's generative AI then organizes this and presents it to the user.

[0341] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently while taking into consideration their emotions.

[0342] The processing flow will be explained below.

[0343] Step 1:

[0344] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine, which completes preparations for each AI and emotion engine.

[0345] Step 2:

[0346] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[0347] Step 3:

[0348] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[0349] Step 4:

[0350] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[0351] Step 5:

[0352] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[0353] Step 6:

[0354] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[0355] Step 7:

[0356] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[0357] Step 8:

[0358] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0359] Step 9:

[0360] The server sends the generated question to the user's terminal, which displays the question to the user.

[0361] Step 10:

[0362] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[0363] Step 11:

[0364] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI and emotion engine.

[0365] Step 12:

[0366] The emotion engine analyzes the user's responses and assesses their emotions, for example recognizing that they are feeling stressed or anxious.

[0367] Step 13:

[0368] A generative AI transcription system records and organizes the user's responses and the emotions analyzed by the emotion engine.

[0369] Step 14:

[0370] Based on the analysis results of the emotion engine, the server feeds back the user's emotional state to the moderator AI, which then adjusts the progress of the discussion and instructs the question generator AI to generate questions that are adapted to the user's state.

[0371] Step 15:

[0372] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[0373] Step 16:

[0374] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[0375] Step 17:

[0376] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[0377] Step 18:

[0378] The device sends the user's response to the server, which then passes the response back to the transcription AI and emotion engine. The emotion engine then analyzes the user's emotions and adjusts the discussion again as necessary.

[0379] Step 19:

[0380] A generative AI scribe records new responses from users and updates the progress of the discussion.

[0381] Step 20:

[0382] A generative AI moderator will complete the discussion on all topics and come up with a final solution while taking into account sentiment data.

[0383] Step 21:

[0384] A generative AI transcription system will summarize all discussions and document the final solution.

[0385] Step 22:

[0386] The server sends the final result to the user's terminal, where the user can view the result.

[0387] These are the specific processing steps of the system that combines the emotion engine, which allows for efficient problem solving while responding to the user's emotional state.

[0388] Example 2

[0389] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0390] Conventional problem-solving support systems have the problem of being unable to take into account the user's emotional state and therefore unable to provide appropriate support to users who feel stressed or anxious. Furthermore, the progress of the discussion and adjustment of questions are uniform, making it difficult to provide appropriate support according to the individual situation of each user.

[0391] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a task through a terminal; a means for receiving the task input by the user; a means for a moderator generation system AI to analyze the task and generate a solution topic; a means for a question generation system AI to generate a question based on the solution topic; a means for an emotion engine to analyze emotions from the user's answers; a means for the user to answer the question generated through the terminal; a means for a scribe generation system AI to receive the user's answers and the analyzed emotions and record and organize the discussion content; a means for the moderator generation system AI to proceed with the discussion based on the user's answers and the analyzed emotions and derive a solution; and a means for presenting the final solution to the user's terminal. This makes it possible to conduct appropriate and effective questions and discussion while taking into account the user's emotional state.

[0392] A "user" is a person who uses the system to input tasks and answer questions.

[0393] A "terminal" is a hardware device on which a user inputs tasks and answers questions, and specifically is a computer device such as a PC, smartphone, or tablet.

[0394] The "server" is a computer system that oversees various generative AIs and emotion engines and manages the user interface.

[0395] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solution topics, and manages the progress of the discussion.

[0396] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[0397] An "emotion engine" is software that analyzes the emotions from users' responses and obtains emotional data.

[0398] The "generative AI for notation" is an artificial intelligence that receives user responses and emotional data analyzed by the emotion engine, and records and organizes the content of the discussion.

[0399] "Solution topics" are specific topics and steps for solving problems that are generated by the moderator generation AI by analyzing the user's issues.

[0400] A "question" is a specific question that the question generation AI generates based on the solution topic for the user to answer.

[0401] "Emotions" are psychological states such as stress, anxiety, or joy that a user expresses when answering questions.

[0402] The "solution" is the final problem-solving method that the moderator's generative AI derives based on the user's answers and emotions.

[0403] MODE FOR CARRYING OUT THE INVENTION

[0404] This invention is a system in which a user inputs a problem through a terminal, a server receives the problem, and various generative AIs and emotion engines work together to support problem solving. This system is characterized by its ability to adjust appropriate questions and discussion progress in real time, taking into account the user's emotional state. Detailed modes for implementing the invention are described below.

[0405] Hardware and software used

[0406] 1. Server

[0407] The server oversees various generative AIs (hosting AI, question generation AI, and secretary generation AI) and the emotion engine, and manages the user interface. The server communicates with devices using HTTP requests and WebSockets.

[0408] 2. Terminal

[0409] A terminal is a hardware device where users input tasks and answer questions. Terminals can be PCs, smartphones, tablets, etc. The terminal provides a user interface and displays information from the server.

[0410] 3. Generative AI and Emotion Engines

[0411] Generative AI for moderators: Analyzes the issues entered by users and generates solutions. Examples of AI models used include GPT-4.

[0412] Question generation AI: Generates specific questions based on the topics generated by the moderator generation AI.

[0413] Generative AI for transcription: Records user responses and emotional data, and organizes the discussion content.

[0414] Emotion engine: Software that analyzes emotions from user responses. For example, IBM Watson® Tone Analyzer.

[0415] System processing flow

[0416] The specific flow of the system processing proceeds as follows:

[0417] 1. Initial Setup

[0418] The server initializes the system and starts each generative AI (for moderator, questioner, and secretary) and emotion engine.

[0419] 2. Enter your assignment

[0420] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[0421] 3. Receiving assignments

[0422] The device sends the user's input data to the server, which receives the data and passes it to the moderator generation AI.

[0423] 4. Issue analysis and topic generation

[0424] The moderator's AI analyzes the problem and generates relevant solution topics, such as "current situation analysis" and "target demographic selection."

[0425] 5. Question Generation

[0426] The question generator AI creates questions based on the topics generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[0427] 6. Posing and answering questions

[0428] The server sends the generated question to the user's device. The device displays the question to the user, and the user inputs an answer. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition." The device then sends the answer to the server.

[0429] 7. Emotion Analysis

[0430] The server passes the user's response to an emotion engine, which analyzes the user's emotions, for example, recognizing that the user is feeling anxious.

[0431] 8. Recording and Organizing

[0432] A generative AI transcription system records and organizes users' responses and analyzed emotions, thereby saving the discussion history and emotional data.

[0433] 9. Moderation of discussions

[0434] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[0435] 10. Next Question Generation

[0436] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[0437] 11. Posting and answering follow-up questions

[0438] The server sends the newly generated question to the user's device. The user then enters an answer to the question, for example, "We are targeting a demographic with a high affinity with young people." The device then sends this answer to the server.

[0439] 12. Re-analyzing and recording emotions

[0440] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[0441] 13. Producing the final result

[0442] The moderator's generative AI completes the discussion for all topics and derives a final solution while taking into account the sentiment data. For example, by integrating user responses and sentiment, it might suggest a solution such as "strengthen online advertising for new products and run a campaign targeted at younger generations."

[0443] 14. Presentation of Results

[0444] The transcription AI summarizes all the discussions and documents the final solution, and the server sends the final result to the user's device, where the user can review it.

[0445] Specific examples

[0446] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[0447] The user enters the assignment through the terminal.

[0448] The device sends the assignment to the server.

[0449] The server receives the assignment and passes it to the moderator generation AI.

[0450] The moderator generation AI analyzes the issues and generates topics such as "current situation analysis" and "target demographic selection."

[0451] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?"

[0452] A user responded, "Our current strength is online advertising, and our weakness is low brand recognition."

[0453] The emotion engine analyzes the user's responses and recognizes that they are feeling anxious.

[0454] The moderator's generative AI will use this information to adjust the progress of the discussion.

[0455] The question generation AI generates a follow-up question: "What type of target demographic do you envision for your new product?"

[0456] Users responded that they are "targeting a demographic with a high affinity with younger generations."

[0457] The host's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people."

[0458] A generative AI system for writing organizes this information and presents it to the user.

[0459] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] The server initializes the system and starts the moderator generation AI, question generation AI, clerk generation AI, and emotion engine. Specifically, it creates execution instances of each AI and emotion engine and allocates the necessary resources. This prepares all modules to run. The input is the system startup command, and the output is the initialization completion state of each AI and emotion engine.

[0462] Step 2:

[0463] The user inputs a problem through the terminal. A text input field is displayed on the terminal, and the user inputs the problem in natural language. For example, the problem is "How to consider a marketing strategy for new product development." The input is the user's problem text, and the output is the terminal sending the problem text to the server.

[0464] Step 3:

[0465] The device sends the user's input to the server. The server receives the input data using a communication protocol (e.g., HTTP request, WebSocket). Specifically, it parses the JSON-formatted data sent from the device and obtains the user's input assignment. The input is the user's assignment text, and the output is the assignment data received by the server.

[0466] Step 4:

[0467] The server passes the received problem data to the moderator generation AI, which analyzes this data and generates solution topics. For example, it uses natural language processing technology to generate topics such as "current situation analysis" and "target demographic selection." The input is the problem text, and the output is a list of generated topics.

[0468] Step 5:

[0469] The question generation AI generates specific questions based on the topics generated by the moderator generation AI. Specifically, it uses a generative AI model (e.g., GPT-4) to construct appropriate questions for each topic. For example, it generates a question such as, "What are the strengths and weaknesses of your current marketing strategy?" The input is a list of topics, and the output is a list of generated questions.

[0470] Step 6:

[0471] The server sends the generated questions to the user's terminal. The terminal displays the questions to the user so that the user can answer them. Specifically, the questions are displayed on the screen and the user enters text into an answer input field. The input is a list of questions, and the output is the questions displayed to the user.

[0472] Step 7:

[0473] The user answers questions through the device. The device collects the user's answers and sends them back to the server. For example, if the user answers "Our current strength is online advertising, and our weakness is low brand recognition," the text is collected. The input is the user's answer to the question, and the output is the answer data sent to the server.

[0474] Step 8:

[0475] The server passes the user's answer to the emotion engine, which analyzes the user's answer text to determine the emotion. Specifically, it uses natural language processing to identify the user's emotional state (e.g., anxiety, joy, anger, etc.). The input is the answer text, and the output is the analyzed emotion data.

[0476] Step 9:

[0477] The transcription-generative AI records and organizes user responses and analyzed emotions. Specifically, it stores responses and emotional data in a database and creates a document to organize the discussion content. The input is the response text and emotional data, and the output is the recorded and organized discussion content.

[0478] Step 10:

[0479] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. Specifically, if the user is feeling stressed, it will take measures such as lowering the difficulty of the questions. The input is emotional data, and the output is instructions for adjusting the progress of the discussion.

[0480] Step 11:

[0481] The question generation AI generates new questions based on the adjusted instructions. For example, it generates a question like, "What type of demographic is the new product targeted for?" The input is the adjusted instructions, and the output is the newly generated question.

[0482] Step 12:

[0483] The server sends a new question to the user's terminal, which displays the question to the user and prompts the user to enter the answer again. The input is the new question, and the output is the question displayed to the user.

[0484] Step 13:

[0485] The user answers a new question and sends it to the server via their device. For example, they might answer, "We are targeting a demographic with a high affinity with young people." The input is the user's new answer, and the output is the answer data sent to the server.

[0486] Step 14:

[0487] The server passes the new response back to the emotion engine, which analyzes it. The analysis results are then passed back to the transcription generation AI, which records the discussion content and emotion data. The input is the new response data, and the output is the analyzed emotion data.

[0488] Step 15:

[0489] The moderator generative AI completes the discussion for all topics and derives a final solution. Specifically, it integrates user responses and emotional data to propose an appropriate solution. The input is all discussion data and emotional data, and the output is the final solution.

[0490] Step 16:

[0491] The transcription generator AI documents the final solution, and the server sends it to the user's device. The user then checks the final result on their device. The input is the final solution, and the output is the result presented to the user.

[0492] (Application example 2)

[0493] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0494] Conventional problem-solving systems proceed without taking into account the user's emotional state, which can cause stress or anxiety for the user, making efficient problem-solving difficult. Furthermore, particularly on online shopping sites, the user's emotions during the purchasing experience cannot be properly addressed, potentially resulting in a decrease in satisfaction. Therefore, there is a need for a system that can analyze the user's emotional state and, based on that, guide the discussion and provide appropriate information.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0496] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for an emotion engine to analyze emotions from the users' answers and adjust the progress of the discussion based on that. This makes it possible to solve problems more efficiently while taking the users' emotional state into consideration, and to improve user satisfaction, especially on online shopping sites.

[0497] A "user" is an entity that uses the system to input questions and provide answers.

[0498] A "terminal" is a device that allows a user to input tasks and answer questions, and includes smartphones, personal computers, etc.

[0499] The "server" is a computer system that oversees the entire system and manages the processing of various generative AIs and emotion engines.

[0500] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solutions, and advances the discussion.

[0501] "Question generation AI" is an artificial intelligence that generates specific questions based on the solution topics generated by the moderator generation AI.

[0502] "Generative notation AI" is an artificial intelligence that receives user responses, records and organizes the discussion, and documents the final solution.

[0503] The "emotion engine" is an engine that analyzes the user's emotional state from their answers and inputs, and adjusts the progress of the discussion and the content of the questions based on that.

[0504] "Solution topics" are specific discussion themes or question subjects that are generated by the moderator generation AI by analyzing the issues.

[0505] A "question" is a question posed to the user that is generated by the question generation AI based on the solution topic.

[0506] An "answer" is information provided by a user by answering a question generated through a terminal.

[0507] A "discussion" is a process of discussion formed through an exchange of questions and answers between the user and the generative AI.

[0508] A "solution" is the final answer or proposal to the user's problem that is arrived at as a result of the discussion.

[0509] The system embodying this invention combines a server, a terminal, various generative AIs, and an emotion engine to support problem solving while taking into account the user's emotional state. This system contributes to improving the user experience, especially on online shopping sites.

[0510] System configuration

[0511] The system's hardware configuration includes the smartphone or PC used by the user and a server. The software configuration includes an AI for generating moderators, an AI for generating questions, an AI for generating notes, and an emotion engine. Specifically, the following software is used:

[0512] Emotion engine: e.g., emotion_recognition library

[0513] Generative AI: OpenAI's GPT model

[0514] Program processing

[0515] During the initial setup phase, the server initializes various generative AIs and emotion engines. When a user inputs a task via their device, the task is received by the server and passed to the moderator generative AI. The moderator generative AI analyzes the user's input and generates a solution topic. For example, if the discussion is about "marketing strategies for a new product," topics such as "current situation analysis" and "target demographic selection" will be generated.

[0516] The question generation AI then generates specific questions based on the topic being addressed. These questions are sent to the user's device, and the user inputs the corresponding answers. For example, in response to the question, "What are the strengths and weaknesses of your current marketing strategy?", the AI ​​will answer, "The current strength is online advertising, and the weakness is low brand awareness."

[0517] The user's answers are analyzed by an emotion engine to determine the emotions the user is feeling (e.g., anxiety or stress). The emotion data analyzed by the emotion engine is fed back to the moderator generation AI, which adjusts the progress of the discussion based on that. For example, if the user is feeling high stress, the difficulty of the questions may be lowered.

[0518] Specific examples

[0519] Specifically, imagine a situation where a user feels uneasy after reading a product review while shopping online. When the user types the question, "What are the advantages of this product?", the emotion engine detects the user's uneasiness, and the generative AI provides information that provides reassurance.

[0520] Example prompt sentence:

[0521] text

[0522] A user is concerned about this statement: 'I've been reading the reviews for this product and they're mostly negative.' How should I respond?

[0523] This allows the system to provide a better purchasing experience while taking into consideration the user's feelings. The generative transcription AI also organizes the discussions and documents the final solution, which is then presented to the user. For example, the system may arrive at a solution such as "strengthen online advertising for new products and run a campaign specifically targeted at younger generations."

[0524] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0525] Step 1:

[0526] The server initializes the system and starts the moderator generation AI, question generation AI, clerk generation AI, and emotion engine. During this initialization step, the environment is configured and the necessary libraries and modules are loaded for the system to operate properly. Specifically, the parameters of the various AI models are set and the emotion engine's analysis model is loaded. The necessary network resources are also secured and connections are prepared.

[0527] Step 2:

[0528] The user inputs the problem they want to solve in natural language through the device. For example, they might input "I would like to consider a marketing strategy for a new product." The device then sends this input to the server. At this stage, the text data entered by the user becomes the input, and the text data sent from the device to the server becomes the output.

[0529] Step 3:

[0530] The server receives the task entered by the user and passes it to the moderator generation AI. The moderator generation AI analyzes the task and generates a solution topic. For example, it generates topics such as "current situation analysis" and "target demographic selection." In this step, the user input (natural language text) received by the server becomes the input, and the generated solution topic (a list in natural language) becomes the output. Specifically, it uses natural language processing technology to break down the task into topics.

[0531] Step 4:

[0532] The question generation AI generates specific questions based on the solution topic generated by the moderator generation AI. For example, a question such as "What are the strengths and weaknesses of your current marketing strategy?" is generated. In this step, the solution topic is input, and a specific question (natural language text) based on it is output.

[0533] Step 5:

[0534] The server sends the generated question to the user's device. The user inputs an answer to the question through the device. For example, the user might input an answer such as, "Our current strength is online advertising, and our weakness is low brand recognition." In this step, the generated question is the input, and the answer (natural language text) entered by the user is the output. Specifically, the question is notified to the user's device, and the user answers it.

[0535] Step 6:

[0536] The server receives the user's response and passes it to the emotion engine for analysis. The emotion engine analyzes the user's emotional state from the response and returns emotion data, such as "anxiety" or "stress," to the server. In this step, the user's response (natural language text) is input, and the analyzed emotion data (e.g., anxiety or stress) is output. Specifically, the emotion analysis model analyzes the text data and labels the emotion.

[0537] Step 7:

[0538] The analyzed emotion data is passed to the transcription-generating AI, which records and organizes the user's responses and emotion data. In this step, emotion data and user responses are input, and organized discussion content (natural language text) is output. Specifically, responses and emotion data are recorded in chronological order.

[0539] Step 8:

[0540] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it lowers the difficulty of the next question. In this step, emotional data is input, and adjusted instructions for the discussion progress are output. Specifically, the generative AI model makes adjustments according to the emotional data.

[0541] Step 9:

[0542] The question generation AI generates new questions based on the adjusted instructions. For example, a question such as, "What marketing strategy are you considering next?" is generated. In this step, the adjusted discussion flow instructions are the input, and the generated new question (natural language text) is the output.

[0543] Step 10:

[0544] The server sends the newly generated question to the user's device. The user again enters an answer to the question and sends it to the server via the device. For example, the user might enter an answer such as, "We expect the target demographic for our new product to be young people." In this step, the new question is the input, and the user's answer (natural language text) is the output.

[0545] Step 11:

[0546] The server passes the new answers to the emotion engine for re-analysis, and the transcription generation AI re-records the discussion content together with the analyzed emotion data. In this step, the user's new answers are input, and the reorganized discussion content is output. Specifically, the answers and emotion data are re-recorded, and the discussion content is periodically updated.

[0547] Step 12:

[0548] The moderator generative AI completes the discussion for all topics and derives a final solution while taking into account the emotional data. In this step, all discussion content and emotional data are input, and the final solution is output. Specifically, the generative AI model performs a comprehensive analysis and proposes an appropriate solution to the user.

[0549] Step 13:

[0550] The scribe generation AI summarizes the entire discussion and documents the final solution. The server sends the final result to the user's device, where the user confirms the result. In this step, the discussion content and the solution are input, and the summarized final solution (document) is output. In concrete terms, the generated document is presented to the user, who then confirms it.

[0551] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0552] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0553] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0554] [Second embodiment]

[0555] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0556] 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.

[0557] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0558] 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.

[0559] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0560] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0561] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0562] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0563] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0564] 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.

[0565] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0566] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0567] The problem-solving system of the present invention is implemented using a server, a terminal, and various AIs. By using this system, users can solve problems efficiently and effectively. Below, the processing of the system's program is explained in detail in natural language.

[0568] overview

[0569] This system supports the process in which users input a problem via a terminal, and multiple generative AIs derive appropriate questions and solutions for that problem. The server plays a central role in the system, managing the processing of various AIs and the user interface.

[0570] Program processing

[0571] 1. Initial Setup:

[0572] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI.

[0573] 2. Enter your assignment:

[0574] The user inputs the problem to be solved in natural language through the terminal. For example, the user might input "How to consider a marketing strategy for new product development."

[0575] 3. Receiving assignments:

[0576] The device sends the user's input to the server, which receives it and passes it to the moderator generation AI.

[0577] 4. Issue analysis and topic generation:

[0578] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, topics such as "current situation analysis" and "target demographic selection" are generated.

[0579] 5. Question generation:

[0580] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0581] 6. Posing and answering questions:

[0582] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[0583] 7. Recording and Organizing:

[0584] The server passes the user's answers to the scribe-generative AI, which records and organizes them, creating a progress and detailed record of the discussion.

[0585] 8. Discussion process:

[0586] The moderator AI will advance the discussion based on the user's answers. It will then have the question generator AI generate questions based on the next topic. For example, the next question generated would be, "What type of demographic do you envision as the target demographic for your new product?"

[0587] 9. Restatement and Answer of Question:

[0588] The user again enters the answer to the question and sends it to the server via the terminal. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[0589] 10. Record and organize again:

[0590] The server receives the second response, and the transcription generation AI records it and organizes the progress of the discussion.

[0591] 11. Producing the final result:

[0592] The moderator AI generates the final solution, and the secretary AI organizes it to create the final result document to be presented to the user.

[0593] 12. Presentation of results:

[0594] The server sends the final result to the user's terminal, where the user can view it.

[0595] Specific examples

[0596] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[0597] 1. Initial Setup:

[0598] The server initializes the system and starts each AI.

[0599] 2. Enter your assignment:

[0600] The user inputs the assignment through the terminal.

[0601] 3. Receiving and analyzing assignments:

[0602] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[0603] 4. Question generation and answering:

[0604] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[0605] 5. Recording and Proceedings:

[0606] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[0607] 6. Re-answers and final results:

[0608] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[0609] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[0610] The processing flow will be explained below.

[0611] Step 1:

[0612] The server starts up and initializes the moderator generation AI, question generation AI, and secretary generation AI. This completes preparation for each AI to operate.

[0613] Step 2:

[0614] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[0615] Step 3:

[0616] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[0617] Step 4:

[0618] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[0619] Step 5:

[0620] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[0621] Step 6:

[0622] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[0623] Step 7:

[0624] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[0625] Step 8:

[0626] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0627] Step 9:

[0628] The server sends the generated question to the user's terminal, which displays the question to the user.

[0629] Step 10:

[0630] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[0631] Step 11:

[0632] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI.

[0633] Step 12:

[0634] The transcription-generating AI receives the user's responses, records and organizes them, and the organized discussion content is obtained.

[0635] Step 13:

[0636] The server feeds the user's answers back to the moderator generation AI and instructs it to move the discussion forward to the next topic.

[0637] Step 14:

[0638] The moderator generation AI instructs the question generation AI to generate questions to advance the discussion on the next topic (e.g., "target demographic selection").

[0639] Step 15:

[0640] A question generation AI generates new questions, such as, "What kind of target demographic do you envision for your new product?"

[0641] Step 16:

[0642] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[0643] Step 17:

[0644] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[0645] Step 18:

[0646] The device sends the user's answer to the server, which then passes the answer back to the scribe generation AI.

[0647] Step 19:

[0648] A generative AI scribe records new responses from users and updates the progress of the discussion.

[0649] Step 20:

[0650] A moderator generative AI will conclude discussions on all topics and come up with a final solution.

[0651] Step 21:

[0652] A generative AI transcription system will summarize all discussions and document the final solution.

[0653] Step 22:

[0654] The server sends the final result to the user's terminal, where the user can view the result.

[0655] The above are the specific processing steps of the system, which allow users to solve problems efficiently by following a series of steps.

[0656] Example 1

[0657] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0658] Today's users spend a lot of time and effort solving complex problems. Solving these problems requires extensive knowledge and experience, and in many cases, the advice and cooperation of experts is essential. Furthermore, the process of finding a solution requires efficient discussion and organization. However, many users do not have the means to receive appropriate assistance. As a result, problem solving is not carried out efficiently and effectively, and the burden on users increases.

[0659] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0660] In this invention, the server includes means for a user to input a problem through a terminal, means for the server to receive the problem input by the user, means for a moderator AI to analyze the problem and generate a solution topic, means for a question generator AI to generate a question based on the solution topic, means for the server to send the generated question to the user's terminal, means for the user to answer the question through the terminal, means for the server to send the user's answer to the clerk generator AI, means for the clerk generator AI to record and organize the user's answer, means for the moderator AI to proceed with the discussion based on the user's answer and derive a solution, and means for presenting the final solution to the user's terminal, thereby enabling users to solve problems efficiently and effectively.

[0661] "User" means an individual or entity that uses the system to enter a problem and request a solution.

[0662] A "terminal" is an electronic device that allows a user to input tasks and answer questions from the system.

[0663] A "problem" is a problem or question that a user wants solved.

[0664] The "server" is a central computer that manages the entire system and processes various generative AIs.

[0665] "Generative AI for hosting" is an artificial intelligence that analyzes users' issues and generates appropriate solution topics.

[0666] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[0667] "Generative AI for transcription" is an artificial intelligence that records and organizes users' responses.

[0668] "Solution topics" are important themes or items for discussion and solutions that the moderator generation AI has derived by analyzing the issue.

[0669] A "question" is a specific problem or inquiry generated by the question generation AI.

[0670] An "answer" is a response or opinion that a user enters in response to a question.

[0671] A "discussion" is a discussion for solving a problem that progresses based on the user's answers.

[0672] A "solution" is the final solution or proposal for solving a problem.

[0673] The problem-solving system of the present invention is implemented using a server, a terminal, and various generative AIs. By using this system, users can solve problems efficiently and effectively.

[0674] System configuration

[0675] The system consists of a terminal operated by the user, a server, and multiple generative AIs (moderator AI, question generator AI, and secretary generator AI). The server plays a central role in the system, managing the processing of the various generative AIs and the user interface.

[0676] Hardware and Software Use

[0677] The server is a high-performance computing device used to process each generative AI at high speed. For example, it is recommended that the server be equipped with a powerful CPU and GPU. Machine learning frameworks such as TensorFlow and PyTorch are used to process the AI ​​models.

[0678] The terminal is used by the user to input tasks and answer generated questions. The terminal can be a PC, smartphone, tablet, etc. The software used can be a web browser or a dedicated application.

[0679] Program processing

[0680] The program processes as follows: First, the server initializes the system and starts the moderator generation AI, question generation AI, and clerk generation AI. Next, the user inputs the problem they want solved in natural language through their device. The device sends this input to the server, which receives it and passes it on to the moderator generation AI.

[0681] The moderator AI analyzes the problem and generates a solution topic. The question generator AI then generates specific questions based on the solution topic, which are sent to the user's device via the server. The user enters answers to the generated questions and sends them to the server via their device. The server receives these and passes them to the scribe generator AI for recording and organizing.

[0682] Specific examples

[0683] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be executed.

[0684] 1. Enter your assignment:

[0685] The user inputs "How to consider a marketing strategy for new product development" through the terminal.

[0686] 2. Receiving and analyzing assignments:

[0687] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[0688] 3. Question generation and answering:

[0689] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[0690] 4. Recording and Proceedings:

[0691] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[0692] 5. Re-answer and final result:

[0693] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[0694] The above is a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[0695] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0696] Step 1:

[0697] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI. The AI ​​models and configuration files are loaded, and each AI module becomes ready for use.

[0698] Input: server

[0699] Output: Initialized AI for each generator

[0700] Step 2:

[0701] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[0702] Input: user

[0703] Output: The assignment text typed into the terminal

[0704] Step 3:

[0705] The terminal sends the assignment text entered by the user to the server. The terminal converts the input data into an appropriate format (e.g., JSON) and prepares it for transmission to the server.

[0706] Input: The assignment text entered into the terminal

[0707] Output: Issue data sent to the server

[0708] Step 4:

[0709] The server analyzes the received assignment data, extracts the JSON format data, and passes it to the moderator generation AI. During the analysis process, the necessary data fields are extracted and the format is organized.

[0710] Input: Issue data sent to the server

[0711] Output: Organized data passed to the moderator generation AI

[0712] Step 5:

[0713] The moderator's generative AI analyzes the problem and generates a solution topic. It uses natural language processing technology to break down the problem and extract the main themes and topics in text format.

[0714] Input: Organized data passed to the host generation AI

[0715] Output: Generated resolution topics

[0716] Step 6:

[0717] The question generation AI generates specific questions based on the solution topic generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[0718] Input: Generated resolution topic

[0719] Output: Generated questions

[0720] Step 7:

[0721] The server sends the questions generated by the question generation AI to the user's device, where the questions are formatted appropriately (e.g., text or JSON) and presented on the device.

[0722] Input: Generated question

[0723] Output: The question sent to the user's device

[0724] Step 8:

[0725] The user inputs answers to questions displayed on the terminal. For example, they might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[0726] Input: The answer entered by the user

[0727] Output: Answers typed into the terminal

[0728] Step 9:

[0729] The terminal sends the user's answer to the server, which converts the answer data into an appropriate format and sends it to the server.

[0730] Input: Answer typed into the terminal

[0731] Output: Response data sent to the server

[0732] Step 10:

[0733] The server receives the user's answer and passes it to the clerk's AI generator, which records the answer as text data and stores it in a database.

[0734] Input: Response data sent to the server

[0735] Output: Answer data passed to the transcription generation AI

[0736] Step 11:

[0737] The transcription AI records the response data and organizes the content of the responses, creating a detailed record of the progress of the discussion.

[0738] Input: Answer data passed to the transcription generation AI

[0739] Output: Recorded and organized response data

[0740] Step 12:

[0741] The moderator AI will proceed with the discussion based on the recorded and organized response data. The question generator AI will generate new questions based on the next topic. For example, a question might be generated: "What type of demographic do you envision as the target demographic for your new product?"

[0742] Input: Recorded and organized response data

[0743] Output: Next question

[0744] Step 13:

[0745] The server then sends the newly generated question to the user's terminal, and the user again enters an answer to the question and sends it again to the server via the terminal.

[0746] Input: The newly generated question

[0747] Output: The new question sent to the user's device.

[0748] Step 14:

[0749] The server again receives the user's input answers and sends them to the transcription AI for recording and organizing, thereby keeping the progress of the discussion up to date.

[0750] Input: Answer data sent by the user again

[0751] Output: Answer data sent again to the transcription generation AI

[0752] Step 15:

[0753] After the discussion progresses and a final solution is reached, the moderator AI documents it, and the scribe AI organizes this document and presents it to the user as the final result.

[0754] Input: Solution data recorded by the transcription-generative AI

[0755] Output: A cleaned up final result document

[0756] Step 16:

[0757] The server sends the final document to the user's device, where the user can download or view it for review.

[0758] Input: Final result document

[0759] Output: The final result document sent to the user's device.

[0760] (Application example 1)

[0761] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] There is a need to quickly and accurately resolve problems that occur on production lines. In particular, there is a problem in that it is difficult for production line operators to immediately identify the cause of the problem and find the optimal solution, so the problem is left unresolved for a long time, resulting in a decline in production efficiency.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0764] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for presenting the final solution to the user's terminal; and means for supporting problem solving on the production line, including means for analyzing data from production equipment and proposing optimal solutions in real time. This makes it possible to quickly identify the cause of a problem when it occurs on the production line and obtain an optimal solution.

[0765] "User" refers to the person who operates the system, inputs problems, and receives solutions.

[0766] A "terminal" is a device used by a user to input tasks and check solutions, and includes PCs, tablets, smartphones, etc.

[0767] "Server" refers to the central system that manages the entire system, receives and analyzes data, and initializes and operates the generative AI.

[0768] "Generative AI for moderators" refers to an artificial intelligence system that analyzes input issues, generates solution topics, and supports the progress of discussions.

[0769] "Question generation AI" refers to an artificial intelligence system that generates appropriate questions based on the solution topic generated by the moderator generation AI.

[0770] "Generative AI for transcription" refers to an artificial intelligence system that receives user responses and records and organizes the content of the discussion.

[0771] "Solution topics" refer to specific items for consideration toward solutions that are generated as a result of the moderator generation AI analyzing the issue.

[0772] "Production equipment" refers to the machines and devices used on the production line, and refers to the hardware used to carry out the production process.

[0773] "Real-time" refers to the instantaneous receipt of data, analysis, and provision of solutions.

[0774] "Production line" refers to the series of processes that produce a product throughout the manufacturing process.

[0775] An "optimal solution" refers to the most effective and efficient way to address a problem, including suggestions derived by generative AI.

[0776] "Means to support problem-solving" refers to methods and technologies that use AI to analyze and propose solutions to various problems that arise on production lines.

[0777] In order to implement the present invention, the following system is constructed.

[0778] First, the server manages the entire system and initializes each generative AI. The server uses generative AI models such as GPT-4 to analyze problems, generate solution topics, and generate questions.

[0779] Users input their issues through their devices, and this input data is sent to the server. The input issues are analyzed by a moderator generation AI, which generates a solution topic. For example, if the issue is "How to quickly resolve a malfunction that occurred on the production line," the generated solution topics include "adjusting the speed of the equipment" and "reviewing maintenance procedures."

[0780] Next, the question generation AI creates a question to be presented to the user based on the generated solution topic. For example, a specific question such as "What is the current speed setting of the conveyor belt?" is generated. This question is presented to the user via their terminal, and the user inputs an answer.

[0781] The user's answers are sent back to the server, where they are recorded and organized by the transcription generation AI. This process ensures that the progress of the discussion is properly recorded and necessary information is accumulated.

[0782] The moderator AI advances the discussion based on the user's answers, generates more detailed questions, and arrives at a final solution, which it then organizes in cooperation with the secretary AI. The final solution is then presented to the user via their device.

[0783] As a specific example, if the problem is an unstable conveyor belt speed, the moderator AI generates a topic about adjusting the conveyor belt speed, and the question generator AI generates the question, "What is the current speed setting?" After the user answers, "The current speed is 120 meters per minute," the scribe AI records this. Ultimately, the moderator AI derives the solution, "Calibrate the speed control unit," and presents this to the user.

[0784] An example prompt is:

[0785] "Problem: Conveyor belt speed is unstable.

[0786] Q: What is the current speed setting for the conveyor belt?

[0787] Answer: The current speed is 120 meters per minute.

[0788] By using this system, when a problem occurs on the production line, it becomes possible to quickly identify the cause and find the optimal solution.

[0789] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0790] Step 1:

[0791] The server initializes the system and starts the moderator generation AI, question generation AI, and scribe generation AI. This prepares each AI to perform processes from problem analysis to presenting solutions. When the server performs its initial setup, it reads the initialization data, loads the AI ​​model, and sets it up so that it can be executed.

[0792] Step 2:

[0793] The user inputs the problem they want solved in natural language through a terminal. For example, they might input a problem such as "How can we quickly solve a problem that occurred on a production line?" This input data is then sent to the server by the terminal.

[0794] Step 3:

[0795] The server receives the task entered by the user and passes it to the moderator generation AI. The server converts the data into an appropriate format and provides it as input data to the moderator generation AI. This enables task analysis in the next step.

[0796] Step 4:

[0797] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, it generates topics such as "adjusting the speed of the equipment" or "reviewing maintenance procedures." Here, it performs natural language analysis of the input data and extracts and generates related topics.

[0798] Step 5:

[0799] The question generation AI generates questions based on the generated solution topic and sends them to the user's device via the server. For example, a question might be generated such as "What is the current speed setting of the conveyor belt?" In this step, the topic is explored in depth and appropriate questions are generated to gather more detailed information.

[0800] Step 6:

[0801] The user answers the questions generated through the terminal and sends the answer to the server. For example, the user might answer "Current speed is 120 meters per minute." The user's answer data is sent to the server and used for recording and organizing in the next step.

[0802] Step 7:

[0803] The scribe-generative AI receives the user's answers and records and organizes the discussion content. The server passes the answer data to the scribe-generative AI, which records the progress of the discussion and generates a log to move forward. For example, if the answer is "The current speed is 120 meters per minute," the content is recorded as text data.

[0804] Step 8:

[0805] The moderator AI advances the discussion based on the user's answers and generates questions based on the next topic. For example, the next question generated might be, "Have you calibrated the speed control unit?" Here, the user's answer data is analyzed and a process is performed to generate questions for the specific issues that need to be resolved next.

[0806] Step 9:

[0807] The user then enters the answer to the question again and sends it to the server via their device. For example, they might reply, "Yes, I have performed calibration. However, the speed is still unstable." This information is then sent to the server and analyzed again.

[0808] Step 10:

[0809] The server receives the second response, and the transcription AI records it and organizes the progress of the discussion. As mentioned above, the user's response is recorded as text data and used to advance the discussion in the next step.

[0810] Step 11:

[0811] The moderator AI generates a final solution, and the scribe AI organizes it and creates a final result document to present to the user. For example, a proposed solution might be, "Since the speed control unit remains unstable even after calibration, consider replacing the control unit."

[0812] Step 12:

[0813] The server sends the final results to the user's device, where the user can view them, thereby obtaining specific solutions and putting them into practice.

[0814] By following these steps, you can quickly find an effective solution to any problem that arises.

[0815] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0816] The problem-solving system of the present invention combines a server, terminals, various generative AIs, and an emotion engine to adjust the progress of appropriate questions and discussions based on the user's emotional state. The system's program processing is described in detail below.

[0817] overview

[0818] In this system, users input a task via a terminal, and the process involves a moderator AI, a question generator AI, a secretary AI, and an emotion engine working together to help solve the task. The server oversees the processing of these AIs and the emotion engine, and manages the user interface.

[0819] Program processing

[0820] 1. Initial Setup:

[0821] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine.

[0822] 2. Enter your assignment:

[0823] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[0824] 3. Receiving assignments:

[0825] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[0826] 4. Issue analysis and topic generation:

[0827] The moderator generation AI analyzes the problem and generates a solution topic, such as "current situation analysis" or "target demographic selection."

[0828] 5. Question generation:

[0829] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0830] 6. Posing and answering questions:

[0831] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[0832] 7. Emotion Analysis:

[0833] The server passes the user's response to the emotion engine, which analyzes the user's response and determines their emotion. For example, if the user is feeling anxious, the engine recognizes that emotion.

[0834] 8. Recording and Organizing:

[0835] The transcription-generating AI records and organizes the user's responses and analyzed emotions, resulting in organized discussion content and emotional data.

[0836] 9. Moderation of discussion:

[0837] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[0838] 10. Generate the following question:

[0839] The question generator AI generates new questions based on tailored instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[0840] 11. Posting and answering further questions:

[0841] The server sends the newly generated question to the user's device. The user then enters the answer to the question again and sends it to the server via their device. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[0842] 12. Emotion analysis and recording again:

[0843] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[0844] 13. Producing the final result:

[0845] The moderator's generative AI completes discussions on all topics and derives a final solution while taking into account emotional data.

[0846] 14. Presentation of results:

[0847] The transcription AI summarizes all the discussions and documents the final solution, which the server then sends to the user's device, where the user can review it.

[0848] Specific examples

[0849] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[0850] 1. Initial Setup:

[0851] The server initializes the system and starts each AI and emotion engine.

[0852] 2. Enter your assignment:

[0853] The user inputs the assignment through the terminal.

[0854] 3. Receiving and analyzing assignments:

[0855] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[0856] 4. Question generation and answering:

[0857] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[0858] 5. Sentiment analysis and discussion moderation:

[0859] The emotion engine analyzes the user's responses and recognizes, for example, whether they are feeling anxious. Based on this, the moderator generation AI adjusts the progress of the discussion and appropriately lowers the difficulty of the questions.

[0860] 6. Re-questioning and final results:

[0861] To the follow-up question, "What demographic do you envision as the target demographic for your new product?", the user answers, "We envision a demographic with a high affinity with young people." Taking into account the emotional data, the moderator's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people." The scribe's generative AI then organizes this and presents it to the user.

[0862] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently while taking into consideration their emotions.

[0863] The processing flow will be explained below.

[0864] Step 1:

[0865] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine, which completes preparations for each AI and emotion engine.

[0866] Step 2:

[0867] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[0868] Step 3:

[0869] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[0870] Step 4:

[0871] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[0872] Step 5:

[0873] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[0874] Step 6:

[0875] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[0876] Step 7:

[0877] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[0878] Step 8:

[0879] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[0880] Step 9:

[0881] The server sends the generated question to the user's terminal, which displays the question to the user.

[0882] Step 10:

[0883] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[0884] Step 11:

[0885] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI and emotion engine.

[0886] Step 12:

[0887] The emotion engine analyzes the user's responses and assesses their emotions, for example recognizing that they are feeling stressed or anxious.

[0888] Step 13:

[0889] A generative AI transcription system records and organizes the user's responses and the emotions analyzed by the emotion engine.

[0890] Step 14:

[0891] Based on the analysis results of the emotion engine, the server feeds back the user's emotional state to the moderator AI, which then adjusts the progress of the discussion and instructs the question generator AI to generate questions that are adapted to the user's state.

[0892] Step 15:

[0893] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[0894] Step 16:

[0895] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[0896] Step 17:

[0897] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[0898] Step 18:

[0899] The device sends the user's response to the server, which then passes the response back to the transcription AI and emotion engine. The emotion engine then analyzes the user's emotions and adjusts the discussion again as necessary.

[0900] Step 19:

[0901] A generative AI scribe records new responses from users and updates the progress of the discussion.

[0902] Step 20:

[0903] A generative AI moderator will complete the discussion on all topics and come up with a final solution while taking into account sentiment data.

[0904] Step 21:

[0905] A generative AI transcription system will summarize all discussions and document the final solution.

[0906] Step 22:

[0907] The server sends the final result to the user's terminal, where the user can view the result.

[0908] These are the specific processing steps of the system that combines the emotion engine, which allows for efficient problem solving while responding to the user's emotional state.

[0909] Example 2

[0910] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0911] Conventional problem-solving support systems have the problem of being unable to take into account the user's emotional state and therefore unable to provide appropriate support to users who feel stressed or anxious. Furthermore, the progress of the discussion and adjustment of questions are uniform, making it difficult to provide appropriate support according to the individual situation of each user.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a task through a terminal; a means for receiving the task input by the user; a means for a moderator generation system AI to analyze the task and generate a solution topic; a means for a question generation system AI to generate a question based on the solution topic; a means for an emotion engine to analyze emotions from the user's answers; a means for the user to answer the question generated through the terminal; a means for a scribe generation system AI to receive the user's answers and the analyzed emotions and record and organize the discussion content; a means for the moderator generation system AI to proceed with the discussion based on the user's answers and the analyzed emotions and derive a solution; and a means for presenting the final solution to the user's terminal. This makes it possible to conduct appropriate and effective questions and discussion while taking into account the user's emotional state.

[0913] A "user" is a person who uses the system to input tasks and answer questions.

[0914] A "terminal" is a hardware device on which a user inputs tasks and answers questions, and specifically is a computer device such as a PC, smartphone, or tablet.

[0915] The "server" is a computer system that oversees various generative AIs and emotion engines and manages the user interface.

[0916] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solution topics, and manages the progress of the discussion.

[0917] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[0918] An "emotion engine" is software that analyzes the emotions from users' responses and obtains emotional data.

[0919] The "generative AI for notation" is an artificial intelligence that receives user responses and emotional data analyzed by the emotion engine, and records and organizes the content of the discussion.

[0920] "Solution topics" are specific topics and steps for solving problems that are generated by the moderator generation AI by analyzing the user's issues.

[0921] A "question" is a specific question that the question generation AI generates based on the solution topic for the user to answer.

[0922] "Emotions" are psychological states such as stress, anxiety, or joy that a user expresses when answering questions.

[0923] The "solution" is the final problem-solving method that the moderator's generative AI derives based on the user's answers and emotions.

[0924] MODE FOR CARRYING OUT THE INVENTION

[0925] This invention is a system in which a user inputs a problem through a terminal, a server receives the problem, and various generative AIs and emotion engines work together to support problem solving. This system is characterized by its ability to adjust appropriate questions and discussion progress in real time, taking into account the user's emotional state. Detailed modes for implementing the invention are described below.

[0926] Hardware and software used

[0927] 1. Server

[0928] The server oversees various generative AIs (hosting AI, question generation AI, and secretary generation AI) and the emotion engine, and manages the user interface. The server communicates with devices using HTTP requests and WebSockets.

[0929] 2. Terminal

[0930] A terminal is a hardware device where users input tasks and answer questions. Terminals can be PCs, smartphones, tablets, etc. The terminal provides a user interface and displays information from the server.

[0931] 3. Generative AI and Emotion Engines

[0932] Generative AI for moderators: Analyzes the issues entered by users and generates solutions. Examples of AI models used include GPT-4.

[0933] Question generation AI: Generates specific questions based on the topics generated by the moderator generation AI.

[0934] Generative AI for transcription: Records user responses and emotional data, and organizes the discussion content.

[0935] Emotion engine: Software that analyzes emotions from user responses. An example is IBM Watson Tone Analyzer.

[0936] System processing flow

[0937] The specific flow of the system processing proceeds as follows:

[0938] 1. Initial Setup

[0939] The server initializes the system and starts each generative AI (for moderator, questioner, and secretary) and emotion engine.

[0940] 2. Enter your assignment

[0941] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[0942] 3. Receiving assignments

[0943] The device sends the user's input data to the server, which receives the data and passes it to the moderator generation AI.

[0944] 4. Issue analysis and topic generation

[0945] The moderator's AI analyzes the problem and generates relevant solution topics, such as "current situation analysis" and "target demographic selection."

[0946] 5. Question Generation

[0947] The question generator AI creates questions based on the topics generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[0948] 6. Posing and answering questions

[0949] The server sends the generated question to the user's device. The device displays the question to the user, and the user inputs an answer. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition." The device then sends the answer to the server.

[0950] 7. Emotion Analysis

[0951] The server passes the user's response to an emotion engine, which analyzes the user's emotions, for example, recognizing that the user is feeling anxious.

[0952] 8. Recording and Organizing

[0953] A generative AI transcription system records and organizes users' responses and analyzed emotions, thereby saving the discussion history and emotional data.

[0954] 9. Moderation of discussions

[0955] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[0956] 10. Next Question Generation

[0957] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[0958] 11. Posting and answering follow-up questions

[0959] The server sends the newly generated question to the user's device. The user then enters an answer to the question, for example, "We are targeting a demographic with a high affinity with young people." The device then sends this answer to the server.

[0960] 12. Re-analyzing and recording emotions

[0961] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[0962] 13. Producing the final result

[0963] The moderator's generative AI completes the discussion for all topics and derives a final solution while taking into account the sentiment data. For example, by integrating user responses and sentiment, it might suggest a solution such as "strengthen online advertising for new products and run a campaign targeted at younger generations."

[0964] 14. Presentation of Results

[0965] The transcription AI summarizes all the discussions and documents the final solution, and the server sends the final result to the user's device, where the user can review it.

[0966] Specific examples

[0967] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[0968] The user enters the assignment through the terminal.

[0969] The device sends the assignment to the server.

[0970] The server receives the assignment and passes it to the moderator generation AI.

[0971] The moderator generation AI analyzes the issues and generates topics such as "current situation analysis" and "target demographic selection."

[0972] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?"

[0973] A user responded, "Our current strength is online advertising, and our weakness is low brand recognition."

[0974] The emotion engine analyzes the user's responses and recognizes that they are feeling anxious.

[0975] The moderator's generative AI will use this information to adjust the progress of the discussion.

[0976] The question generation AI generates a follow-up question: "What type of target demographic do you envision for your new product?"

[0977] Users responded that they are "targeting a demographic with a high affinity with younger generations."

[0978] The host's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people."

[0979] A generative AI system for writing organizes this information and presents it to the user.

[0980] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0981] Step 1:

[0982] The server initializes the system and starts the moderator generation AI, question generation AI, clerk generation AI, and emotion engine. Specifically, it creates execution instances of each AI and emotion engine and allocates the necessary resources. This prepares all modules to run. The input is the system startup command, and the output is the initialization completion state of each AI and emotion engine.

[0983] Step 2:

[0984] The user inputs a problem through the terminal. A text input field is displayed on the terminal, and the user inputs the problem in natural language. For example, the problem is "How to consider a marketing strategy for new product development." The input is the user's problem text, and the output is the terminal sending the problem text to the server.

[0985] Step 3:

[0986] The device sends the user's input to the server. The server receives the input data using a communication protocol (e.g., HTTP request, WebSocket). Specifically, it parses the JSON-formatted data sent from the device and obtains the user's input assignment. The input is the user's assignment text, and the output is the assignment data received by the server.

[0987] Step 4:

[0988] The server passes the received problem data to the moderator generation AI, which analyzes this data and generates solution topics. For example, it uses natural language processing technology to generate topics such as "current situation analysis" and "target demographic selection." The input is the problem text, and the output is a list of generated topics.

[0989] Step 5:

[0990] The question generation AI generates specific questions based on the topics generated by the moderator generation AI. Specifically, it uses a generative AI model (e.g., GPT-4) to construct appropriate questions for each topic. For example, it generates a question such as, "What are the strengths and weaknesses of your current marketing strategy?" The input is a list of topics, and the output is a list of generated questions.

[0991] Step 6:

[0992] The server sends the generated questions to the user's terminal. The terminal displays the questions to the user so that the user can answer them. Specifically, the questions are displayed on the screen and the user enters text into an answer input field. The input is a list of questions, and the output is the questions displayed to the user.

[0993] Step 7:

[0994] The user answers questions through the device. The device collects the user's answers and sends them back to the server. For example, if the user answers "Our current strength is online advertising, and our weakness is low brand recognition," the text is collected. The input is the user's answer to the question, and the output is the answer data sent to the server.

[0995] Step 8:

[0996] The server passes the user's answer to the emotion engine, which analyzes the user's answer text to determine the emotion. Specifically, it uses natural language processing to identify the user's emotional state (e.g., anxiety, joy, anger, etc.). The input is the answer text, and the output is the analyzed emotion data.

[0997] Step 9:

[0998] The transcription-generative AI records and organizes user responses and analyzed emotions. Specifically, it stores responses and emotional data in a database and creates a document to organize the discussion content. The input is the response text and emotional data, and the output is the recorded and organized discussion content.

[0999] Step 10:

[1000] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. Specifically, if the user is feeling stressed, it will take measures such as lowering the difficulty of the questions. The input is emotional data, and the output is instructions for adjusting the progress of the discussion.

[1001] Step 11:

[1002] The question generation AI generates new questions based on the adjusted instructions. For example, it generates a question like, "What type of demographic is the new product targeted for?" The input is the adjusted instructions, and the output is the newly generated question.

[1003] Step 12:

[1004] The server sends a new question to the user's terminal, which displays the question to the user and prompts the user to enter the answer again. The input is the new question, and the output is the question displayed to the user.

[1005] Step 13:

[1006] The user answers a new question and sends it to the server via their device. For example, they might answer, "We are targeting a demographic with a high affinity with young people." The input is the user's new answer, and the output is the answer data sent to the server.

[1007] Step 14:

[1008] The server passes the new response back to the emotion engine, which analyzes it. The analysis results are then passed back to the transcription generation AI, which records the discussion content and emotion data. The input is the new response data, and the output is the analyzed emotion data.

[1009] Step 15:

[1010] The moderator generative AI completes the discussion for all topics and derives a final solution. Specifically, it integrates user responses and emotional data to propose an appropriate solution. The input is all discussion data and emotional data, and the output is the final solution.

[1011] Step 16:

[1012] The transcription generator AI documents the final solution, and the server sends it to the user's device. The user then checks the final result on their device. The input is the final solution, and the output is the result presented to the user.

[1013] (Application example 2)

[1014] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1015] Conventional problem-solving systems proceed without taking into account the user's emotional state, which can cause stress or anxiety for the user, making efficient problem-solving difficult. Furthermore, particularly on online shopping sites, the user's emotions during the purchasing experience cannot be properly addressed, potentially resulting in a decrease in satisfaction. Therefore, there is a need for a system that can analyze the user's emotional state and, based on that, guide the discussion and provide appropriate information.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1017] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for an emotion engine to analyze emotions from the users' answers and adjust the progress of the discussion based on that. This makes it possible to solve problems more efficiently while taking the users' emotional state into consideration, and to improve user satisfaction, especially on online shopping sites.

[1018] A "user" is an entity that uses the system to input questions and provide answers.

[1019] A "terminal" is a device that allows a user to input tasks and answer questions, and includes smartphones, personal computers, etc.

[1020] The "server" is a computer system that oversees the entire system and manages the processing of various generative AIs and emotion engines.

[1021] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solutions, and advances the discussion.

[1022] "Question generation AI" is an artificial intelligence that generates specific questions based on the solution topics generated by the moderator generation AI.

[1023] "Generative notation AI" is an artificial intelligence that receives user responses, records and organizes the discussion, and documents the final solution.

[1024] The "emotion engine" is an engine that analyzes the user's emotional state from their answers and inputs, and adjusts the progress of the discussion and the content of the questions based on that.

[1025] "Solution topics" are specific discussion themes or question subjects that are generated by the moderator generation AI by analyzing the issues.

[1026] A "question" is a question posed to the user that is generated by the question generation AI based on the solution topic.

[1027] An "answer" is information provided by a user by answering a question generated through a terminal.

[1028] A "discussion" is a process of discussion formed through an exchange of questions and answers between the user and the generative AI.

[1029] A "solution" is the final answer or proposal to the user's problem that is arrived at as a result of the discussion.

[1030] The system embodying this invention combines a server, a terminal, various generative AIs, and an emotion engine to support problem solving while taking into account the user's emotional state. This system contributes to improving the user experience, especially on online shopping sites.

[1031] System configuration

[1032] The system's hardware configuration includes the smartphone or PC used by the user and a server. The software configuration includes an AI for generating moderators, an AI for generating questions, an AI for generating notes, and an emotion engine. Specifically, the following software is used:

[1033] Emotion engine: e.g., emotion_recognition library

[1034] Generative AI: OpenAI's GPT model

[1035] Program processing

[1036] During the initial setup phase, the server initializes various generative AIs and emotion engines. When a user inputs a task via their device, the task is received by the server and passed to the moderator generative AI. The moderator generative AI analyzes the user's input and generates a solution topic. For example, if the discussion is about "marketing strategies for a new product," topics such as "current situation analysis" and "target demographic selection" will be generated.

[1037] The question generation AI then generates specific questions based on the topic being addressed. These questions are sent to the user's device, and the user inputs the corresponding answers. For example, in response to the question, "What are the strengths and weaknesses of your current marketing strategy?", the AI ​​will answer, "The current strength is online advertising, and the weakness is low brand awareness."

[1038] The user's answers are analyzed by an emotion engine to determine the emotions the user is feeling (e.g., anxiety or stress). The emotion data analyzed by the emotion engine is fed back to the moderator generation AI, which adjusts the progress of the discussion based on that. For example, if the user is feeling high stress, the difficulty of the questions may be lowered.

[1039] Specific examples

[1040] Specifically, imagine a situation where a user feels uneasy after reading a product review while shopping online. When the user types the question, "What are the advantages of this product?", the emotion engine detects the user's uneasiness, and the generative AI provides information that provides reassurance.

[1041] Example prompt sentence:

[1042] text

[1043] A user is concerned about this statement: 'I've been reading the reviews for this product and they're mostly negative.' How should I respond?

[1044] This allows the system to provide a better purchasing experience while taking into consideration the user's feelings. The generative transcription AI also organizes the discussions and documents the final solution, which is then presented to the user. For example, the system may arrive at a solution such as "strengthen online advertising for new products and run a campaign specifically targeted at younger generations."

[1045] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1046] Step 1:

[1047] The server initializes the system and starts the moderator generation AI, question generation AI, clerk generation AI, and emotion engine. During this initialization step, the environment is configured and the necessary libraries and modules are loaded for the system to operate properly. Specifically, the parameters of the various AI models are set and the emotion engine's analysis model is loaded. The necessary network resources are also secured and connections are prepared.

[1048] Step 2:

[1049] The user inputs the problem they want to solve in natural language through the device. For example, they might input "I would like to consider a marketing strategy for a new product." The device then sends this input to the server. At this stage, the text data entered by the user becomes the input, and the text data sent from the device to the server becomes the output.

[1050] Step 3:

[1051] The server receives the task entered by the user and passes it to the moderator generation AI. The moderator generation AI analyzes the task and generates a solution topic. For example, it generates topics such as "current situation analysis" and "target demographic selection." In this step, the user input (natural language text) received by the server becomes the input, and the generated solution topic (a list in natural language) becomes the output. Specifically, it uses natural language processing technology to break down the task into topics.

[1052] Step 4:

[1053] The question generation AI generates specific questions based on the solution topic generated by the moderator generation AI. For example, a question such as "What are the strengths and weaknesses of your current marketing strategy?" is generated. In this step, the solution topic is input, and a specific question (natural language text) based on it is output.

[1054] Step 5:

[1055] The server sends the generated question to the user's device. The user inputs an answer to the question through the device. For example, the user might input an answer such as, "Our current strength is online advertising, and our weakness is low brand recognition." In this step, the generated question is the input, and the answer (natural language text) entered by the user is the output. Specifically, the question is notified to the user's device, and the user answers it.

[1056] Step 6:

[1057] The server receives the user's response and passes it to the emotion engine for analysis. The emotion engine analyzes the user's emotional state from the response and returns emotion data, such as "anxiety" or "stress," to the server. In this step, the user's response (natural language text) is input, and the analyzed emotion data (e.g., anxiety or stress) is output. Specifically, the emotion analysis model analyzes the text data and labels the emotion.

[1058] Step 7:

[1059] The analyzed emotion data is passed to the transcription-generating AI, which records and organizes the user's responses and emotion data. In this step, emotion data and user responses are input, and organized discussion content (natural language text) is output. Specifically, responses and emotion data are recorded in chronological order.

[1060] Step 8:

[1061] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it lowers the difficulty of the next question. In this step, emotional data is input, and adjusted instructions for the discussion progress are output. Specifically, the generative AI model makes adjustments according to the emotional data.

[1062] Step 9:

[1063] The question generation AI generates new questions based on the adjusted instructions. For example, a question such as, "What marketing strategy are you considering next?" is generated. In this step, the adjusted discussion flow instructions are the input, and the generated new question (natural language text) is the output.

[1064] Step 10:

[1065] The server sends the newly generated question to the user's device. The user again enters an answer to the question and sends it to the server via the device. For example, the user might enter an answer such as, "We expect the target demographic for our new product to be young people." In this step, the new question is the input, and the user's answer (natural language text) is the output.

[1066] Step 11:

[1067] The server passes the new answers to the emotion engine for re-analysis, and the transcription generation AI re-records the discussion content together with the analyzed emotion data. In this step, the user's new answers are input, and the reorganized discussion content is output. Specifically, the answers and emotion data are re-recorded, and the discussion content is periodically updated.

[1068] Step 12:

[1069] The moderator generative AI completes the discussion for all topics and derives a final solution while taking into account the emotional data. In this step, all discussion content and emotional data are input, and the final solution is output. Specifically, the generative AI model performs a comprehensive analysis and proposes an appropriate solution to the user.

[1070] Step 13:

[1071] The scribe generation AI summarizes the entire discussion and documents the final solution. The server sends the final result to the user's device, where the user confirms the result. In this step, the discussion content and the solution are input, and the summarized final solution (document) is output. In concrete terms, the generated document is presented to the user, who then confirms it.

[1072] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1074] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1075] [Third embodiment]

[1076] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1077] 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.

[1078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[1079] 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.

[1080] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1083] 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.

[1084] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1085] 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.

[1086] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1087] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1088] The problem-solving system of the present invention is implemented using a server, a terminal, and various AIs. By using this system, users can solve problems efficiently and effectively. Below, the processing of the system's program is explained in detail in natural language.

[1089] overview

[1090] This system supports the process in which users input a problem via a terminal, and multiple generative AIs derive appropriate questions and solutions for that problem. The server plays a central role in the system, managing the processing of various AIs and the user interface.

[1091] Program processing

[1092] 1. Initial Setup:

[1093] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI.

[1094] 2. Enter your assignment:

[1095] The user inputs the problem to be solved in natural language through the terminal. For example, the user might input "How to consider a marketing strategy for new product development."

[1096] 3. Receiving assignments:

[1097] The device sends the user's input to the server, which receives it and passes it to the moderator generation AI.

[1098] 4. Issue analysis and topic generation:

[1099] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, topics such as "current situation analysis" and "target demographic selection" are generated.

[1100] 5. Question generation:

[1101] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1102] 6. Posing and answering questions:

[1103] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[1104] 7. Recording and Organizing:

[1105] The server passes the user's answers to the scribe-generative AI, which records and organizes them, creating a progress and detailed record of the discussion.

[1106] 8. Discussion process:

[1107] The moderator AI will advance the discussion based on the user's answers. It will then have the question generator AI generate questions based on the next topic. For example, the next question generated would be, "What type of demographic do you envision as the target demographic for your new product?"

[1108] 9. Restatement and Answer of Question:

[1109] The user again enters the answer to the question and sends it to the server via the terminal. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[1110] 10. Record and organize again:

[1111] The server receives the second response, and the transcription generation AI records it and organizes the progress of the discussion.

[1112] 11. Producing the final result:

[1113] The moderator AI generates the final solution, and the secretary AI organizes it to create the final result document to be presented to the user.

[1114] 12. Presentation of results:

[1115] The server sends the final result to the user's terminal, where the user can view it.

[1116] Specific examples

[1117] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[1118] 1. Initial Setup:

[1119] The server initializes the system and starts each AI.

[1120] 2. Enter your assignment:

[1121] The user inputs the assignment through the terminal.

[1122] 3. Receiving and analyzing assignments:

[1123] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[1124] 4. Question generation and answering:

[1125] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[1126] 5. Recording and Proceedings:

[1127] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[1128] 6. Re-answers and final results:

[1129] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[1130] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[1131] The processing flow will be explained below.

[1132] Step 1:

[1133] The server starts up and initializes the moderator generation AI, question generation AI, and secretary generation AI. This completes preparation for each AI to operate.

[1134] Step 2:

[1135] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[1136] Step 3:

[1137] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[1138] Step 4:

[1139] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[1140] Step 5:

[1141] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[1142] Step 6:

[1143] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[1144] Step 7:

[1145] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[1146] Step 8:

[1147] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1148] Step 9:

[1149] The server sends the generated question to the user's terminal, which displays the question to the user.

[1150] Step 10:

[1151] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[1152] Step 11:

[1153] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI.

[1154] Step 12:

[1155] The transcription-generating AI receives the user's responses, records and organizes them, and the organized discussion content is obtained.

[1156] Step 13:

[1157] The server feeds the user's answers back to the moderator generation AI and instructs it to move the discussion forward to the next topic.

[1158] Step 14:

[1159] The moderator generation AI instructs the question generation AI to generate questions to advance the discussion on the next topic (e.g., "target demographic selection").

[1160] Step 15:

[1161] A question generation AI generates new questions, such as, "What kind of target demographic do you envision for your new product?"

[1162] Step 16:

[1163] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[1164] Step 17:

[1165] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[1166] Step 18:

[1167] The device sends the user's answer to the server, which then passes the answer back to the scribe generation AI.

[1168] Step 19:

[1169] A generative AI scribe records new responses from users and updates the progress of the discussion.

[1170] Step 20:

[1171] A moderator generative AI will conclude discussions on all topics and come up with a final solution.

[1172] Step 21:

[1173] A generative AI transcription system will summarize all discussions and document the final solution.

[1174] Step 22:

[1175] The server sends the final result to the user's terminal, where the user can view the result.

[1176] The above are the specific processing steps of the system, which allow users to solve problems efficiently by following a series of steps.

[1177] Example 1

[1178] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1179] Today's users spend a lot of time and effort solving complex problems. Solving these problems requires extensive knowledge and experience, and in many cases, the advice and cooperation of experts is essential. Furthermore, the process of finding a solution requires efficient discussion and organization. However, many users do not have the means to receive appropriate assistance. As a result, problem solving is not carried out efficiently and effectively, and the burden on users increases.

[1180] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1181] In this invention, the server includes means for a user to input a problem through a terminal, means for the server to receive the problem input by the user, means for a moderator AI to analyze the problem and generate a solution topic, means for a question generator AI to generate a question based on the solution topic, means for the server to send the generated question to the user's terminal, means for the user to answer the question through the terminal, means for the server to send the user's answer to the clerk generator AI, means for the clerk generator AI to record and organize the user's answer, means for the moderator AI to proceed with the discussion based on the user's answer and derive a solution, and means for presenting the final solution to the user's terminal, thereby enabling users to solve problems efficiently and effectively.

[1182] "User" means an individual or entity that uses the system to enter a problem and request a solution.

[1183] A "terminal" is an electronic device that allows a user to input tasks and answer questions from the system.

[1184] A "problem" is a problem or question that a user wants solved.

[1185] The "server" is a central computer that manages the entire system and processes various generative AIs.

[1186] "Generative AI for hosting" is an artificial intelligence that analyzes users' issues and generates appropriate solution topics.

[1187] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[1188] "Generative AI for transcription" is an artificial intelligence that records and organizes users' responses.

[1189] "Solution topics" are important themes or items for discussion and solutions that the moderator generation AI has derived by analyzing the issue.

[1190] A "question" is a specific problem or inquiry generated by the question generation AI.

[1191] An "answer" is a response or opinion that a user enters in response to a question.

[1192] A "discussion" is a discussion for solving a problem that progresses based on the user's answers.

[1193] A "solution" is the final solution or proposal for solving a problem.

[1194] The problem-solving system of the present invention is implemented using a server, a terminal, and various generative AIs. By using this system, users can solve problems efficiently and effectively.

[1195] System configuration

[1196] The system consists of a terminal operated by the user, a server, and multiple generative AIs (moderator AI, question generator AI, and secretary generator AI). The server plays a central role in the system, managing the processing of the various generative AIs and the user interface.

[1197] Hardware and Software Use

[1198] The server is a high-performance computing device used to process each generative AI at high speed. For example, it is recommended that the server be equipped with a powerful CPU and GPU. Machine learning frameworks such as TensorFlow and PyTorch are used to process the AI ​​models.

[1199] The terminal is used by the user to input tasks and answer generated questions. The terminal can be a PC, smartphone, tablet, etc. The software used can be a web browser or a dedicated application.

[1200] Program processing

[1201] The program processes as follows: First, the server initializes the system and starts the moderator generation AI, question generation AI, and clerk generation AI. Next, the user inputs the problem they want solved in natural language through their device. The device sends this input to the server, which receives it and passes it on to the moderator generation AI.

[1202] The moderator AI analyzes the problem and generates a solution topic. The question generator AI then generates specific questions based on the solution topic, which are sent to the user's device via the server. The user enters answers to the generated questions and sends them to the server via their device. The server receives these and passes them to the scribe generator AI for recording and organizing.

[1203] Specific examples

[1204] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be executed.

[1205] 1. Enter your assignment:

[1206] The user inputs "How to consider a marketing strategy for new product development" through the terminal.

[1207] 2. Receiving and analyzing assignments:

[1208] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[1209] 3. Question generation and answering:

[1210] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[1211] 4. Recording and Proceedings:

[1212] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[1213] 5. Re-answer and final result:

[1214] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[1215] The above is a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[1216] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1217] Step 1:

[1218] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI. The AI ​​models and configuration files are loaded, and each AI module becomes ready for use.

[1219] Input: server

[1220] Output: Initialized AI for each generator

[1221] Step 2:

[1222] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[1223] Input: user

[1224] Output: The assignment text typed into the terminal

[1225] Step 3:

[1226] The terminal sends the assignment text entered by the user to the server. The terminal converts the input data into an appropriate format (e.g., JSON) and prepares it for transmission to the server.

[1227] Input: The assignment text entered into the terminal

[1228] Output: Issue data sent to the server

[1229] Step 4:

[1230] The server analyzes the received assignment data, extracts the JSON format data, and passes it to the moderator generation AI. During the analysis process, the necessary data fields are extracted and the format is organized.

[1231] Input: Issue data sent to the server

[1232] Output: Organized data passed to the moderator generation AI

[1233] Step 5:

[1234] The moderator's generative AI analyzes the problem and generates a solution topic. It uses natural language processing technology to break down the problem and extract the main themes and topics in text format.

[1235] Input: Organized data passed to the host generation AI

[1236] Output: Generated resolution topics

[1237] Step 6:

[1238] The question generation AI generates specific questions based on the solution topic generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[1239] Input: Generated resolution topic

[1240] Output: Generated questions

[1241] Step 7:

[1242] The server sends the questions generated by the question generation AI to the user's device, where the questions are formatted appropriately (e.g., text or JSON) and presented on the device.

[1243] Input: Generated question

[1244] Output: The question sent to the user's device

[1245] Step 8:

[1246] The user inputs answers to questions displayed on the terminal. For example, they might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[1247] Input: The answer entered by the user

[1248] Output: Answers typed into the terminal

[1249] Step 9:

[1250] The terminal sends the user's answer to the server, which converts the answer data into an appropriate format and sends it to the server.

[1251] Input: Answer typed into the terminal

[1252] Output: Response data sent to the server

[1253] Step 10:

[1254] The server receives the user's answer and passes it to the clerk's AI generator, which records the answer as text data and stores it in a database.

[1255] Input: Response data sent to the server

[1256] Output: Answer data passed to the transcription generation AI

[1257] Step 11:

[1258] The transcription AI records the response data and organizes the content of the responses, creating a detailed record of the progress of the discussion.

[1259] Input: Answer data passed to the transcription generation AI

[1260] Output: Recorded and organized response data

[1261] Step 12:

[1262] The moderator AI will proceed with the discussion based on the recorded and organized response data. The question generator AI will generate new questions based on the next topic. For example, a question might be generated: "What type of demographic do you envision as the target demographic for your new product?"

[1263] Input: Recorded and organized response data

[1264] Output: Next question

[1265] Step 13:

[1266] The server then sends the newly generated question to the user's terminal, and the user again enters an answer to the question and sends it again to the server via the terminal.

[1267] Input: The newly generated question

[1268] Output: The new question sent to the user's device.

[1269] Step 14:

[1270] The server again receives the user's input answers and sends them to the transcription AI for recording and organizing, thereby keeping the progress of the discussion up to date.

[1271] Input: Answer data sent by the user again

[1272] Output: Answer data sent again to the transcription generation AI

[1273] Step 15:

[1274] After the discussion progresses and a final solution is reached, the moderator AI documents it, and the scribe AI organizes this document and presents it to the user as the final result.

[1275] Input: Solution data recorded by the transcription-generative AI

[1276] Output: A cleaned up final result document

[1277] Step 16:

[1278] The server sends the final document to the user's device, where the user can download or view it for review.

[1279] Input: Final result document

[1280] Output: The final result document sent to the user's device.

[1281] (Application example 1)

[1282] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1283] There is a need to quickly and accurately resolve problems that occur on production lines. In particular, there is a problem in that it is difficult for production line operators to immediately identify the cause of the problem and find the optimal solution, so the problem is left unresolved for a long time, resulting in a decline in production efficiency.

[1284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1285] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for presenting the final solution to the user's terminal; and means for supporting problem solving on the production line, including means for analyzing data from production equipment and proposing optimal solutions in real time. This makes it possible to quickly identify the cause of a problem when it occurs on the production line and obtain an optimal solution.

[1286] "User" refers to the person who operates the system, inputs problems, and receives solutions.

[1287] A "terminal" is a device used by a user to input tasks and check solutions, and includes PCs, tablets, smartphones, etc.

[1288] "Server" refers to the central system that manages the entire system, receives and analyzes data, and initializes and operates the generative AI.

[1289] "Generative AI for moderators" refers to an artificial intelligence system that analyzes input issues, generates solution topics, and supports the progress of discussions.

[1290] "Question generation AI" refers to an artificial intelligence system that generates appropriate questions based on the solution topic generated by the moderator generation AI.

[1291] "Generative AI for transcription" refers to an artificial intelligence system that receives user responses and records and organizes the content of the discussion.

[1292] "Solution topics" refer to specific items for consideration toward solutions that are generated as a result of the moderator generation AI analyzing the issue.

[1293] "Production equipment" refers to the machines and devices used on the production line, and refers to the hardware used to carry out the production process.

[1294] "Real-time" refers to the instantaneous receipt of data, analysis, and provision of solutions.

[1295] "Production line" refers to the series of processes that produce a product throughout the manufacturing process.

[1296] An "optimal solution" refers to the most effective and efficient way to address a problem, including suggestions derived by generative AI.

[1297] "Means to support problem-solving" refers to methods and technologies that use AI to analyze and propose solutions to various problems that arise on production lines.

[1298] In order to implement the present invention, the following system is constructed.

[1299] First, the server manages the entire system and initializes each generative AI. The server uses generative AI models such as GPT-4 to analyze problems, generate solution topics, and generate questions.

[1300] Users input their issues through their devices, and this input data is sent to the server. The input issues are analyzed by a moderator generation AI, which generates a solution topic. For example, if the issue is "How to quickly resolve a malfunction that occurred on the production line," the generated solution topics include "adjusting the speed of the equipment" and "reviewing maintenance procedures."

[1301] Next, the question generation AI creates a question to be presented to the user based on the generated solution topic. For example, a specific question such as "What is the current speed setting of the conveyor belt?" is generated. This question is presented to the user via their terminal, and the user inputs an answer.

[1302] The user's answers are sent back to the server, where they are recorded and organized by the transcription generation AI. This process ensures that the progress of the discussion is properly recorded and necessary information is accumulated.

[1303] The moderator AI advances the discussion based on the user's answers, generates more detailed questions, and arrives at a final solution, which it then organizes in cooperation with the secretary AI. The final solution is then presented to the user via their device.

[1304] As a specific example, if the problem is an unstable conveyor belt speed, the moderator AI generates a topic about adjusting the conveyor belt speed, and the question generator AI generates the question, "What is the current speed setting?" After the user answers, "The current speed is 120 meters per minute," the scribe AI records this. Ultimately, the moderator AI derives the solution, "Calibrate the speed control unit," and presents this to the user.

[1305] An example prompt is:

[1306] "Problem: Conveyor belt speed is unstable.

[1307] Q: What is the current speed setting for the conveyor belt?

[1308] Answer: The current speed is 120 meters per minute.

[1309] By using this system, when a problem occurs on the production line, it becomes possible to quickly identify the cause and find the optimal solution.

[1310] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1311] Step 1:

[1312] The server initializes the system and starts the moderator generation AI, question generation AI, and scribe generation AI. This prepares each AI to perform processes from problem analysis to presenting solutions. When the server performs its initial setup, it reads the initialization data, loads the AI ​​model, and sets it up so that it can be executed.

[1313] Step 2:

[1314] The user inputs the problem they want solved in natural language through a terminal. For example, they might input a problem such as "How can we quickly solve a problem that occurred on a production line?" This input data is then sent to the server by the terminal.

[1315] Step 3:

[1316] The server receives the task entered by the user and passes it to the moderator generation AI. The server converts the data into an appropriate format and provides it as input data to the moderator generation AI. This enables task analysis in the next step.

[1317] Step 4:

[1318] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, it generates topics such as "adjusting the speed of the equipment" or "reviewing maintenance procedures." Here, it performs natural language analysis of the input data and extracts and generates related topics.

[1319] Step 5:

[1320] The question generation AI generates questions based on the generated solution topic and sends them to the user's device via the server. For example, a question might be generated such as "What is the current speed setting of the conveyor belt?" In this step, the topic is explored in depth and appropriate questions are generated to gather more detailed information.

[1321] Step 6:

[1322] The user answers the questions generated through the terminal and sends the answer to the server. For example, the user might answer "Current speed is 120 meters per minute." The user's answer data is sent to the server and used for recording and organizing in the next step.

[1323] Step 7:

[1324] The scribe-generative AI receives the user's answers and records and organizes the discussion content. The server passes the answer data to the scribe-generative AI, which records the progress of the discussion and generates a log to move forward. For example, if the answer is "The current speed is 120 meters per minute," the content is recorded as text data.

[1325] Step 8:

[1326] The moderator AI advances the discussion based on the user's answers and generates questions based on the next topic. For example, the next question generated might be, "Have you calibrated the speed control unit?" Here, the user's answer data is analyzed and a process is performed to generate questions for the specific issues that need to be resolved next.

[1327] Step 9:

[1328] The user then enters the answer to the question again and sends it to the server via their device. For example, they might reply, "Yes, I have performed calibration. However, the speed is still unstable." This information is then sent to the server and analyzed again.

[1329] Step 10:

[1330] The server receives the second response, and the transcription AI records it and organizes the progress of the discussion. As mentioned above, the user's response is recorded as text data and used to advance the discussion in the next step.

[1331] Step 11:

[1332] The moderator AI generates a final solution, and the scribe AI organizes it and creates a final result document to present to the user. For example, a proposed solution might be, "Since the speed control unit remains unstable even after calibration, consider replacing the control unit."

[1333] Step 12:

[1334] The server sends the final results to the user's device, where the user can view them, thereby obtaining specific solutions and putting them into practice.

[1335] By following these steps, you can quickly find an effective solution to any problem that arises.

[1336] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1337] The problem-solving system of the present invention combines a server, terminals, various generative AIs, and an emotion engine to adjust the progress of appropriate questions and discussions based on the user's emotional state. The system's program processing is described in detail below.

[1338] overview

[1339] In this system, users input a task via a terminal, and the process involves a moderator AI, a question generator AI, a secretary AI, and an emotion engine working together to help solve the task. The server oversees the processing of these AIs and the emotion engine, and manages the user interface.

[1340] Program processing

[1341] 1. Initial Setup:

[1342] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine.

[1343] 2. Enter your assignment:

[1344] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[1345] 3. Receiving assignments:

[1346] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[1347] 4. Issue analysis and topic generation:

[1348] The moderator generation AI analyzes the problem and generates a solution topic, such as "current situation analysis" or "target demographic selection."

[1349] 5. Question generation:

[1350] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1351] 6. Posing and answering questions:

[1352] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[1353] 7. Emotion Analysis:

[1354] The server passes the user's response to the emotion engine, which analyzes the user's response and determines their emotion. For example, if the user is feeling anxious, the engine recognizes that emotion.

[1355] 8. Recording and Organizing:

[1356] The transcription-generating AI records and organizes the user's responses and analyzed emotions, resulting in organized discussion content and emotional data.

[1357] 9. Moderation of discussion:

[1358] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[1359] 10. Generate the following question:

[1360] The question generator AI generates new questions based on tailored instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[1361] 11. Posting and answering further questions:

[1362] The server sends the newly generated question to the user's device. The user then enters the answer to the question again and sends it to the server via their device. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[1363] 12. Emotion analysis and recording again:

[1364] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[1365] 13. Producing the final result:

[1366] The moderator's generative AI completes discussions on all topics and derives a final solution while taking into account emotional data.

[1367] 14. Presentation of results:

[1368] The transcription AI summarizes all the discussions and documents the final solution, which the server then sends to the user's device, where the user can review it.

[1369] Specific examples

[1370] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[1371] 1. Initial Setup:

[1372] The server initializes the system and starts each AI and emotion engine.

[1373] 2. Enter your assignment:

[1374] The user inputs the assignment through the terminal.

[1375] 3. Receiving and analyzing assignments:

[1376] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[1377] 4. Question generation and answering:

[1378] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[1379] 5. Sentiment analysis and discussion moderation:

[1380] The emotion engine analyzes the user's responses and recognizes, for example, whether they are feeling anxious. Based on this, the moderator generation AI adjusts the progress of the discussion and appropriately lowers the difficulty of the questions.

[1381] 6. Re-questioning and final results:

[1382] To the follow-up question, "What demographic do you envision as the target demographic for your new product?", the user answers, "We envision a demographic with a high affinity with young people." Taking into account the emotional data, the moderator's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people." The scribe's generative AI then organizes this and presents it to the user.

[1383] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently while taking into consideration their emotions.

[1384] The processing flow will be explained below.

[1385] Step 1:

[1386] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine, which completes preparations for each AI and emotion engine.

[1387] Step 2:

[1388] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[1389] Step 3:

[1390] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[1391] Step 4:

[1392] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[1393] Step 5:

[1394] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[1395] Step 6:

[1396] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[1397] Step 7:

[1398] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[1399] Step 8:

[1400] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1401] Step 9:

[1402] The server sends the generated question to the user's terminal, which displays the question to the user.

[1403] Step 10:

[1404] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[1405] Step 11:

[1406] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI and emotion engine.

[1407] Step 12:

[1408] The emotion engine analyzes the user's responses and assesses their emotions, for example recognizing that they are feeling stressed or anxious.

[1409] Step 13:

[1410] A generative AI transcription system records and organizes the user's responses and the emotions analyzed by the emotion engine.

[1411] Step 14:

[1412] Based on the analysis results of the emotion engine, the server feeds back the user's emotional state to the moderator AI, which then adjusts the progress of the discussion and instructs the question generator AI to generate questions that are adapted to the user's state.

[1413] Step 15:

[1414] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[1415] Step 16:

[1416] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[1417] Step 17:

[1418] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[1419] Step 18:

[1420] The device sends the user's response to the server, which then passes the response back to the transcription AI and emotion engine. The emotion engine then analyzes the user's emotions and adjusts the discussion again as necessary.

[1421] Step 19:

[1422] A generative AI scribe records new responses from users and updates the progress of the discussion.

[1423] Step 20:

[1424] A generative AI moderator will complete the discussion on all topics and come up with a final solution while taking into account sentiment data.

[1425] Step 21:

[1426] A generative AI transcription system will summarize all discussions and document the final solution.

[1427] Step 22:

[1428] The server sends the final result to the user's terminal, where the user can view the result.

[1429] These are the specific processing steps of the system that combines the emotion engine, which allows for efficient problem solving while responding to the user's emotional state.

[1430] Example 2

[1431] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1432] Conventional problem-solving support systems have the problem of being unable to take into account the user's emotional state and therefore unable to provide appropriate support to users who feel stressed or anxious. Furthermore, the progress of the discussion and adjustment of questions are uniform, making it difficult to provide appropriate support according to the individual situation of each user.

[1433] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a task through a terminal; a means for receiving the task input by the user; a means for a moderator generation system AI to analyze the task and generate a solution topic; a means for a question generation system AI to generate a question based on the solution topic; a means for an emotion engine to analyze emotions from the user's answers; a means for the user to answer the question generated through the terminal; a means for a scribe generation system AI to receive the user's answers and the analyzed emotions and record and organize the discussion content; a means for the moderator generation system AI to proceed with the discussion based on the user's answers and the analyzed emotions and derive a solution; and a means for presenting the final solution to the user's terminal. This makes it possible to conduct appropriate and effective questions and discussion while taking into account the user's emotional state.

[1434] A "user" is a person who uses the system to input tasks and answer questions.

[1435] A "terminal" is a hardware device on which a user inputs tasks and answers questions, and specifically is a computer device such as a PC, smartphone, or tablet.

[1436] The "server" is a computer system that oversees various generative AIs and emotion engines and manages the user interface.

[1437] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solution topics, and manages the progress of the discussion.

[1438] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[1439] An "emotion engine" is software that analyzes the emotions from users' responses and obtains emotional data.

[1440] The "generative AI for notation" is an artificial intelligence that receives user responses and emotional data analyzed by the emotion engine, and records and organizes the content of the discussion.

[1441] "Solution topics" are specific topics and steps for solving problems that are generated by the moderator generation AI by analyzing the user's issues.

[1442] A "question" is a specific question that the question generation AI generates based on the solution topic for the user to answer.

[1443] "Emotions" are psychological states such as stress, anxiety, or joy that a user expresses when answering questions.

[1444] The "solution" is the final problem-solving method that the moderator's generative AI derives based on the user's answers and emotions.

[1445] MODE FOR CARRYING OUT THE INVENTION

[1446] This invention is a system in which a user inputs a problem through a terminal, a server receives the problem, and various generative AIs and emotion engines work together to support problem solving. This system is characterized by its ability to adjust appropriate questions and discussion progress in real time, taking into account the user's emotional state. Detailed modes for implementing the invention are described below.

[1447] Hardware and software used

[1448] 1. Server

[1449] The server oversees various generative AIs (hosting AI, question generation AI, and secretary generation AI) and the emotion engine, and manages the user interface. The server communicates with devices using HTTP requests and WebSockets.

[1450] 2. Terminal

[1451] A terminal is a hardware device where users input tasks and answer questions. Terminals can be PCs, smartphones, tablets, etc. The terminal provides a user interface and displays information from the server.

[1452] 3. Generative AI and Emotion Engines

[1453] Generative AI for moderators: Analyzes the issues entered by users and generates solutions. Examples of AI models used include GPT-4.

[1454] Question generation AI: Generates specific questions based on the topics generated by the moderator generation AI.

[1455] Generative AI for transcription: Records user responses and emotional data, and organizes the discussion content.

[1456] Emotion engine: Software that analyzes emotions from user responses. An example is IBM Watson Tone Analyzer.

[1457] System processing flow

[1458] The specific flow of the system processing proceeds as follows:

[1459] 1. Initial Setup

[1460] The server initializes the system and starts each generative AI (for moderator, questioner, and secretary) and emotion engine.

[1461] 2. Enter your assignment

[1462] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[1463] 3. Receiving assignments

[1464] The device sends the user's input data to the server, which receives the data and passes it to the moderator generation AI.

[1465] 4. Issue analysis and topic generation

[1466] The moderator's AI analyzes the problem and generates relevant solution topics, such as "current situation analysis" and "target demographic selection."

[1467] 5. Question Generation

[1468] The question generator AI creates questions based on the topics generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[1469] 6. Posing and answering questions

[1470] The server sends the generated question to the user's device. The device displays the question to the user, and the user inputs an answer. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition." The device then sends the answer to the server.

[1471] 7. Emotion Analysis

[1472] The server passes the user's response to an emotion engine, which analyzes the user's emotions, for example, recognizing that the user is feeling anxious.

[1473] 8. Recording and Organizing

[1474] A generative AI transcription system records and organizes users' responses and analyzed emotions, thereby saving the discussion history and emotional data.

[1475] 9. Moderation of discussions

[1476] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[1477] 10. Next Question Generation

[1478] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[1479] 11. Posting and answering follow-up questions

[1480] The server sends the newly generated question to the user's device. The user then enters an answer to the question, for example, "We are targeting a demographic with a high affinity with young people." The device then sends this answer to the server.

[1481] 12. Re-analyzing and recording emotions

[1482] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[1483] 13. Producing the final result

[1484] The moderator's generative AI completes the discussion for all topics and derives a final solution while taking into account the sentiment data. For example, by integrating user responses and sentiment, it might suggest a solution such as "strengthen online advertising for new products and run a campaign targeted at younger generations."

[1485] 14. Presentation of Results

[1486] The transcription AI summarizes all the discussions and documents the final solution, and the server sends the final result to the user's device, where the user can review it.

[1487] Specific examples

[1488] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[1489] The user enters the assignment through the terminal.

[1490] The device sends the assignment to the server.

[1491] The server receives the assignment and passes it to the moderator generation AI.

[1492] The moderator generation AI analyzes the issues and generates topics such as "current situation analysis" and "target demographic selection."

[1493] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?"

[1494] A user responded, "Our current strength is online advertising, and our weakness is low brand recognition."

[1495] The emotion engine analyzes the user's responses and recognizes that they are feeling anxious.

[1496] The moderator's generative AI will use this information to adjust the progress of the discussion.

[1497] The question generation AI generates a follow-up question: "What type of target demographic do you envision for your new product?"

[1498] Users responded that they are "targeting a demographic with a high affinity with younger generations."

[1499] The host's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people."

[1500] A generative AI system for writing organizes this information and presents it to the user.

[1501] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1502] Step 1:

[1503] The server initializes the system and starts the moderator generation AI, question generation AI, clerk generation AI, and emotion engine. Specifically, it creates execution instances of each AI and emotion engine and allocates the necessary resources. This prepares all modules to run. The input is the system startup command, and the output is the initialization completion state of each AI and emotion engine.

[1504] Step 2:

[1505] The user inputs a problem through the terminal. A text input field is displayed on the terminal, and the user inputs the problem in natural language. For example, the problem is "How to consider a marketing strategy for new product development." The input is the user's problem text, and the output is the terminal sending the problem text to the server.

[1506] Step 3:

[1507] The device sends the user's input to the server. The server receives the input data using a communication protocol (e.g., HTTP request, WebSocket). Specifically, it parses the JSON-formatted data sent from the device and obtains the user's input assignment. The input is the user's assignment text, and the output is the assignment data received by the server.

[1508] Step 4:

[1509] The server passes the received problem data to the moderator generation AI, which analyzes this data and generates solution topics. For example, it uses natural language processing technology to generate topics such as "current situation analysis" and "target demographic selection." The input is the problem text, and the output is a list of generated topics.

[1510] Step 5:

[1511] The question generation AI generates specific questions based on the topics generated by the moderator generation AI. Specifically, it uses a generative AI model (e.g., GPT-4) to construct appropriate questions for each topic. For example, it generates a question such as, "What are the strengths and weaknesses of your current marketing strategy?" The input is a list of topics, and the output is a list of generated questions.

[1512] Step 6:

[1513] The server sends the generated questions to the user's terminal. The terminal displays the questions to the user so that the user can answer them. Specifically, the questions are displayed on the screen and the user enters text into an answer input field. The input is a list of questions, and the output is the questions displayed to the user.

[1514] Step 7:

[1515] The user answers questions through the device. The device collects the user's answers and sends them back to the server. For example, if the user answers "Our current strength is online advertising, and our weakness is low brand recognition," the text is collected. The input is the user's answer to the question, and the output is the answer data sent to the server.

[1516] Step 8:

[1517] The server passes the user's answer to the emotion engine, which analyzes the user's answer text to determine the emotion. Specifically, it uses natural language processing to identify the user's emotional state (e.g., anxiety, joy, anger, etc.). The input is the answer text, and the output is the analyzed emotion data.

[1518] Step 9:

[1519] The transcription-generative AI records and organizes user responses and analyzed emotions. Specifically, it stores responses and emotional data in a database and creates a document to organize the discussion content. The input is the response text and emotional data, and the output is the recorded and organized discussion content.

[1520] Step 10:

[1521] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. Specifically, if the user is feeling stressed, it will take measures such as lowering the difficulty of the questions. The input is emotional data, and the output is instructions for adjusting the progress of the discussion.

[1522] Step 11:

[1523] The question generation AI generates new questions based on the adjusted instructions. For example, it generates a question like, "What type of demographic is the new product targeted for?" The input is the adjusted instructions, and the output is the newly generated question.

[1524] Step 12:

[1525] The server sends a new question to the user's terminal, which displays the question to the user and prompts the user to enter the answer again. The input is the new question, and the output is the question displayed to the user.

[1526] Step 13:

[1527] The user answers a new question and sends it to the server via their device. For example, they might answer, "We are targeting a demographic with a high affinity with young people." The input is the user's new answer, and the output is the answer data sent to the server.

[1528] Step 14:

[1529] The server passes the new response back to the emotion engine, which analyzes it. The analysis results are then passed back to the transcription generation AI, which records the discussion content and emotion data. The input is the new response data, and the output is the analyzed emotion data.

[1530] Step 15:

[1531] The moderator generative AI completes the discussion for all topics and derives a final solution. Specifically, it integrates user responses and emotional data to propose an appropriate solution. The input is all discussion data and emotional data, and the output is the final solution.

[1532] Step 16:

[1533] The transcription generator AI documents the final solution, and the server sends it to the user's device. The user then checks the final result on their device. The input is the final solution, and the output is the result presented to the user.

[1534] (Application example 2)

[1535] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1536] Conventional problem-solving systems proceed without taking into account the user's emotional state, which can cause stress or anxiety for the user, making efficient problem-solving difficult. Furthermore, particularly on online shopping sites, the user's emotions during the purchasing experience cannot be properly addressed, potentially resulting in a decrease in satisfaction. Therefore, there is a need for a system that can analyze the user's emotional state and, based on that, guide the discussion and provide appropriate information.

[1537] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1538] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for an emotion engine to analyze emotions from the users' answers and adjust the progress of the discussion based on that. This makes it possible to solve problems more efficiently while taking the users' emotional state into consideration, and to improve user satisfaction, especially on online shopping sites.

[1539] A "user" is an entity that uses the system to input questions and provide answers.

[1540] A "terminal" is a device that allows a user to input tasks and answer questions, and includes smartphones, personal computers, etc.

[1541] The "server" is a computer system that oversees the entire system and manages the processing of various generative AIs and emotion engines.

[1542] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solutions, and advances the discussion.

[1543] "Question generation AI" is an artificial intelligence that generates specific questions based on the solution topics generated by the moderator generation AI.

[1544] "Generative notation AI" is an artificial intelligence that receives user responses, records and organizes the discussion, and documents the final solution.

[1545] The "emotion engine" is an engine that analyzes the user's emotional state from their answers and inputs, and adjusts the progress of the discussion and the content of the questions based on that.

[1546] "Solution topics" are specific discussion themes or question subjects that are generated by the moderator generation AI by analyzing the issues.

[1547] A "question" is a question posed to the user that is generated by the question generation AI based on the solution topic.

[1548] An "answer" is information provided by a user by answering a question generated through a terminal.

[1549] A "discussion" is a process of discussion formed through an exchange of questions and answers between the user and the generative AI.

[1550] A "solution" is the final answer or proposal to the user's problem that is arrived at as a result of the discussion.

[1551] The system embodying this invention combines a server, a terminal, various generative AIs, and an emotion engine to support problem solving while taking into account the user's emotional state. This system contributes to improving the user experience, especially on online shopping sites.

[1552] System configuration

[1553] The system's hardware configuration includes the smartphone or PC used by the user and a server. The software configuration includes an AI for generating moderators, an AI for generating questions, an AI for generating notes, and an emotion engine. Specifically, the following software is used:

[1554] Emotion engine: e.g., emotion_recognition library

[1555] Generative AI: OpenAI's GPT model

[1556] Program processing

[1557] During the initial setup phase, the server initializes various generative AIs and emotion engines. When a user inputs a task via their device, the task is received by the server and passed to the moderator generative AI. The moderator generative AI analyzes the user's input and generates a solution topic. For example, if the discussion is about "marketing strategies for a new product," topics such as "current situation analysis" and "target demographic selection" will be generated.

[1558] The question generation AI then generates specific questions based on the topic being addressed. These questions are sent to the user's device, and the user inputs the corresponding answers. For example, in response to the question, "What are the strengths and weaknesses of your current marketing strategy?", the AI ​​will answer, "The current strength is online advertising, and the weakness is low brand awareness."

[1559] The user's answers are analyzed by an emotion engine to determine the emotions the user is feeling (e.g., anxiety or stress). The emotion data analyzed by the emotion engine is fed back to the moderator generation AI, which adjusts the progress of the discussion based on that. For example, if the user is feeling high stress, the difficulty of the questions may be lowered.

[1560] Specific examples

[1561] Specifically, imagine a situation where a user feels uneasy after reading a product review while shopping online. When the user types the question, "What are the advantages of this product?", the emotion engine detects the user's uneasiness, and the generative AI provides information that provides reassurance.

[1562] Example prompt sentence:

[1563] text

[1564] A user is concerned about this statement: 'I've been reading the reviews for this product and they're mostly negative.' How should I respond?

[1565] This allows the system to provide a better purchasing experience while taking into consideration the user's feelings. The generative transcription AI also organizes the discussions and documents the final solution, which is then presented to the user. For example, the system may arrive at a solution such as "strengthen online advertising for new products and run a campaign specifically targeted at younger generations."

[1566] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1567] Step 1:

[1568] The server initializes the system and starts the moderator generation AI, question generation AI, clerk generation AI, and emotion engine. During this initialization step, the environment is configured and the necessary libraries and modules are loaded for the system to operate properly. Specifically, the parameters of the various AI models are set and the emotion engine's analysis model is loaded. The necessary network resources are also secured and connections are prepared.

[1569] Step 2:

[1570] The user inputs the problem they want to solve in natural language through the device. For example, they might input "I would like to consider a marketing strategy for a new product." The device then sends this input to the server. At this stage, the text data entered by the user becomes the input, and the text data sent from the device to the server becomes the output.

[1571] Step 3:

[1572] The server receives the task entered by the user and passes it to the moderator generation AI. The moderator generation AI analyzes the task and generates a solution topic. For example, it generates topics such as "current situation analysis" and "target demographic selection." In this step, the user input (natural language text) received by the server becomes the input, and the generated solution topic (a list in natural language) becomes the output. Specifically, it uses natural language processing technology to break down the task into topics.

[1573] Step 4:

[1574] The question generation AI generates specific questions based on the solution topic generated by the moderator generation AI. For example, a question such as "What are the strengths and weaknesses of your current marketing strategy?" is generated. In this step, the solution topic is input, and a specific question (natural language text) based on it is output.

[1575] Step 5:

[1576] The server sends the generated question to the user's device. The user inputs an answer to the question through the device. For example, the user might input an answer such as, "Our current strength is online advertising, and our weakness is low brand recognition." In this step, the generated question is the input, and the answer (natural language text) entered by the user is the output. Specifically, the question is notified to the user's device, and the user answers it.

[1577] Step 6:

[1578] The server receives the user's response and passes it to the emotion engine for analysis. The emotion engine analyzes the user's emotional state from the response and returns emotion data, such as "anxiety" or "stress," to the server. In this step, the user's response (natural language text) is input, and the analyzed emotion data (e.g., anxiety or stress) is output. Specifically, the emotion analysis model analyzes the text data and labels the emotion.

[1579] Step 7:

[1580] The analyzed emotion data is passed to the transcription-generating AI, which records and organizes the user's responses and emotion data. In this step, emotion data and user responses are input, and organized discussion content (natural language text) is output. Specifically, responses and emotion data are recorded in chronological order.

[1581] Step 8:

[1582] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it lowers the difficulty of the next question. In this step, emotional data is input, and adjusted instructions for the discussion progress are output. Specifically, the generative AI model makes adjustments according to the emotional data.

[1583] Step 9:

[1584] The question generation AI generates new questions based on the adjusted instructions. For example, a question such as, "What marketing strategy are you considering next?" is generated. In this step, the adjusted discussion flow instructions are the input, and the generated new question (natural language text) is the output.

[1585] Step 10:

[1586] The server sends the newly generated question to the user's device. The user again enters an answer to the question and sends it to the server via the device. For example, the user might enter an answer such as, "We expect the target demographic for our new product to be young people." In this step, the new question is the input, and the user's answer (natural language text) is the output.

[1587] Step 11:

[1588] The server passes the new answers to the emotion engine for re-analysis, and the transcription generation AI re-records the discussion content together with the analyzed emotion data. In this step, the user's new answers are input, and the reorganized discussion content is output. Specifically, the answers and emotion data are re-recorded, and the discussion content is periodically updated.

[1589] Step 12:

[1590] The moderator generative AI completes the discussion for all topics and derives a final solution while taking into account the emotional data. In this step, all discussion content and emotional data are input, and the final solution is output. Specifically, the generative AI model performs a comprehensive analysis and proposes an appropriate solution to the user.

[1591] Step 13:

[1592] The scribe generation AI summarizes the entire discussion and documents the final solution. The server sends the final result to the user's device, where the user confirms the result. In this step, the discussion content and the solution are input, and the summarized final solution (document) is output. In concrete terms, the generated document is presented to the user, who then confirms it.

[1593] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1594] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1595] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1596] [Fourth embodiment]

[1597] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1598] 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.

[1599] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[1600] 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.

[1601] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1602] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1603] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1604] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1605] 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.

[1606] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1607] 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.

[1608] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1609] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1610] The problem-solving system of the present invention is implemented using a server, a terminal, and various AIs. By using this system, users can solve problems efficiently and effectively. Below, the processing of the system's program is explained in detail in natural language.

[1611] overview

[1612] This system supports the process in which users input a problem via a terminal, and multiple generative AIs derive appropriate questions and solutions for that problem. The server plays a central role in the system, managing the processing of various AIs and the user interface.

[1613] Program processing

[1614] 1. Initial Setup:

[1615] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI.

[1616] 2. Enter your assignment:

[1617] The user inputs the problem to be solved in natural language through the terminal. For example, the user might input "How to consider a marketing strategy for new product development."

[1618] 3. Receiving assignments:

[1619] The device sends the user's input to the server, which receives it and passes it to the moderator generation AI.

[1620] 4. Issue analysis and topic generation:

[1621] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, topics such as "current situation analysis" and "target demographic selection" are generated.

[1622] 5. Question generation:

[1623] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1624] 6. Posing and answering questions:

[1625] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[1626] 7. Recording and Organizing:

[1627] The server passes the user's answers to the scribe-generative AI, which records and organizes them, creating a progress and detailed record of the discussion.

[1628] 8. Discussion process:

[1629] The moderator AI will advance the discussion based on the user's answers. It will then have the question generator AI generate questions based on the next topic. For example, the next question generated would be, "What type of demographic do you envision as the target demographic for your new product?"

[1630] 9. Restatement and Answer of Question:

[1631] The user again enters the answer to the question and sends it to the server via the terminal. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[1632] 10. Record and organize again:

[1633] The server receives the second response, and the transcription generation AI records it and organizes the progress of the discussion.

[1634] 11. Producing the final result:

[1635] The moderator AI generates the final solution, and the secretary AI organizes it to create the final result document to be presented to the user.

[1636] 12. Presentation of results:

[1637] The server sends the final result to the user's terminal, where the user can view it.

[1638] Specific examples

[1639] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[1640] 1. Initial Setup:

[1641] The server initializes the system and starts each AI.

[1642] 2. Enter your assignment:

[1643] The user inputs the assignment through the terminal.

[1644] 3. Receiving and analyzing assignments:

[1645] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[1646] 4. Question generation and answering:

[1647] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[1648] 5. Recording and Proceedings:

[1649] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[1650] 6. Re-answers and final results:

[1651] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[1652] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[1653] The processing flow will be explained below.

[1654] Step 1:

[1655] The server starts up and initializes the moderator generation AI, question generation AI, and secretary generation AI. This completes preparation for each AI to operate.

[1656] Step 2:

[1657] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[1658] Step 3:

[1659] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[1660] Step 4:

[1661] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[1662] Step 5:

[1663] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[1664] Step 6:

[1665] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[1666] Step 7:

[1667] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[1668] Step 8:

[1669] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1670] Step 9:

[1671] The server sends the generated question to the user's terminal, which displays the question to the user.

[1672] Step 10:

[1673] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[1674] Step 11:

[1675] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI.

[1676] Step 12:

[1677] The transcription-generating AI receives the user's responses, records and organizes them, and the organized discussion content is obtained.

[1678] Step 13:

[1679] The server feeds the user's answers back to the moderator generation AI and instructs it to move the discussion forward to the next topic.

[1680] Step 14:

[1681] The moderator generation AI instructs the question generation AI to generate questions to advance the discussion on the next topic (e.g., "target demographic selection").

[1682] Step 15:

[1683] A question generation AI generates new questions, such as, "What kind of target demographic do you envision for your new product?"

[1684] Step 16:

[1685] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[1686] Step 17:

[1687] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[1688] Step 18:

[1689] The device sends the user's answer to the server, which then passes the answer back to the scribe generation AI.

[1690] Step 19:

[1691] A generative AI scribe records new responses from users and updates the progress of the discussion.

[1692] Step 20:

[1693] A moderator generative AI will conclude discussions on all topics and come up with a final solution.

[1694] Step 21:

[1695] A generative AI transcription system will summarize all discussions and document the final solution.

[1696] Step 22:

[1697] The server sends the final result to the user's terminal, where the user can view the result.

[1698] The above are the specific processing steps of the system, which allow users to solve problems efficiently by following a series of steps.

[1699] Example 1

[1700] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1701] Today's users spend a lot of time and effort solving complex problems. Solving these problems requires extensive knowledge and experience, and in many cases, the advice and cooperation of experts is essential. Furthermore, the process of finding a solution requires efficient discussion and organization. However, many users do not have the means to receive appropriate assistance. As a result, problem solving is not carried out efficiently and effectively, and the burden on users increases.

[1702] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1703] In this invention, the server includes means for a user to input a problem through a terminal, means for the server to receive the problem input by the user, means for a moderator AI to analyze the problem and generate a solution topic, means for a question generator AI to generate a question based on the solution topic, means for the server to send the generated question to the user's terminal, means for the user to answer the question through the terminal, means for the server to send the user's answer to the clerk generator AI, means for the clerk generator AI to record and organize the user's answer, means for the moderator AI to proceed with the discussion based on the user's answer and derive a solution, and means for presenting the final solution to the user's terminal, thereby enabling users to solve problems efficiently and effectively.

[1704] "User" means an individual or entity that uses the system to enter a problem and request a solution.

[1705] A "terminal" is an electronic device that allows a user to input tasks and answer questions from the system.

[1706] A "problem" is a problem or question that a user wants solved.

[1707] The "server" is a central computer that manages the entire system and processes various generative AIs.

[1708] "Generative AI for hosting" is an artificial intelligence that analyzes users' issues and generates appropriate solution topics.

[1709] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[1710] "Generative AI for transcription" is an artificial intelligence that records and organizes users' responses.

[1711] "Solution topics" are important themes or items for discussion and solutions that the moderator generation AI has derived by analyzing the issue.

[1712] A "question" is a specific problem or inquiry generated by the question generation AI.

[1713] An "answer" is a response or opinion that a user enters in response to a question.

[1714] A "discussion" is a discussion for solving a problem that progresses based on the user's answers.

[1715] A "solution" is the final solution or proposal for solving a problem.

[1716] The problem-solving system of the present invention is implemented using a server, a terminal, and various generative AIs. By using this system, users can solve problems efficiently and effectively.

[1717] System configuration

[1718] The system consists of a terminal operated by the user, a server, and multiple generative AIs (moderator AI, question generator AI, and secretary generator AI). The server plays a central role in the system, managing the processing of the various generative AIs and the user interface.

[1719] Hardware and Software Use

[1720] The server is a high-performance computing device used to process each generative AI at high speed. For example, it is recommended that the server be equipped with a powerful CPU and GPU. Machine learning frameworks such as TensorFlow and PyTorch are used to process the AI ​​models.

[1721] The terminal is used by the user to input tasks and answer generated questions. The terminal can be a PC, smartphone, tablet, etc. The software used can be a web browser or a dedicated application.

[1722] Program processing

[1723] The program processes as follows: First, the server initializes the system and starts the moderator generation AI, question generation AI, and clerk generation AI. Next, the user inputs the problem they want solved in natural language through their device. The device sends this input to the server, which receives it and passes it on to the moderator generation AI.

[1724] The moderator AI analyzes the problem and generates a solution topic. The question generator AI then generates specific questions based on the solution topic, which are sent to the user's device via the server. The user enters answers to the generated questions and sends them to the server via their device. The server receives these and passes them to the scribe generator AI for recording and organizing.

[1725] Specific examples

[1726] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be executed.

[1727] 1. Enter your assignment:

[1728] The user inputs "How to consider a marketing strategy for new product development" through the terminal.

[1729] 2. Receiving and analyzing assignments:

[1730] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[1731] 3. Question generation and answering:

[1732] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[1733] 4. Recording and Proceedings:

[1734] The scribe-generating AI records the user's answers. The moderator-generating AI then generates the question, "What kind of demographic do you envision as the target demographic for your new product?"

[1735] 5. Re-answer and final result:

[1736] The user responded, "We are targeting a demographic with a high affinity with younger generations." Ultimately, the moderator AI came up with the solution of "strengthening online advertising for the new product and running a campaign specifically targeted at younger generations," which the scribe AI then organized and presented to the user.

[1737] The above is a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently.

[1738] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1739] Step 1:

[1740] The server initializes the system and starts the moderator generation AI, question generation AI, and secretary generation AI. The AI ​​models and configuration files are loaded, and each AI module becomes ready for use.

[1741] Input: server

[1742] Output: Initialized AI for each generator

[1743] Step 2:

[1744] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[1745] Input: user

[1746] Output: The assignment text typed into the terminal

[1747] Step 3:

[1748] The terminal sends the assignment text entered by the user to the server. The terminal converts the input data into an appropriate format (e.g., JSON) and prepares it for transmission to the server.

[1749] Input: The assignment text entered into the terminal

[1750] Output: Issue data sent to the server

[1751] Step 4:

[1752] The server analyzes the received assignment data, extracts the JSON format data, and passes it to the moderator generation AI. During the analysis process, the necessary data fields are extracted and the format is organized.

[1753] Input: Issue data sent to the server

[1754] Output: Organized data passed to the moderator generation AI

[1755] Step 5:

[1756] The moderator's generative AI analyzes the problem and generates a solution topic. It uses natural language processing technology to break down the problem and extract the main themes and topics in text format.

[1757] Input: Organized data passed to the host generation AI

[1758] Output: Generated resolution topics

[1759] Step 6:

[1760] The question generation AI generates specific questions based on the solution topic generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[1761] Input: Generated resolution topic

[1762] Output: Generated questions

[1763] Step 7:

[1764] The server sends the questions generated by the question generation AI to the user's device, where the questions are formatted appropriately (e.g., text or JSON) and presented on the device.

[1765] Input: Generated question

[1766] Output: The question sent to the user's device

[1767] Step 8:

[1768] The user inputs answers to questions displayed on the terminal. For example, they might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[1769] Input: The answer entered by the user

[1770] Output: Answers typed into the terminal

[1771] Step 9:

[1772] The terminal sends the user's answer to the server, which converts the answer data into an appropriate format and sends it to the server.

[1773] Input: Answer typed into the terminal

[1774] Output: Response data sent to the server

[1775] Step 10:

[1776] The server receives the user's answer and passes it to the clerk's AI generator, which records the answer as text data and stores it in a database.

[1777] Input: Response data sent to the server

[1778] Output: Answer data passed to the transcription generation AI

[1779] Step 11:

[1780] The transcription AI records the response data and organizes the content of the responses, creating a detailed record of the progress of the discussion.

[1781] Input: Answer data passed to the transcription generation AI

[1782] Output: Recorded and organized response data

[1783] Step 12:

[1784] The moderator AI will proceed with the discussion based on the recorded and organized response data. The question generator AI will generate new questions based on the next topic. For example, a question might be generated: "What type of demographic do you envision as the target demographic for your new product?"

[1785] Input: Recorded and organized response data

[1786] Output: Next question

[1787] Step 13:

[1788] The server then sends the newly generated question to the user's terminal, and the user again enters an answer to the question and sends it again to the server via the terminal.

[1789] Input: The newly generated question

[1790] Output: The new question sent to the user's device.

[1791] Step 14:

[1792] The server again receives the user's input answers and sends them to the transcription AI for recording and organizing, thereby keeping the progress of the discussion up to date.

[1793] Input: Answer data sent by the user again

[1794] Output: Answer data sent again to the transcription generation AI

[1795] Step 15:

[1796] After the discussion progresses and a final solution is reached, the moderator AI documents it, and the scribe AI organizes this document and presents it to the user as the final result.

[1797] Input: Solution data recorded by the transcription-generative AI

[1798] Output: A cleaned up final result document

[1799] Step 16:

[1800] The server sends the final document to the user's device, where the user can download or view it for review.

[1801] Input: Final result document

[1802] Output: The final result document sent to the user's device.

[1803] (Application example 1)

[1804] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1805] There is a need to quickly and accurately resolve problems that occur on production lines. In particular, there is a problem in that it is difficult for production line operators to immediately identify the cause of the problem and find the optimal solution, so the problem is left unresolved for a long time, resulting in a decline in production efficiency.

[1806] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1807] In this invention, the server includes: means for a user to input a problem through a terminal; means for the server to receive the problem input by the user; means for a moderator AI to analyze the problem and generate a solution topic; means for a question generator AI to generate questions based on the solution topic; means for users to answer the questions generated through their terminals; means for a scribe generator AI to receive the users' answers and record and organize the discussion content; means for the moderator AI to advance the discussion based on the users' answers and derive a solution; means for presenting the final solution to the user's terminal; and means for supporting problem solving on the production line, including means for analyzing data from production equipment and proposing optimal solutions in real time. This makes it possible to quickly identify the cause of a problem when it occurs on the production line and obtain an optimal solution.

[1808] "User" refers to the person who operates the system, inputs problems, and receives solutions.

[1809] A "terminal" is a device used by a user to input tasks and check solutions, and includes PCs, tablets, smartphones, etc.

[1810] "Server" refers to the central system that manages the entire system, receives and analyzes data, and initializes and operates the generative AI.

[1811] "Generative AI for moderators" refers to an artificial intelligence system that analyzes input issues, generates solution topics, and supports the progress of discussions.

[1812] "Question generation AI" refers to an artificial intelligence system that generates appropriate questions based on the solution topic generated by the moderator generation AI.

[1813] "Generative AI for transcription" refers to an artificial intelligence system that receives user responses and records and organizes the content of the discussion.

[1814] "Solution topics" refer to specific items for consideration toward solutions that are generated as a result of the moderator generation AI analyzing the issue.

[1815] "Production equipment" refers to the machines and devices used on the production line, and refers to the hardware used to carry out the production process.

[1816] "Real-time" refers to the instantaneous receipt of data, analysis, and provision of solutions.

[1817] "Production line" refers to the series of processes that produce a product throughout the manufacturing process.

[1818] An "optimal solution" refers to the most effective and efficient way to address a problem, including suggestions derived by generative AI.

[1819] "Means to support problem-solving" refers to methods and technologies that use AI to analyze and propose solutions to various problems that arise on production lines.

[1820] In order to implement the present invention, the following system is constructed.

[1821] First, the server manages the entire system and initializes each generative AI. The server uses generative AI models such as GPT-4 to analyze problems, generate solution topics, and generate questions.

[1822] Users input their issues through their devices, and this input data is sent to the server. The input issues are analyzed by a moderator generation AI, which generates a solution topic. For example, if the issue is "How to quickly resolve a malfunction that occurred on the production line," the generated solution topics include "adjusting the speed of the equipment" and "reviewing maintenance procedures."

[1823] Next, the question generation AI creates a question to be presented to the user based on the generated solution topic. For example, a specific question such as "What is the current speed setting of the conveyor belt?" is generated. This question is presented to the user via their terminal, and the user inputs an answer.

[1824] The user's answers are sent back to the server, where they are recorded and organized by the transcription generation AI. This process ensures that the progress of the discussion is properly recorded and necessary information is accumulated.

[1825] The moderator AI advances the discussion based on the user's answers, generates more detailed questions, and arrives at a final solution, which it then organizes in cooperation with the secretary AI. The final solution is then presented to the user via their device.

[1826] As a specific example, if the problem is an unstable conveyor belt speed, the moderator AI generates a topic about adjusting the conveyor belt speed, and the question generator AI generates the question, "What is the current speed setting?" After the user answers, "The current speed is 120 meters per minute," the scribe AI records this. Ultimately, the moderator AI derives the solution, "Calibrate the speed control unit," and presents this to the user.

[1827] An example prompt is:

[1828] "Problem: Conveyor belt speed is unstable.

[1829] Q: What is the current speed setting for the conveyor belt?

[1830] Answer: The current speed is 120 meters per minute.

[1831] By using this system, when a problem occurs on the production line, it becomes possible to quickly identify the cause and find the optimal solution.

[1832] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1833] Step 1:

[1834] The server initializes the system and starts the moderator generation AI, question generation AI, and scribe generation AI. This prepares each AI to perform processes from problem analysis to presenting solutions. When the server performs its initial setup, it reads the initialization data, loads the AI ​​model, and sets it up so that it can be executed.

[1835] Step 2:

[1836] The user inputs the problem they want solved in natural language through a terminal. For example, they might input a problem such as "How can we quickly solve a problem that occurred on a production line?" This input data is then sent to the server by the terminal.

[1837] Step 3:

[1838] The server receives the task entered by the user and passes it to the moderator generation AI. The server converts the data into an appropriate format and provides it as input data to the moderator generation AI. This enables task analysis in the next step.

[1839] Step 4:

[1840] The moderator generation AI analyzes the problem and generates a topic to solve it. For example, it generates topics such as "adjusting the speed of the equipment" or "reviewing maintenance procedures." Here, it performs natural language analysis of the input data and extracts and generates related topics.

[1841] Step 5:

[1842] The question generation AI generates questions based on the generated solution topic and sends them to the user's device via the server. For example, a question might be generated such as "What is the current speed setting of the conveyor belt?" In this step, the topic is explored in depth and appropriate questions are generated to gather more detailed information.

[1843] Step 6:

[1844] The user answers the questions generated through the terminal and sends the answer to the server. For example, the user might answer "Current speed is 120 meters per minute." The user's answer data is sent to the server and used for recording and organizing in the next step.

[1845] Step 7:

[1846] The scribe-generative AI receives the user's answers and records and organizes the discussion content. The server passes the answer data to the scribe-generative AI, which records the progress of the discussion and generates a log to move forward. For example, if the answer is "The current speed is 120 meters per minute," the content is recorded as text data.

[1847] Step 8:

[1848] The moderator AI advances the discussion based on the user's answers and generates questions based on the next topic. For example, the next question generated might be, "Have you calibrated the speed control unit?" Here, the user's answer data is analyzed and a process is performed to generate questions for the specific issues that need to be resolved next.

[1849] Step 9:

[1850] The user then enters the answer to the question again and sends it to the server via their device. For example, they might reply, "Yes, I have performed calibration. However, the speed is still unstable." This information is then sent to the server and analyzed again.

[1851] Step 10:

[1852] The server receives the second response, and the transcription AI records it and organizes the progress of the discussion. As mentioned above, the user's response is recorded as text data and used to advance the discussion in the next step.

[1853] Step 11:

[1854] The moderator AI generates a final solution, and the scribe AI organizes it and creates a final result document to present to the user. For example, a proposed solution might be, "Since the speed control unit remains unstable even after calibration, consider replacing the control unit."

[1855] Step 12:

[1856] The server sends the final results to the user's device, where the user can view them, thereby obtaining specific solutions and putting them into practice.

[1857] By following these steps, you can quickly find an effective solution to any problem that arises.

[1858] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1859] The problem-solving system of the present invention combines a server, terminals, various generative AIs, and an emotion engine to adjust the progress of appropriate questions and discussions based on the user's emotional state. The system's program processing is described in detail below.

[1860] overview

[1861] In this system, users input a task via a terminal, and the process involves a moderator AI, a question generator AI, a secretary AI, and an emotion engine working together to help solve the task. The server oversees the processing of these AIs and the emotion engine, and manages the user interface.

[1862] Program processing

[1863] 1. Initial Setup:

[1864] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine.

[1865] 2. Enter your assignment:

[1866] The user inputs the problem to be solved in natural language through the terminal, for example, "How to consider a marketing strategy for new product development."

[1867] 3. Receiving assignments:

[1868] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[1869] 4. Issue analysis and topic generation:

[1870] The moderator generation AI analyzes the problem and generates a solution topic, such as "current situation analysis" or "target demographic selection."

[1871] 5. Question generation:

[1872] The question generation AI generates questions based on the topics generated by the moderator AI, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1873] 6. Posing and answering questions:

[1874] The server sends the generated questions to the user's device. The user then enters answers to the questions and sends them to the server via the device. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition."

[1875] 7. Emotion Analysis:

[1876] The server passes the user's response to the emotion engine, which analyzes the user's response and determines their emotion. For example, if the user is feeling anxious, the engine recognizes that emotion.

[1877] 8. Recording and Organizing:

[1878] The transcription-generating AI records and organizes the user's responses and analyzed emotions, resulting in organized discussion content and emotional data.

[1879] 9. Moderation of discussion:

[1880] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[1881] 10. Generate the following question:

[1882] The question generator AI generates new questions based on tailored instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[1883] 11. Posting and answering further questions:

[1884] The server sends the newly generated question to the user's device. The user then enters the answer to the question again and sends it to the server via their device. For example, the user might answer, "We are targeting a demographic with a high affinity with young people."

[1885] 12. Emotion analysis and recording again:

[1886] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[1887] 13. Producing the final result:

[1888] The moderator's generative AI completes discussions on all topics and derives a final solution while taking into account emotional data.

[1889] 14. Presentation of results:

[1890] The transcription AI summarizes all the discussions and documents the final solution, which the server then sends to the user's device, where the user can review it.

[1891] Specific examples

[1892] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[1893] 1. Initial Setup:

[1894] The server initializes the system and starts each AI and emotion engine.

[1895] 2. Enter your assignment:

[1896] The user inputs the assignment through the terminal.

[1897] 3. Receiving and analyzing assignments:

[1898] The server receives the assignment and passes it to the moderator generation AI, which analyzes the assignment and generates topics such as "current situation analysis" and "target demographic selection."

[1899] 4. Question generation and answering:

[1900] The question generation AI generates the question, "What are the strengths and weaknesses of your current marketing strategy?" The user answers, "Our current strength is online advertising, and our weakness is low brand awareness."

[1901] 5. Sentiment analysis and discussion moderation:

[1902] The emotion engine analyzes the user's responses and recognizes, for example, whether they are feeling anxious. Based on this, the moderator generation AI adjusts the progress of the discussion and appropriately lowers the difficulty of the questions.

[1903] 6. Re-questioning and final results:

[1904] To the follow-up question, "What demographic do you envision as the target demographic for your new product?", the user answers, "We envision a demographic with a high affinity with young people." Taking into account the emotional data, the moderator's generative AI derives the final solution: "Strengthen online advertising for the new product and run a campaign specifically targeted at young people." The scribe's generative AI then organizes this and presents it to the user.

[1905] The above is a detailed description of a specific embodiment for carrying out the present invention. This system allows users to receive support for solving problems efficiently while taking into consideration their emotions.

[1906] The processing flow will be explained below.

[1907] Step 1:

[1908] The server initializes the system and starts the moderator generation AI, question generation AI, secretary generation AI, and emotion engine, which completes preparations for each AI and emotion engine.

[1909] Step 2:

[1910] The terminal displays a login screen to the user, where the user enters credentials, and the terminal sends the entered credentials to the server.

[1911] Step 3:

[1912] The server verifies the user's credentials and, if authentication is successful, displays the main screen on the user's device, allowing the user to access the system.

[1913] Step 4:

[1914] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[1915] Step 5:

[1916] The device sends the user's input to the server, which receives the input and passes it to the moderator generation AI.

[1917] Step 6:

[1918] The moderator generation AI analyzes the tasks received from users and generates main topics, such as "current situation analysis" and "target demographic selection."

[1919] Step 7:

[1920] The server processes the topic received from the moderator generation AI and instructs it to pass it on to the question generation AI.

[1921] Step 8:

[1922] A question generator AI generates specific questions based on each topic, such as, "What are the strengths and weaknesses of your current marketing strategy?"

[1923] Step 9:

[1924] The server sends the generated question to the user's terminal, which displays the question to the user.

[1925] Step 10:

[1926] The user inputs answers to the questions through the terminal, for example, "Our current strength is online advertising, and our weakness is low brand recognition."

[1927] Step 11:

[1928] The device sends the user's answer to the server, which receives the answer and passes it to the transcription generation AI and emotion engine.

[1929] Step 12:

[1930] The emotion engine analyzes the user's responses and assesses their emotions, for example recognizing that they are feeling stressed or anxious.

[1931] Step 13:

[1932] A generative AI transcription system records and organizes the user's responses and the emotions analyzed by the emotion engine.

[1933] Step 14:

[1934] Based on the analysis results of the emotion engine, the server feeds back the user's emotional state to the moderator AI, which then adjusts the progress of the discussion and instructs the question generator AI to generate questions that are adapted to the user's state.

[1935] Step 15:

[1936] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[1937] Step 16:

[1938] The server sends the newly generated question to the user's terminal, which displays the question to the user.

[1939] Step 17:

[1940] The user answers a new question through the device. For example, they might answer, "We are targeting a demographic with a high affinity with young people."

[1941] Step 18:

[1942] The device sends the user's response to the server, which then passes the response back to the transcription AI and emotion engine. The emotion engine then analyzes the user's emotions and adjusts the discussion again as necessary.

[1943] Step 19:

[1944] A generative AI scribe records new responses from users and updates the progress of the discussion.

[1945] Step 20:

[1946] A generative AI moderator will complete the discussion on all topics and come up with a final solution while taking into account sentiment data.

[1947] Step 21:

[1948] A generative AI transcription system will summarize all discussions and document the final solution.

[1949] Step 22:

[1950] The server sends the final result to the user's terminal, where the user can view the result.

[1951] These are the specific processing steps of the system that combines the emotion engine, which allows for efficient problem solving while responding to the user's emotional state.

[1952] Example 2

[1953] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1954] Conventional problem-solving support systems have the problem of being unable to take into account the user's emotional state and therefore unable to provide appropriate support to users who feel stressed or anxious. Furthermore, the progress of the discussion and adjustment of questions are uniform, making it difficult to provide appropriate support according to the individual situation of each user.

[1955] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a task through a terminal; a means for receiving the task input by the user; a means for a moderator generation system AI to analyze the task and generate a solution topic; a means for a question generation system AI to generate a question based on the solution topic; a means for an emotion engine to analyze emotions from the user's answers; a means for the user to answer the question generated through the terminal; a means for a scribe generation system AI to receive the user's answers and the analyzed emotions and record and organize the discussion content; a means for the moderator generation system AI to proceed with the discussion based on the user's answers and the analyzed emotions and derive a solution; and a means for presenting the final solution to the user's terminal. This makes it possible to conduct appropriate and effective questions and discussion while taking into account the user's emotional state.

[1956] A "user" is a person who uses the system to input tasks and answer questions.

[1957] A "terminal" is a hardware device on which a user inputs tasks and answers questions, and specifically is a computer device such as a PC, smartphone, or tablet.

[1958] The "server" is a computer system that oversees various generative AIs and emotion engines and manages the user interface.

[1959] "Generative AI for moderators" is an artificial intelligence that analyzes the issues entered by users, generates solution topics, and manages the progress of the discussion.

[1960] "Question generation AI" is an artificial intelligence that generates specific questions based on the topics generated by the moderator generation AI.

[1961] An "emotion engine" is software that analyzes the emotions from users' responses and obtains emotional data.

[1962] The "generative AI for notation" is an artificial intelligence that receives user responses and emotional data analyzed by the emotion engine, and records and organizes the content of the discussion.

[1963] "Solution topics" are specific topics and steps for solving problems that are generated by the moderator generation AI by analyzing the user's issues.

[1964] A "question" is a specific question that the question generation AI generates based on the solution topic for the user to answer.

[1965] "Emotions" are psychological states such as stress, anxiety, or joy that a user expresses when answering questions.

[1966] The "solution" is the final problem-solving method that the moderator's generative AI derives based on the user's answers and emotions.

[1967] MODE FOR CARRYING OUT THE INVENTION

[1968] This invention is a system in which a user inputs a problem through a terminal, a server receives the problem, and various generative AIs and emotion engines work together to support problem solving. This system is characterized by its ability to adjust appropriate questions and discussion progress in real time, taking into account the user's emotional state. Detailed modes for implementing the invention are described below.

[1969] Hardware and software used

[1970] 1. Server

[1971] The server oversees various generative AIs (hosting AI, question generation AI, and secretary generation AI) and the emotion engine, and manages the user interface. The server communicates with devices using HTTP requests and WebSockets.

[1972] 2. Terminal

[1973] A terminal is a hardware device where users input tasks and answer questions. Terminals can be PCs, smartphones, tablets, etc. The terminal provides a user interface and displays information from the server.

[1974] 3. Generative AI and Emotion Engines

[1975] Generative AI for moderators: Analyzes the issues entered by users and generates solutions. Examples of AI models used include GPT-4.

[1976] Question generation AI: Generates specific questions based on the topics generated by the moderator generation AI.

[1977] Generative AI for transcription: Records user responses and emotional data, and organizes the discussion content.

[1978] Emotion engine: Software that analyzes emotions from user responses. An example is IBM Watson Tone Analyzer.

[1979] System processing flow

[1980] The specific flow of the system processing proceeds as follows:

[1981] 1. Initial Setup

[1982] The server initializes the system and starts each generative AI (for moderator, questioner, and secretary) and emotion engine.

[1983] 2. Enter your assignment

[1984] The user inputs the problem to be solved in natural language through the terminal. For example, they might input "How to consider a marketing strategy for new product development."

[1985] 3. Receiving assignments

[1986] The device sends the user's input data to the server, which receives the data and passes it to the moderator generation AI.

[1987] 4. Issue analysis and topic generation

[1988] The moderator's AI analyzes the problem and generates relevant solution topics, such as "current situation analysis" and "target demographic selection."

[1989] 5. Question Generation

[1990] The question generator AI creates questions based on the topics generated by the moderator AI, for example, "What are the strengths and weaknesses of your current marketing strategy?"

[1991] 6. Posing and answering questions

[1992] The server sends the generated question to the user's device. The device displays the question to the user, and the user inputs an answer. For example, the user might answer, "Our current strength is online advertising, and our weakness is low brand recognition." The device then sends the answer to the server.

[1993] 7. Emotion Analysis

[1994] The server passes the user's response to an emotion engine, which analyzes the user's emotions, for example, recognizing that the user is feeling anxious.

[1995] 8. Recording and Organizing

[1996] A generative AI transcription system records and organizes users' responses and analyzed emotions, thereby saving the discussion history and emotional data.

[1997] 9. Moderation of discussions

[1998] Based on the emotional data analyzed by the emotion engine, the moderator generative AI adjusts the progress of the discussion. For example, if the user is feeling stressed, it will lower the difficulty of the questions.

[1999] 10. Next Question Generation

[2000] The question generator AI generates new questions based on the adjusted instructions, such as, "What type of demographic do you envision as the target audience for your new product?"

[2001] 11. Posting and answering follow-up questions

[2002] The server sends the newly generated question to the user's device. The user then enters an answer to the question, for example, "We are targeting a demographic with a high affinity with young people." The device then sends this answer to the server.

[2003] 12. Re-analyzing and recording emotions

[2004] The server passes the new response to the emotion engine, and the transcription generation AI re-records the discussion along with the analyzed emotions.

[2005] 13. Producing the final result

[2006] The moderator's generative AI completes the discussion for all topics and derives a final solution while taking into account the sentiment data. For example, by integrating user responses and sentiment, it might suggest a solution such as "strengthen online advertising for new products and run a campaign targeted at younger generations."

[2007] 14. Presentation of Results

[2008] The transcription AI summarizes all the discussions and documents the final solution, and the server sends the final result to the user's device, where the user can review it.

[2009] Specific examples

[2010] For example, if a user inputs the problem "How to consider a marketing strategy for new product development" into the system, the following process will be carried out.

[2011] The user enters the assignment through the termin...

Claims

1. A means for a user to input a task through a terminal; a means for the server to receive a challenge input by a user; A means for the moderator's generation AI to analyze the issues and generate solution topics, A means for the question generation AI to generate questions based on the solution topic; means for a user to answer questions generated through a terminal; A means for the transcription AI to receive user responses and record and organize the discussion content; A generative AI moderator will lead the discussion based on the user's answers and derive a solution. a means for presenting the final solution to a user's terminal; A system including:

2. 2. The system of claim 1, wherein the scribe generation AI includes means for summarizing the content of the discussion and organizing a final solution.

3. 2. The system according to claim 1, wherein the server includes means for initializing the moderator AI, the question AI, and the clerk AI.

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