system

Abstract

The system according to the embodiment aims to enable people aiming to start a business to efficiently learn startup know-how and receive appropriate advice. [Solution] A system according to an embodiment includes a learning unit, a receiving unit, an analysis unit, and a providing unit. The learning unit learns startup know-how. The receiving unit receives questions from users. The analysis unit analyzes the questions received by the receiving unit. The providing unit provides answers based on the questions analyzed by the analysis unit.

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] With conventional technology, it was difficult for people aiming to start a business to efficiently learn startup know-how and obtain appropriate advice.

[0005] The system according to the embodiment aims to enable people aiming to start a business to efficiently learn startup know-how and receive appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a receiving unit, an analysis unit, and a providing unit. The learning unit learns startup know-how. The receiving unit receives questions from users. The analysis unit analyzes the questions received by the receiving unit. The providing unit provides answers based on the questions analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows people aiming to start a business to efficiently learn start-up know-how and receive appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mentoring system according to an embodiment of the present invention is a personalized mentoring service for people aspiring to start a business. This mentoring system learns startup know-how, accepts questions from users, and uses a generation AI to analyze the questions and provide professional answers. For example, the mentoring system learns about startup success stories and failures, how to build a business model, and how to raise funds. Next, the user inputs a question through the UI of a messaging app. The generation AI analyzes the question and provides a professional answer. For example, in response to the question, "How do I create a business plan?", the generation AI provides a detailed answer including the basic structure of the business plan, important points to note, and success stories. In response to the question, "What are the tips for fundraising?", the generation AI provides specific advice including fundraising methods, success stories, and important points to note. This allows people aspiring to start a business to easily receive advice from mentors with specialized knowledge and experience. This allows people aspiring to start a business to easily receive advice from mentors with specialized knowledge and experience. For example, the mentoring system allows people aspiring to start a business to easily obtain various knowledge necessary for starting a business, such as creating a business plan, fundraising methods, and developing a marketing strategy. It is also easy to use, as users can simply enter their question in the familiar UI of the messaging app and receive a professional answer from the generative AI.

[0029] The mentoring system according to the embodiment includes a learning unit, a receiving unit, an analysis unit, and a providing unit. The learning unit learns startup know-how. Examples of startup know-how include, but are not limited to, success stories, failure stories, methods for building business models, and methods for fundraising. For example, the learning unit learns from startup success stories and analyzes the factors behind their success. Furthermore, the learning unit can also learn from failure stories and identify the causes of failure. Furthermore, the learning unit can learn methods for building business models and propose effective business models. Furthermore, the learning unit can learn methods for fundraising and provide optimal fundraising strategies. The receiving unit receives questions from users. Examples of questions from users include, but are not limited to, methods for creating business plans and tips for fundraising. For example, the receiving unit receives questions entered by users on the UI of a messaging app. Furthermore, the receiving unit can also receive questions using voice input or image input. The analysis unit uses a generation AI to analyze the questions received by the receiving unit. The analysis unit analyzes the question using, for example, natural language processing technology. The analysis unit can also analyze the question using a machine learning algorithm. Furthermore, the analysis unit can understand the intent of the question and generate an appropriate answer using a generation AI. The provision unit provides an answer based on the question analyzed by the analysis unit. The provision unit can provide a professional answer based on, for example, the results of the analysis by the generation AI. The provision unit can also provide specific advice, such as how to create a business plan or tips for fundraising. Furthermore, the provision unit can provide advice from mentors with specialized knowledge and experience in response to user questions. This allows the mentoring system according to the embodiment to enable entrepreneurs to easily receive advice from mentors with specialized knowledge and experience. For example, they can acquire various knowledge necessary for starting a business, such as how to create a business plan, how to raise funds, and how to develop a marketing strategy. Furthermore, the system is easy to use because users can receive professional answers from the generation AI simply by entering their questions in the familiar UI of a messaging app.

[0030] The learning unit can learn about successful and unsuccessful startup cases, how to build business models, and how to raise funds. For example, the learning unit can learn about successful startup cases and analyze the factors behind their success. The learning unit can also learn about unsuccessful cases and identify the causes of failure. The learning unit can also learn how to build business models and propose effective business models. Furthermore, the learning unit can learn how to raise funds and provide optimal fundraising strategies. This allows the system to have extensive knowledge about startups. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generative AI. For example, the learning unit can input successful and unsuccessful startup cases into the generative AI, which can then learn them.

[0031] The reception unit can receive questions input by the user on the UI of the messaging app. For example, the reception unit receives questions input by the user on the UI of the messaging app. The reception unit can also receive questions using voice input or image input. For example, when the user inputs a question by voice, the reception unit can receive the question using voice recognition technology. When the user inputs a question using an image, the reception unit can also receive the question using image analysis technology. This allows the user to input questions using a UI that is familiar to the user. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input a question input by the user on the UI of the messaging app to AI, and the AI ​​can receive the question.

[0032] The analysis unit can analyze the user's question using the generation AI. The analysis unit can analyze the question using, for example, natural language processing technology. The analysis unit can also analyze the question using a machine learning algorithm. Furthermore, the analysis unit can use the generation AI to understand the intent of the question and generate an appropriate answer. For example, the analysis unit inputs the user's question into the generation AI, which then analyzes the question. In this way, the use of the generation AI improves the accuracy of question analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's question into the generation AI, which then analyzes the question.

[0033] The providing unit can provide a professional answer based on the analysis results of the generation AI. The providing unit can provide a professional answer based on, for example, the analysis results of the generation AI. The providing unit can also provide specific advice, such as how to create a business plan or tips for fundraising. Furthermore, the providing unit can provide advice from mentors with specialized knowledge and experience in response to user questions. For example, the providing unit can provide a detailed answer including the basic structure of a business plan, points to note, success stories, etc., based on the analysis results of the generation AI. The providing unit can also provide specific advice, such as how to fundraise, success stories, and points to note, based on the analysis results of the generation AI. In this way, professional answers can be provided by using the generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the analysis results of the generation AI into AI, which can then provide a professional answer.

[0034] The learning unit can learn success stories and failure stories specific to each industry in addition to startup know-how. For example, the learning unit can learn success stories and failure stories specific to startups in the IT industry. The learning unit can also learn success stories and failure stories specific to startups in the healthcare industry. The learning unit can also learn success stories and failure stories specific to startups in the fintech industry. This allows for more specialized knowledge to be provided by learning success stories and failure stories specific to each industry. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit can input success stories and failure stories specific to each industry into the generation AI, which can then learn them.

[0035] The learning unit can incorporate the latest startup trends and technology trends during learning. For example, the learning unit learns startup trends that utilize the latest AI technology. The learning unit can also learn startup trends that utilize blockchain technology. The learning unit can also learn the latest startup trends related to sustainability. This allows the latest startup trends and technology trends to be incorporated, making it possible to always provide the latest information. Some or all of the above-mentioned processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the latest startup trends and technology trends into the generation AI, which can then learn them.

[0036] The learning unit can add information about regional business environments and legal regulations to the learning content. For example, the learning unit learns information about the business environment and legal regulations in the United States. The learning unit can also learn information about the business environment and legal regulations in Europe. The learning unit can also learn information about the business environment and legal regulations in Asia. By adding information about regional business environments and legal regulations, appropriate advice tailored to each region can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit can input information about regional business environments and legal regulations into the generation AI, which then learns the information.

[0037] The learning unit can customize the learning content by reflecting the user's past question history. For example, the learning unit prioritizes learning related information based on the content of questions asked by the user in the past. The learning unit can also learn information on a specific topic in depth from the user's past question history. The learning unit can also learn the latest information on fields in which the user has previously shown interest. This makes it possible to provide more personalized information by reflecting the user's past question history. Some or all of the above-mentioned processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past question history into the generation AI, which can then customize the learning content.

[0038] The learning unit can add information specialized to the user's business field to the learning content. For example, if the user is interested in the IT industry, the learning unit can learn information specialized to the IT industry. Furthermore, if the user is interested in the healthcare industry, the learning unit can learn information specialized to the healthcare industry. Furthermore, if the user is interested in the fintech industry, the learning unit can learn information specialized to the fintech industry. By adding information specialized to the user's business field, more specialized advice can be provided. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input information specialized to the user's business field into the generation AI, which can then learn the information.

[0039] The learning unit can continuously update the learning content by reflecting user feedback. The learning unit updates the learning content based on, for example, feedback provided by the user. Furthermore, if specific information is missing from the user feedback, the learning unit can add that information and learn. Furthermore, the learning unit can reflect user feedback and improve the accuracy of the learning content. In this way, the accuracy of the learning content is improved by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input user feedback into the generation AI, which can update the learning content.

[0040] When accepting a question, the acceptance unit can select the optimal acceptance method by referring to the user's past question history. For example, the acceptance unit can preferentially suggest question formats that the user has frequently used in the past. The acceptance unit can also preferentially accept questions on specific topics from the user's past question history. The acceptance unit can also suggest the optimal question acceptance method based on the user's past question history. In this way, the optimal question acceptance method can be selected by referring to the user's past question history. Some or all of the above-mentioned processing in the acceptance unit may be performed using AI, or may be performed without using AI. For example, the acceptance unit can input the user's past question history into AI, which can select the optimal question acceptance method.

[0041] When receiving questions, the reception unit can filter them based on the user's current business phase. For example, if the user is in the early stages of a startup, the reception unit can prioritize basic questions. Furthermore, if the user is in the growth stage, the reception unit can prioritize questions about fundraising and marketing. Furthermore, if the user is in the mature stage, the reception unit can prioritize questions about business expansion and international development. This makes it possible to receive questions according to the user's business phase. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's current business phase into AI, which can then filter the questions.

[0042] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question in text, the acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the acceptance unit can also accept the question using image analysis technology. This makes it possible to accept questions optimally depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, or may be performed without using AI. For example, the acceptance unit can input the user's input method into AI, which can select the optimal acceptance means.

[0043] When accepting questions, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize accepting questions related to that region. Furthermore, if the user is conducting international business, the reception unit can prioritize accepting questions related to international expansion. Furthermore, if the user is in a specific city, the reception unit can prioritize accepting questions related to the business environment of that city. This makes it possible to accept questions based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can prioritize accepting questions that are highly relevant.

[0044] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can analyze content posted by the user on social media and prioritize receiving related questions. The reception unit can also suggest questions that the user is likely to be interested in based on the user's social media activity. The reception unit can also suggest related questions based on the activity of the user's friends on social media. This makes it possible to receive questions based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which can then receive related questions.

[0045] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit can suggest an optimal question reception method based on feedback provided by the user in the past. The reception unit can also preferentially accept questions on specific topics based on the user's past feedback. The reception unit can also reflect the user's past feedback to improve the accuracy of the question reception method. This makes it possible to accept questions based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI, which can then customize the reception method.

[0046] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question history. For example, the analysis unit prioritizes analysis of related information based on the content of questions asked by the user in the past. The analysis unit can also perform in-depth analysis of information on a specific topic from the user's past question history. The analysis unit can also analyze the latest information on fields in which the user has previously shown interest. This enables analysis based on the user's past question history. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past question history into the generation AI, which can then improve the accuracy of the analysis.

[0047] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply a business model analysis algorithm to a question about a business plan. The analysis unit can also apply a finance analysis algorithm to a question about fundraising. The analysis unit can also apply a marketing analysis algorithm to a question about marketing. This enables optimal analysis depending on the category of the question. Some or all of the above-mentioned processing in the analysis unit may be performed using or without using the generation AI. For example, the analysis unit can input the category of the question into the generation AI, and the generation AI can apply different analysis algorithms.

[0048] When analyzing a question, the analysis unit can use an analysis method specialized for the user's business field. For example, if the user asks a question about the IT industry, the analysis unit can use an analysis method specialized for the IT industry. Furthermore, if the user asks a question about the healthcare industry, the analysis unit can use an analysis method specialized for the healthcare industry. Furthermore, if the user asks a question about the fintech industry, the analysis unit can use an analysis method specialized for the fintech industry. This enables analysis specialized for the user's business field. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information specialized for the user's business field into the generation AI, and the generation AI can analyze it.

[0049] When analyzing a question, the analysis unit can determine the priority of analysis based on the time of submission by the user. For example, if a user submits an urgent question, the analysis unit causes the generation AI to prioritize analysis. Furthermore, if a user submits questions periodically, the analysis unit can determine the priority of analysis based on the time of submission. Furthermore, if a user submits questions during a specific time period, the analysis unit can determine the priority of analysis based on the time period. This enables analysis based on the time of submission by the user. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of submission by the user to the generation AI, and the generation AI can determine the priority of analysis.

[0050] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature. In the analysis unit, for example, the generation AI performs the analysis based on related literature provided by the user. The analysis unit can also automatically search for literature related to the user's question and reflect it in the analysis. The analysis unit can also improve the accuracy of the analysis based on literature previously referenced by the user. This makes it possible to perform analysis based on the user's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's related literature into the generation AI, and the generation AI can refer to it to improve the accuracy of the analysis.

[0051] When analyzing a question, the analysis unit can take the user's market value into consideration. For example, if the user has a high market value, the analysis unit can have the generation AI perform a detailed analysis and provide expert advice. Furthermore, if the user is in an emerging market, the analysis unit can have the generation AI perform an analysis taking into account market characteristics. Furthermore, if the user is in a mature market, the analysis unit can have the generation AI perform an analysis taking into account the competitive situation in the market. This makes it possible to perform an analysis based on the user's market value. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's market value into the generation AI, and the generation AI can perform an analysis taking that into consideration.

[0052] When providing an answer, the providing unit can provide the optimal answer by referring to the user's past question history. For example, the providing unit can prioritize providing related information based on the content of questions asked by the user in the past. The providing unit can also provide in-depth information on a specific topic from the user's past question history. The providing unit can also provide the latest information on areas in which the user has shown interest in the past. This makes it possible to provide the optimal answer based on the user's past question history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past question history into AI, which can then provide the optimal answer.

[0053] When providing an answer, the providing unit can adjust the level of detail of the answer depending on the user's business phase. For example, if the user is in the early stages of a startup, the providing unit can provide detailed basic information. Furthermore, if the user is in the growth stage, the providing unit can provide detailed information on fundraising and marketing. Furthermore, if the user is in the mature stage, the providing unit can provide detailed information on business expansion and international development. This enables detailed answers to be provided depending on the user's business phase. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's business phase into AI, which can adjust the level of detail of the answer.

[0054] The providing unit can improve the accuracy of the answer by reflecting user feedback when providing an answer. The providing unit can improve the accuracy of the answer, for example, based on feedback provided by the user. Furthermore, if specific information is missing from the user feedback, the providing unit can add that information and provide the answer. Furthermore, the providing unit can reflect user feedback and continuously update the content of the answer. This enables highly accurate answers based on user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user feedback into AI, which can improve the accuracy of the answer.

[0055] When providing an answer, the providing unit can provide the optimal answer by taking into account the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide information related to that region. Furthermore, if the user is conducting international business, the providing unit can also provide information about international expansion. Furthermore, if the user is in a specific city, the providing unit can also provide information about the business environment of that city. This makes it possible to provide the optimal answer based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can then provide the optimal answer.

[0056] When providing an answer, the providing unit can analyze the user's social media activity and provide a relevant answer. For example, the providing unit can analyze content posted by the user on social media and provide related information. The providing unit can also provide information that is likely to be of interest to the user based on the user's social media activity. The providing unit can also provide related information by referring to the activity of the user's friends on social media. This makes it possible to provide an optimal answer based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, which can then provide a relevant answer.

[0057] When providing an answer, the providing unit can adjust the use of technical terminology in the answer depending on the user's level of expertise. For example, if the user is a beginner, the providing unit can provide an answer in simple language, avoiding technical terminology. Furthermore, if the user is an intermediate user, the providing unit can provide an answer using appropriate technical terminology. Furthermore, if the user is an advanced user, the providing unit can provide a detailed answer using a lot of technical terminology. This enables the provision of an optimal answer depending on the user's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into AI, which can adjust the use of technical terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The mentoring system can also refer to the history of a user's past mentoring sessions and automatically suggest topics to cover in the next session. For example, if the previous session discussed creating a business plan, the next session could check on progress. Also, if a user repeatedly asks questions about a particular issue, the system can provide new information or resources related to that issue. Furthermore, new topics can be suggested based on topics the user has shown interest in in the past. This allows for personalized mentoring tailored to the user's needs.

[0060] The mentoring system can also customize the content of mentoring according to the user's business phase. For example, if the user is in the early stages of a startup, it can provide them with information on creating a basic business plan and conducting market research. If the user is in the growth stage, it can provide them with information on fundraising and marketing strategies. Furthermore, if the user is in the mature stage, it can provide them with advice on business expansion and international development. This makes it possible to provide the optimal mentoring according to the user's business phase.

[0061] The mentoring system can also provide advice on region-specific business information and legal regulations by taking into account the user's geographic location. For example, if the user is in the United States, information on the American business environment and legal regulations can be provided. If the user is in Europe, information on the European business environment and legal regulations can be provided. Furthermore, if the user is in Asia, information on the Asian business environment and legal regulations can be provided. This makes it possible to provide appropriate advice tailored to the business environment and legal regulations of each region.

[0062] The mentoring system can also analyze the user's social media activity and provide relevant mentoring content. For example, it can provide relevant business information and advice based on the content the user posts on social media. It can also suggest topics that the user might be interested in based on their social media activity. It can also provide relevant information based on the activity of the user's friends on social media. This makes it possible to provide optimal mentoring based on the user's social media activity.

[0063] The mentoring system can also match users with specialized mentors who specialize in their business field. For example, if a user is interested in the IT industry, a mentor specializing in the IT industry can be provided. If a user is interested in the healthcare industry, a mentor specializing in the healthcare industry can be provided. Furthermore, if a user is interested in the fintech industry, a mentor specializing in the fintech industry can be provided. This makes it possible to provide the optimal mentor according to the user's business field.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The learning department learns startup know-how. Startup know-how includes success stories and failure stories, how to build business models, and how to raise funds. The learning department studies success stories and analyzes the factors behind success. They can also study failure stories and identify the causes of failure. Furthermore, they learn how to build business models and how to raise funds, and are able to propose effective business models and optimal fundraising strategies. Step 2: The reception unit accepts questions from users. Questions from users include how to create a business plan and tips for fundraising. The reception unit accepts questions entered by the user on the messaging app's UI. Questions can also be accepted using voice input or image input. Step 3: The analysis unit uses the generation AI to analyze the question received by the reception unit. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the question, understand the intent of the question, and generate an appropriate answer. Step 4: The provision unit provides answers based on the questions analyzed by the analysis unit. The provision unit provides professional answers based on the results of the analysis by the generation AI. It can also provide specific advice, such as how to create a business plan or tips on fundraising. It can also provide advice from mentors with specialized knowledge and experience.

[0066] (Example 2) A mentoring system according to an embodiment of the present invention is a personalized mentoring service for people aspiring to start a business. This mentoring system learns startup know-how, accepts questions from users, and uses a generation AI to analyze the questions and provide professional answers. For example, the mentoring system learns about startup success stories and failures, how to build a business model, and how to raise funds. Next, the user inputs a question through the UI of a messaging app. The generation AI analyzes the question and provides a professional answer. For example, in response to the question, "How do I create a business plan?", the generation AI provides a detailed answer including the basic structure of the business plan, important points to note, and success stories. In response to the question, "What are the tips for fundraising?", the generation AI provides specific advice including fundraising methods, success stories, and important points to note. This allows people aspiring to start a business to easily receive advice from mentors with specialized knowledge and experience. This allows people aspiring to start a business to easily receive advice from mentors with specialized knowledge and experience. For example, the mentoring system allows people aspiring to start a business to easily obtain various knowledge necessary for starting a business, such as creating a business plan, fundraising methods, and developing a marketing strategy. It is also easy to use, as users can simply enter their question in the familiar UI of the messaging app and receive a professional answer from the generative AI.

[0067] The mentoring system according to the embodiment includes a learning unit, a receiving unit, an analysis unit, and a providing unit. The learning unit learns startup know-how. Examples of startup know-how include, but are not limited to, success stories, failure stories, methods for building business models, and methods for fundraising. For example, the learning unit learns from startup success stories and analyzes the factors behind their success. Furthermore, the learning unit can also learn from failure stories and identify the causes of failure. Furthermore, the learning unit can learn methods for building business models and propose effective business models. Furthermore, the learning unit can learn methods for fundraising and provide optimal fundraising strategies. The receiving unit receives questions from users. Examples of questions from users include, but are not limited to, methods for creating business plans and tips for fundraising. For example, the receiving unit receives questions entered by users on the UI of a messaging app. Furthermore, the receiving unit can also receive questions using voice input or image input. The analysis unit uses a generation AI to analyze the questions received by the receiving unit. The analysis unit analyzes the question using, for example, natural language processing technology. The analysis unit can also analyze the question using a machine learning algorithm. Furthermore, the analysis unit can understand the intent of the question and generate an appropriate answer using a generation AI. The provision unit provides an answer based on the question analyzed by the analysis unit. The provision unit can provide a professional answer based on, for example, the results of the analysis by the generation AI. The provision unit can also provide specific advice, such as how to create a business plan or tips for fundraising. Furthermore, the provision unit can provide advice from mentors with specialized knowledge and experience in response to user questions. This allows the mentoring system according to the embodiment to enable entrepreneurs to easily receive advice from mentors with specialized knowledge and experience. For example, they can acquire various knowledge necessary for starting a business, such as how to create a business plan, how to raise funds, and how to develop a marketing strategy. Furthermore, the system is easy to use because users can receive professional answers from the generation AI simply by entering their questions in the familiar UI of a messaging app.

[0068] The learning unit can learn about successful and unsuccessful startup cases, how to build business models, and how to raise funds. For example, the learning unit can learn about successful startup cases and analyze the factors behind their success. The learning unit can also learn about unsuccessful cases and identify the causes of failure. The learning unit can also learn how to build business models and propose effective business models. Furthermore, the learning unit can learn how to raise funds and provide optimal fundraising strategies. This allows the system to have extensive knowledge about startups. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generative AI. For example, the learning unit can input successful and unsuccessful startup cases into the generative AI, which can then learn them.

[0069] The reception unit can receive questions input by the user on the UI of the messaging app. For example, the reception unit receives questions input by the user on the UI of the messaging app. The reception unit can also receive questions using voice input or image input. For example, when the user inputs a question by voice, the reception unit can receive the question using voice recognition technology. When the user inputs a question using an image, the reception unit can also receive the question using image analysis technology. This allows the user to input questions using a UI that is familiar to the user. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input a question input by the user on the UI of the messaging app to AI, and the AI ​​can receive the question.

[0070] The analysis unit can analyze the user's question using the generation AI. The analysis unit can analyze the question using, for example, natural language processing technology. The analysis unit can also analyze the question using a machine learning algorithm. Furthermore, the analysis unit can use the generation AI to understand the intent of the question and generate an appropriate answer. For example, the analysis unit inputs the user's question into the generation AI, which then analyzes the question. In this way, the use of the generation AI improves the accuracy of question analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's question into the generation AI, which then analyzes the question.

[0071] The providing unit can provide a professional answer based on the analysis results of the generation AI. The providing unit can provide a professional answer based on, for example, the analysis results of the generation AI. The providing unit can also provide specific advice, such as how to create a business plan or tips for fundraising. Furthermore, the providing unit can provide advice from mentors with specialized knowledge and experience in response to user questions. For example, the providing unit can provide a detailed answer including the basic structure of a business plan, points to note, success stories, etc., based on the analysis results of the generation AI. The providing unit can also provide specific advice, such as how to fundraise, success stories, and points to note, based on the analysis results of the generation AI. In this way, professional answers can be provided by using the generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the analysis results of the generation AI into AI, which can then provide a professional answer.

[0072] The learning unit can estimate the user's emotions and adjust the priority of learning content based on the estimated user emotions. For example, if the user is feeling anxious, the learning unit can cause the generation AI to prioritize learning success stories and provide positive information. Alternatively, if the user is feeling confident, the learning unit can cause the generation AI to prioritize learning failure stories and emphasize the importance of risk management. Alternatively, if the user is feeling anxious, the learning unit can cause the generation AI to prioritize learning methods for building business models with immediate results. This allows for more appropriate information to be provided by adjusting the priority of learning content according to the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit can be performed using the generation AI, or without the generation AI. For example, the learning unit can input the user's emotion data into the generation AI, which can then adjust the priority of learning content.

[0073] The learning unit can learn success stories and failure stories specific to each industry in addition to startup know-how. For example, the learning unit can learn success stories and failure stories specific to startups in the IT industry. The learning unit can also learn success stories and failure stories specific to startups in the healthcare industry. The learning unit can also learn success stories and failure stories specific to startups in the fintech industry. This allows for more specialized knowledge to be provided by learning success stories and failure stories specific to each industry. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit can input success stories and failure stories specific to each industry into the generation AI, which can then learn them.

[0074] The learning unit can incorporate the latest startup trends and technology trends during learning. For example, the learning unit learns startup trends that utilize the latest AI technology. The learning unit can also learn startup trends that utilize blockchain technology. The learning unit can also learn the latest startup trends related to sustainability. This allows the latest startup trends and technology trends to be incorporated, making it possible to always provide the latest information. Some or all of the above-mentioned processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the latest startup trends and technology trends into the generation AI, which can then learn them.

[0075] The learning unit can add information about regional business environments and legal regulations to the learning content. For example, the learning unit learns information about the business environment and legal regulations in the United States. The learning unit can also learn information about the business environment and legal regulations in Europe. The learning unit can also learn information about the business environment and legal regulations in Asia. By adding information about regional business environments and legal regulations, appropriate advice tailored to each region can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit can input information about regional business environments and legal regulations into the generation AI, which then learns the information.

[0076] The learning unit can estimate the user's emotions and adjust the depth of the learning content based on the estimated user emotions. For example, if the user feels anxious, the learning unit allows the generation AI to learn basic information in depth and provide detailed explanations. Furthermore, if the user feels confident, the learning unit can also allow the generation AI to learn advanced information and provide expert advice. Furthermore, if the user feels anxious, the learning unit can also allow the generation AI to learn information that can be understood in a short time and provide concise explanations. This allows for more appropriate information to be provided by adjusting the depth of the learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's emotion data into the generation AI, which then adjusts the depth of the learning content.

[0077] The learning unit can customize the learning content by reflecting the user's past question history. For example, the learning unit prioritizes learning related information based on the content of questions asked by the user in the past. The learning unit can also learn information on a specific topic in depth from the user's past question history. The learning unit can also learn the latest information on fields in which the user has previously shown interest. This makes it possible to provide more personalized information by reflecting the user's past question history. Some or all of the above-mentioned processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past question history into the generation AI, which can then customize the learning content.

[0078] The learning unit can add information specialized to the user's business field to the learning content. For example, if the user is interested in the IT industry, the learning unit can learn information specialized to the IT industry. Furthermore, if the user is interested in the healthcare industry, the learning unit can learn information specialized to the healthcare industry. Furthermore, if the user is interested in the fintech industry, the learning unit can learn information specialized to the fintech industry. By adding information specialized to the user's business field, more specialized advice can be provided. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input information specialized to the user's business field into the generation AI, which can then learn the information.

[0079] The learning unit can continuously update the learning content by reflecting user feedback. The learning unit updates the learning content based on, for example, feedback provided by the user. Furthermore, if specific information is missing from the user feedback, the learning unit can add that information and learn. Furthermore, the learning unit can reflect user feedback and improve the accuracy of the learning content. In this way, the accuracy of the learning content is improved by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input user feedback into the generation AI, which can update the learning content.

[0080] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple interface and minimize the steps for entering a question. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable question reception method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept questions. This allows for more appropriate question reception by adjusting the question reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then adjust the question reception method.

[0081] When accepting a question, the acceptance unit can select the optimal acceptance method by referring to the user's past question history. For example, the acceptance unit can preferentially suggest question formats that the user has frequently used in the past. The acceptance unit can also preferentially accept questions on specific topics from the user's past question history. The acceptance unit can also suggest the optimal question acceptance method based on the user's past question history. In this way, the optimal question acceptance method can be selected by referring to the user's past question history. Some or all of the above-mentioned processing in the acceptance unit may be performed using AI, or may be performed without using AI. For example, the acceptance unit can input the user's past question history into AI, which can select the optimal question acceptance method.

[0082] When receiving questions, the reception unit can filter them based on the user's current business phase. For example, if the user is in the early stages of a startup, the reception unit can prioritize basic questions. Furthermore, if the user is in the growth stage, the reception unit can prioritize questions about fundraising and marketing. Furthermore, if the user is in the mature stage, the reception unit can prioritize questions about business expansion and international development. This makes it possible to receive questions according to the user's business phase. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's current business phase into AI, which can then filter the questions.

[0083] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question in text, the acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the acceptance unit can also accept the question using image analysis technology. This makes it possible to accept questions optimally depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, or may be performed without using AI. For example, the acceptance unit can input the user's input method into AI, which can select the optimal acceptance means.

[0084] The reception unit can estimate the user's emotions and determine the priority of questions based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize urgent questions. Furthermore, if the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, if the user is feeling impatient, the reception unit can prioritize questions that require a quick answer. This allows for more appropriate question reception by determining the priority of questions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then determine the priority of questions.

[0085] When accepting questions, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize accepting questions related to that region. Furthermore, if the user is conducting international business, the reception unit can prioritize accepting questions related to international expansion. Furthermore, if the user is in a specific city, the reception unit can prioritize accepting questions related to the business environment of that city. This makes it possible to accept questions based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can prioritize accepting questions that are highly relevant.

[0086] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can analyze content posted by the user on social media and prioritize receiving related questions. The reception unit can also suggest questions that the user is likely to be interested in based on the user's social media activity. The reception unit can also suggest related questions based on the activity of the user's friends on social media. This makes it possible to receive questions based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which can then receive related questions.

[0087] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit can suggest an optimal question reception method based on feedback provided by the user in the past. The reception unit can also preferentially accept questions on specific topics based on the user's past feedback. The reception unit can also reflect the user's past feedback to improve the accuracy of the question reception method. This makes it possible to accept questions based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI, which can then customize the reception method.

[0088] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. For example, if the user feels anxious, the analysis unit allows the generation AI to perform a detailed analysis and provide a reassuring answer. Furthermore, if the user feels confident, the analysis unit allows the generation AI to perform an advanced analysis and provide expert advice. Furthermore, if the user feels anxious, the analysis unit allows the generation AI to perform a quick analysis and provide a concise answer. This enables more appropriate analysis by adjusting the question analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input user emotion data into the generation AI, which then adjusts the question analysis method.

[0089] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question history. For example, the analysis unit prioritizes analysis of related information based on the content of questions asked by the user in the past. The analysis unit can also perform in-depth analysis of information on a specific topic from the user's past question history. The analysis unit can also analyze the latest information on fields in which the user has previously shown interest. This enables analysis based on the user's past question history. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past question history into the generation AI, which can then improve the accuracy of the analysis.

[0090] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply a business model analysis algorithm to a question about a business plan. The analysis unit can also apply a finance analysis algorithm to a question about fundraising. The analysis unit can also apply a marketing analysis algorithm to a question about marketing. This enables optimal analysis depending on the category of the question. Some or all of the above-mentioned processing in the analysis unit may be performed using or without using the generation AI. For example, the analysis unit can input the category of the question into the generation AI, and the generation AI can apply different analysis algorithms.

[0091] When analyzing a question, the analysis unit can use an analysis method specialized for the user's business field. For example, if the user asks a question about the IT industry, the analysis unit can use an analysis method specialized for the IT industry. Furthermore, if the user asks a question about the healthcare industry, the analysis unit can use an analysis method specialized for the healthcare industry. Furthermore, if the user asks a question about the fintech industry, the analysis unit can use an analysis method specialized for the fintech industry. This enables analysis specialized for the user's business field. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information specialized for the user's business field into the generation AI, and the generation AI can analyze it.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user feels anxious, the analysis unit can have the generation AI provide detailed analysis results to give a sense of security. Furthermore, if the user feels confident, the analysis unit can have the generation AI provide advanced analysis results and offer expert advice. Furthermore, if the user feels anxious, the analysis unit can have the generation AI provide concise analysis results and provide a quick response. This enables the display of analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the display method of the analysis results.

[0093] When analyzing a question, the analysis unit can determine the priority of analysis based on the time of submission by the user. For example, if a user submits an urgent question, the analysis unit causes the generation AI to prioritize analysis. Furthermore, if a user submits questions periodically, the analysis unit can determine the priority of analysis based on the time of submission. Furthermore, if a user submits questions during a specific time period, the analysis unit can determine the priority of analysis based on the time period. This enables analysis based on the time of submission by the user. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of submission by the user to the generation AI, and the generation AI can determine the priority of analysis.

[0094] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature. In the analysis unit, for example, the generation AI performs the analysis based on related literature provided by the user. The analysis unit can also automatically search for literature related to the user's question and reflect it in the analysis. The analysis unit can also improve the accuracy of the analysis based on literature previously referenced by the user. This makes it possible to perform analysis based on the user's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's related literature into the generation AI, and the generation AI can refer to it to improve the accuracy of the analysis.

[0095] When analyzing a question, the analysis unit can take the user's market value into consideration. For example, if the user has a high market value, the analysis unit can have the generation AI perform a detailed analysis and provide expert advice. Furthermore, if the user is in an emerging market, the analysis unit can have the generation AI perform an analysis taking into account market characteristics. Furthermore, if the user is in a mature market, the analysis unit can have the generation AI perform an analysis taking into account the competitive situation in the market. This makes it possible to perform an analysis based on the user's market value. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's market value into the generation AI, and the generation AI can perform an analysis taking that into consideration.

[0096] The providing unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can have the generation AI provide an answer in an expression that gives a sense of security. Furthermore, if the user is feeling confident, the providing unit can have the generation AI provide an answer in a professional expression. Furthermore, if the user is feeling impatient, the providing unit can have the generation AI provide an answer in a concise and quick expression. This enables the answer to be expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user emotion data into the generation AI, which can then adjust the way the answer is expressed.

[0097] When providing an answer, the providing unit can provide the optimal answer by referring to the user's past question history. For example, the providing unit can prioritize providing related information based on the content of questions asked by the user in the past. The providing unit can also provide in-depth information on a specific topic from the user's past question history. The providing unit can also provide the latest information on areas in which the user has shown interest in the past. This makes it possible to provide the optimal answer based on the user's past question history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past question history into AI, which can then provide the optimal answer.

[0098] When providing an answer, the providing unit can adjust the level of detail of the answer depending on the user's business phase. For example, if the user is in the early stages of a startup, the providing unit can provide detailed basic information. Furthermore, if the user is in the growth stage, the providing unit can provide detailed information on fundraising and marketing. Furthermore, if the user is in the mature stage, the providing unit can provide detailed information on business expansion and international development. This enables detailed answers to be provided depending on the user's business phase. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's business phase into AI, which can adjust the level of detail of the answer.

[0099] The providing unit can improve the accuracy of the answer by reflecting user feedback when providing an answer. The providing unit can improve the accuracy of the answer, for example, based on feedback provided by the user. Furthermore, if specific information is missing from the user feedback, the providing unit can add that information and provide the answer. Furthermore, the providing unit can reflect user feedback and continuously update the content of the answer. This enables highly accurate answers based on user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user feedback into AI, which can improve the accuracy of the answer.

[0100] The providing unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, if the user feels anxious, the providing unit can have the generation AI provide a detailed answer, giving a sense of security. Furthermore, if the user feels confident, the providing unit can have the generation AI provide a concise answer and offer expert advice. Furthermore, if the user feels impatient, the providing unit can have the generation AI provide a short, to-the-point answer. This allows the length of the answer to be tailored to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using AI, or can be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the length of the answer.

[0101] When providing an answer, the providing unit can provide the optimal answer by taking into account the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide information related to that region. Furthermore, if the user is conducting international business, the providing unit can also provide information about international expansion. Furthermore, if the user is in a specific city, the providing unit can also provide information about the business environment of that city. This makes it possible to provide the optimal answer based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can then provide the optimal answer.

[0102] When providing an answer, the providing unit can analyze the user's social media activity and provide a relevant answer. For example, the providing unit can analyze content posted by the user on social media and provide related information. The providing unit can also provide information that is likely to be of interest to the user based on the user's social media activity. The providing unit can also provide related information by referring to the activity of the user's friends on social media. This makes it possible to provide an optimal answer based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, which can then provide a relevant answer.

[0103] When providing an answer, the providing unit can adjust the use of technical terminology in the answer depending on the user's level of expertise. For example, if the user is a beginner, the providing unit can provide an answer in simple language, avoiding technical terminology. Furthermore, if the user is an intermediate user, the providing unit can provide an answer using appropriate technical terminology. Furthermore, if the user is an advanced user, the providing unit can provide a detailed answer using a lot of technical terminology. This enables the provision of an optimal answer depending on the user's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into AI, which can adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the learning unit, receiving unit, analysis unit, and providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns startup know-how. The receiving unit is realized by the control unit 46A of the smart device 14 and receives questions from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The providing unit is realized by the control unit 46A of the smart device 14 and provides professional answers based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the learning unit, reception unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns startup know-how. The reception unit is realized by the control unit 46A of the smart glasses 214 and receives questions from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides professional answers based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the learning unit, receiving unit, analysis unit, and providing unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns startup know-how. The receiving unit is realized by the control unit 46A of the headset type terminal 314 and receives questions from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The providing unit is realized by the control unit 46A of the headset type terminal 314 and provides professional answers based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the learning unit, reception unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns startup know-how. The reception unit is realized by the control unit 46A of the robot 414 and receives questions from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The provision unit is realized by the control unit 46A of the robot 414 and provides professional answers based on the analysis results.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The mentoring system can also estimate the user's emotions and adjust the timing of mentoring based on the estimated emotions. For example, if the user is feeling stressed, the system can provide mentoring at a time when the user is able to relax. If the user is highly motivated, the system can also provide mentoring immediately. Furthermore, if the user is tired, the system can send a message encouraging the user to take a rest and resume mentoring later. This allows mentoring to be provided at the optimal time according to the user's emotions.

[0106] The mentoring system can also refer to the history of a user's past mentoring sessions and automatically suggest topics to cover in the next session. For example, if the previous session discussed creating a business plan, the next session could check on progress. Also, if a user repeatedly asks questions about a particular issue, the system can provide new information or resources related to that issue. Furthermore, new topics can be suggested based on topics the user has shown interest in in the past. This allows for personalized mentoring tailored to the user's needs.

[0107] The mentoring system can also estimate the user's emotions and adjust the content of the mentoring based on the estimated emotions. For example, if the user is feeling anxious, the system can prioritize positive success stories. If the user is feeling confident, the system can also provide information on risk management. Furthermore, if the user is feeling anxious, the system can provide immediate advice. This makes it possible to provide optimal mentoring content according to the user's emotions.

[0108] The mentoring system can also customize the content of mentoring according to the user's business phase. For example, if the user is in the early stages of a startup, it can provide them with information on creating a basic business plan and conducting market research. If the user is in the growth stage, it can provide them with information on fundraising and marketing strategies. Furthermore, if the user is in the mature stage, it can provide them with advice on business expansion and international development. This makes it possible to provide the optimal mentoring according to the user's business phase.

[0109] The mentoring system can further estimate the user's emotions and adjust the mentoring feedback method based on the estimated emotions. For example, if the user feels anxious, the system can prioritize providing positive feedback. If the user feels confident, the system can also provide constructive criticism. Furthermore, if the user feels impatient, the system can provide quick and concise feedback. This allows the system to provide optimal feedback according to the user's emotions.

[0110] The mentoring system can also provide advice on region-specific business information and legal regulations by taking into account the user's geographic location. For example, if the user is in the United States, information on the American business environment and legal regulations can be provided. If the user is in Europe, information on the European business environment and legal regulations can be provided. Furthermore, if the user is in Asia, information on the Asian business environment and legal regulations can be provided. This makes it possible to provide appropriate advice tailored to the business environment and legal regulations of each region.

[0111] The mentoring system can further estimate the user's emotions and adjust the mentoring progress speed based on the estimated emotions. For example, if the user feels anxious, the system can proceed with the mentoring at a slower pace. On the other hand, if the user feels confident, the system can proceed with the mentoring at a faster pace. Furthermore, if the user feels impatient, the system can provide mentoring that focuses on important points in a short amount of time. This makes it possible to provide mentoring at an optimal progress speed according to the user's emotions.

[0112] The mentoring system can also analyze the user's social media activity and provide relevant mentoring content. For example, it can provide relevant business information and advice based on the content the user posts on social media. It can also suggest topics that the user might be interested in based on their social media activity. It can also provide relevant information based on the activity of the user's friends on social media. This makes it possible to provide optimal mentoring based on the user's social media activity.

[0113] The mentoring system can further estimate the user's emotions and adjust the format of the mentoring based on the estimated emotions. For example, if the user feels anxious, the system can provide mentoring in a visually intensive format. If the user feels confident, the system can provide mentoring in a text-based format. Furthermore, if the user feels impatient, the system can provide mentoring in a concise and to-the-point format. This allows mentoring to be provided in the optimal format depending on the user's emotions.

[0114] The mentoring system can also match users with specialized mentors who specialize in their business field. For example, if a user is interested in the IT industry, a mentor specializing in the IT industry can be provided. If a user is interested in the healthcare industry, a mentor specializing in the healthcare industry can be provided. Furthermore, if a user is interested in the fintech industry, a mentor specializing in the fintech industry can be provided. This makes it possible to provide the optimal mentor according to the user's business field.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The learning department learns startup know-how. Startup know-how includes success stories and failure stories, how to build business models, and how to raise funds. The learning department studies success stories and analyzes the factors behind success. They can also study failure stories and identify the causes of failure. Furthermore, they learn how to build business models and how to raise funds, and are able to propose effective business models and optimal fundraising strategies. Step 2: The reception unit accepts questions from users. Questions from users include how to create a business plan and tips for fundraising. The reception unit accepts questions entered by the user on the messaging app's UI. Questions can also be accepted using voice input or image input. Step 3: The analysis unit uses the generation AI to analyze the question received by the reception unit. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the question, understand the intent of the question, and generate an appropriate answer. Step 4: The provision unit provides answers based on the questions analyzed by the analysis unit. The provision unit provides professional answers based on the results of the analysis by the generation AI. It can also provide specific advice, such as how to create a business plan or tips on fundraising. It can also provide advice from mentors with specialized knowledge and experience.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning department where students learn startup know-how, a reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; a providing unit that provides an answer based on the question analyzed by the analyzing unit; Equipped with A system characterized by:

2. The learning unit Learn about successful and unsuccessful startup cases, how to build a business model, and how to raise funds.

2. The system of claim 1.

3. The reception unit Accept questions entered by the user on the messaging app UI 2. The system of claim 1.

4. The analysis unit Generative AI analyzes user questions 2. The system of claim 1.

5. The providing unit Generative AI provides professional answers based on the results of its analysis.

2. The system of claim 1.

6. The learning unit Inferring user emotions and adjusting the priorities of learning content based on the estimated user emotions 2. The system of claim 1.

7. The learning unit In addition to startup know-how, learn about success stories and failure cases specific to each industry.

2. The system of claim 1.

8. The learning unit Incorporate the latest startup and technology trends into your learning 2. The system of claim 1.

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