system

Abstract

An object of the system according to the embodiment is to enable beginners to quickly obtain specialized information and support.SOLUTION: In one embodiment, a system comprises a chat client, a generated AI, a knowledge-graph, an advertisement displayer, a privacy protector, and a marketing strategizer. The chat client receives a user's question. The generation AI analyzes the question received by the chat client and extracts relevant information from the knowledge-graph. The advertisement display unit displays an advertisement based on the question analyzed by the generation AI. The privacy protector protects the user's data. The marketing strategy unit prompts the expert to participate through the SNS.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for new entrepreneurs to quickly obtain specialized information and support, and knowledge sharing has been insufficient.

[0005] The system according to the embodiment aims to enable beginner entrepreneurs to quickly obtain specialized information and support. [Means for solving the problem]

[0006] The system according to the embodiment includes a chat client, a generation AI, a knowledge graph, an advertisement display unit, a privacy protection unit, and a marketing strategy unit. The chat client accepts questions from users. The generation AI analyzes the questions accepted by the chat client and extracts related information from the knowledge graph. The advertisement display unit displays advertisements based on the questions analyzed by the generation AI. The privacy protection unit protects user data. The marketing strategy unit encourages the participation of experts through social networking sites. [Effects of the Invention]

[0007] The system according to the embodiment allows beginner entrepreneurs to quickly obtain specialized information and support. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The support system according to the embodiment of the present invention is a system that provides innovative support to beginner entrepreneurs and promotes business success through the provision of professional information and support. As a result, the support system can provide comprehensive support to beginner entrepreneurs and promote business success.

[0029] The assistance system according to the embodiment includes a chat client, a generation AI, a knowledge graph, an advertisement display unit, a privacy protection unit, and a marketing strategy unit. The chat client accepts user questions. For example, the user can input the question in text format. The chat client also has a voice input function, allowing the user to input the question by voice. The generation AI analyzes the question accepted by the chat client. For example, the generation AI uses natural language processing technology to understand the intent of the question and generate an appropriate answer. The generation AI can also analyze the content of the question and provide related information using a machine learning algorithm. The knowledge graph extracts related information based on the question analyzed by the generation AI. For example, the knowledge graph can search for related information from a database and provide it to the generation AI. The knowledge graph can also obtain information from an external API and provide it to the generation AI. The advertisement display unit displays advertisements based on the question analyzed by the generation AI. For example, if a user asks about how to create a business plan, advertisements for related books and seminars are displayed. The advertisement display unit can also analyze the user's past behavioral history and display the most relevant advertisements. The privacy protection unit protects user data. For example, the privacy protection unit protects user personal information using encryption technology. The privacy protection unit can also perform access control to prevent unauthorized access by third parties. The marketing strategy unit encourages the participation of experts through social media. For example, the marketing strategy unit invites experts via social media to provide users with reliable information. The marketing strategy unit can also collect user feedback during the free service period and use it to improve the service. As a result, the support system according to the embodiment can provide comprehensive support to novice entrepreneurs and promote business success. For example, users can instantly obtain expert information through a chat client that utilizes knowledge graph technology. Furthermore, a free service based on an advertising model allows users to receive high-quality support at no cost.You can use the service with peace of mind in an environment where privacy and security are ensured.

[0030] In response to a user's question, the generative AI can refer to past success stories and failure stories from the knowledge graph and provide specific advice. For example, in response to a user's question, the generative AI can extract past success stories and failure stories from the knowledge graph and provide specific advice. For example, if asked about how to create a business plan, the generative AI will present examples of successful business plans and improvements to unsuccessful plans. The generative AI can also refer to past project data and industry best practices to provide specific advice to the user. This allows the AI ​​to provide specific advice to users and support their business success.

[0031] In response to user questions, generative AI can provide relevant industry news and trend information from the knowledge graph in real time. For example, in response to a user question, generative AI can extract and provide relevant industry news and trend information from the knowledge graph in real time. For example, when asked about a new marketing method, generative AI can generate an answer based on the latest industry news and trend information. Generative AI can also refer to news feeds and real-time data to provide users with the latest information. This can support business success by providing users with the latest industry news and trend information.

[0032] The chat client has a voice input function, allowing the user to input questions by voice, and the generation AI to provide answers by voice. The chat client, for example, has a voice input function, allowing the user to input questions by voice. For example, the user may ask by voice, "Please tell me how to create a business plan," and the generation AI will provide answers by voice. The chat client can also use voice recognition technology to convert the user's voice into text and send it to the generation AI. This allows questions and answers to be asked by voice, improving user convenience.

[0033] The chat client is equipped with visual elements, and the generation AI can provide answers using diagrams and videos. The chat client, for example, is equipped with visual elements, and the generation AI can provide answers using diagrams and videos. For example, if a question is asked about how to create a business plan, specific steps can be explained using diagrams and videos. The chat client can also provide users with visually easy-to-understand information using infographics and animations. This allows users to deepen their understanding by providing answers using diagrams and videos.

[0034] In response to a user's question, the generative AI can automatically collect and provide relevant expert opinions and comments from a knowledge graph. For example, in response to a user's question, the generative AI can automatically collect and provide relevant expert opinions and comments from a knowledge graph. For example, if a question is asked about marketing strategy, the generative AI can provide specific advice based on the expert opinions and comments. The generative AI can also collect expert opinions and comments using an expert database or API integration and provide them to the user. This allows the provision of expert opinions and comments to provide highly reliable information to the user.

[0035] The generation AI can refer to the contents of past discussions and forums from the knowledge graph to generate the optimal answer to a user's question. For example, when a user asks a question, the generation AI can refer to the contents of past discussions and forums from the knowledge graph to generate the optimal answer. For example, when a question is asked about how to create a business plan, the generation AI can provide specific steps based on the contents of past discussions and forums. The generation AI can also refer to the forum database and past threads to provide the optimal answer to the user. This makes it possible to provide the optimal answer to the user by referring to the contents of past discussions and forums.

[0036] Knowledge sharing platforms are equipped with a real-time chat function, allowing users to communicate directly with each other. For example, a user can ask a question in real time, and other users can immediately respond. Knowledge sharing platforms can also implement the real-time chat function using WebSocket or a real-time messaging protocol. This allows users to communicate directly with each other, promoting knowledge sharing.

[0037] The knowledge sharing platform is equipped with a blog function, allowing users to post their own experiences and knowledge. The knowledge sharing platform is equipped with, for example, a blog function, allowing users to post their own experiences and knowledge. For example, users may share their own business experiences and success stories in blog format. The knowledge sharing platform is also equipped with a posting editor and comment function, allowing users to leave comments on other users' posts. This allows users to post their own experiences and knowledge, thereby promoting knowledge sharing.

[0038] The advertisement display unit can analyze the user's past behavior history and display the most relevant advertisement. The advertisement display unit can, for example, analyze the user's past behavior history and display the most relevant advertisement. For example, the advertisement is displayed based on keywords the user has previously searched for or pages the user has viewed. The advertisement display unit can also refer to click data and website browsing history to display the most relevant advertisement to the user. In this way, by analyzing the user's past behavior history, the most relevant advertisement can be displayed, improving the user experience.

[0039] The advertisement display unit can display advertisements related to the user's current question in real time. For example, if a user asks about how to create a business plan, advertisements for related books and seminars are displayed. The advertisement display unit can also analyze the user's question using keyword matching and context analysis and display related advertisements. This can improve the user experience by displaying advertisements related to the user's current question in real time.

[0040] The advertisement display unit may introduce a reward system, allowing users to earn points by clicking on advertisements. The advertisement display unit may, for example, introduce a reward system, allowing users to earn points by clicking on advertisements. For example, points may be added each time a user clicks on an advertisement, and a special benefit may be obtained when a certain number of points are accumulated. The advertisement display unit may also design a point system and types of rewards, and provide the reward system to users. This allows users to earn points by clicking on advertisements, thereby improving user engagement.

[0041] The advertisement display unit adds a feedback function, allowing users to rate advertisements. The advertisement display unit, for example, adds a feedback function, allowing users to rate advertisements. For example, users can rate advertisements, thereby improving the quality of advertisements. The advertisement display unit can also design an evaluation form or a comment function to provide users with a feedback function. This allows users to rate advertisements, thereby improving the quality of advertisements.

[0042] The privacy protection unit can store user data in distributed storage to enhance security. The privacy protection unit can, for example, store user data in distributed storage to enhance security. For example, data can be distributed and stored across multiple servers to improve security. The privacy protection unit can also store user data using blockchain technology or a distributed file system. As a result, security can be enhanced by storing user data in distributed storage.

[0043] The privacy protection unit can conduct regular security audits of user data to detect and fix vulnerabilities early. The privacy protection unit can, for example, conduct regular security audits of user data to detect and fix vulnerabilities early. For example, it can conduct regular security checks to detect and fix vulnerabilities. The privacy protection unit can also conduct security audits using vulnerability scans and penetration tests. By conducting regular security audits, vulnerabilities in user data can be detected and fixed early, thereby strengthening security.

[0044] The privacy protection unit can provide a deletion function that allows a user to completely delete their own data. The privacy protection unit provides, for example, a deletion function that allows a user to completely delete their own data. For example, a user sends a request to delete their own data, and the data is completely deleted. The privacy protection unit can also completely delete data and delete backups. This makes it possible to strengthen privacy protection by providing a function that allows a user to completely delete their own data.

[0045] The privacy protection unit can introduce two-factor authentication to strengthen security and protect user accounts. The privacy protection unit can, for example, introduce two-factor authentication to strengthen security and protect user accounts. For example, when a user logs in, two-factor authentication is performed using SMS or an authentication app in addition to a password. The privacy protection unit can also design two-factor authentication technology and implementation methods to provide users with enhanced security. As a result, by introducing two-factor authentication, user accounts can be protected and security can be strengthened.

[0046] The marketing strategy department can periodically hold events where experts share their knowledge through live streaming via social media. The marketing strategy department, for example, periodically holds events where experts share their knowledge through live streaming via social media. For example, experts give live-streamed lectures on how to create a business plan or marketing strategies. The marketing strategy department can also implement live streaming using streaming protocols and distribution platforms. This allows for the provision of highly reliable information to users by periodically holding events where experts share their knowledge through live streaming.

[0047] The marketing strategy department can collect user feedback during the period when the service is provided free of charge and use it to improve the service. The marketing strategy department, for example, collects user feedback during the period when the service is provided free of charge and uses it to improve the service. For example, the marketing strategy department collects feedback on users' impressions of using the service and areas for improvement as feedback. The marketing strategy department can also collect feedback using questionnaire surveys and user interviews and use it to improve the service. In this way, by collecting user feedback and using it to improve the service, it is possible to improve the user experience.

[0048] The marketing strategy department can introduce a referral program as an initial marketing strategy, in which users can receive rewards by inviting their friends. For example, the marketing strategy department introduces a referral program as an initial marketing strategy, in which users can receive rewards by inviting their friends. For example, each time a user invites a friend, points are added and rewards are obtained. The marketing strategy department can also generate invitation links and design types of rewards to provide the referral program to users. By introducing a referral program in which users can receive rewards by inviting their friends, user engagement can be improved.

[0049] The marketing strategy department can introduce a loyalty program as an initial marketing strategy, whereby users can earn points by using the service. For example, the marketing strategy department introduces a loyalty program as an initial marketing strategy, whereby users can earn points by using the service. For example, points are added each time a user uses the service, and benefits are obtained. The marketing strategy department can also design the criteria for awarding points and the types of rewards, and provide the loyalty program to users. In this way, by introducing a loyalty program where users can earn points by using the service, it is possible to improve user engagement.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The assistance system can provide relevant patent information in response to user questions. For example, if a user asks about a new business idea, it can provide relevant patent information and provide information on existing patents and technologies for which patents are pending. The assistance system can also refer to a patent database to provide the user with advice on how to obtain a patent and the procedures for filing a patent application. This allows the user to obtain information to determine whether their business idea is patentable.

[0052] The assistance system can provide relevant legal and regulatory information in response to user questions. For example, if a user asks about how to sell a new product, the assistance system can provide relevant legal and regulatory information and provide information about the permits and licenses required to sell the product. The assistance system can also refer to a legal and regulatory database to provide the user with advice on how to comply with laws and regulations and the procedures required. This allows the user to obtain information to confirm whether their business is in compliance with laws and regulations.

[0053] The assistance system can provide relevant market research data in response to a user's question. For example, if a user asks about the marketability of a new product, the assistance system can provide relevant market research data and information about the product's market size and competitive situation. The assistance system can also refer to a market research database and provide the user with advice on how to conduct market research and analyze the data. This allows the user to obtain information to determine how their product will be received in the market.

[0054] The support system can provide relevant financial information in response to a user's question. For example, if a user asks about how to raise funds for a new business, the support system can provide relevant financial information and provide information on fundraising methods and investors. The support system can also refer to a financial database and provide the user with advice on how to create a financial plan and how to manage funds. This allows the user to understand the financial situation of their business and obtain information to select an appropriate fundraising method.

[0055] The assistance system can provide relevant technical information in response to a user's questions. For example, if a user asks about how to develop new technology, the assistance system will provide relevant technical information, such as information on technology development methods and the latest technological trends. The assistance system can also refer to a technology database to provide the user with advice on the technology development process and necessary resources. This allows the user to obtain information to make their technology development project a success.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The chat client accepts a question from the user. For example, the user can input the question in text format. The chat client also has a voice input function, allowing the user to input the question by voice. Step 2: The generation AI analyzes the question received by the chat client. For example, the generation AI uses natural language processing technology to understand the intent of the question and generate an appropriate answer. The generation AI can also use machine learning algorithms to analyze the content of the question and provide related information. Step 3: The knowledge graph extracts relevant information based on the question analyzed by the generation AI. For example, the knowledge graph can search for relevant information from a database and provide it to the generation AI. The knowledge graph can also obtain information from an external API and provide it to the generation AI. Step 4: The ad display unit displays ads based on the questions analyzed by the generation AI. For example, if a user asks about how to create a business plan, ads for related books and seminars will be displayed. The ad display unit can also analyze the user's past behavioral history to display the most relevant ads. Step 5: The privacy protection unit protects the user's data. For example, the privacy protection unit protects the user's personal information using encryption technology. The privacy protection unit can also perform access control to prevent unauthorized access by third parties. Step 6: The Marketing Strategy Department encourages the participation of experts through social media. For example, the Marketing Strategy Department can invite experts via social media to provide users with reliable information. The Marketing Strategy Department can also collect user feedback during the free service period and use it to improve the service.

[0058] (Example 2) The support system according to the embodiment of the present invention is a system that provides innovative support to beginner entrepreneurs and promotes business success through the provision of professional information and support. As a result, the support system can provide comprehensive support to beginner entrepreneurs and promote business success.

[0059] The assistance system according to the embodiment includes a chat client, a generation AI, a knowledge graph, an advertisement display unit, a privacy protection unit, and a marketing strategy unit. The chat client accepts user questions. For example, the user can input the question in text format. The chat client also has a voice input function, allowing the user to input the question by voice. The generation AI analyzes the question accepted by the chat client. For example, the generation AI uses natural language processing technology to understand the intent of the question and generate an appropriate answer. The generation AI can also analyze the content of the question and provide related information using a machine learning algorithm. The knowledge graph extracts related information based on the question analyzed by the generation AI. For example, the knowledge graph can search for related information from a database and provide it to the generation AI. The knowledge graph can also obtain information from an external API and provide it to the generation AI. The advertisement display unit displays advertisements based on the question analyzed by the generation AI. For example, if a user asks about how to create a business plan, advertisements for related books and seminars are displayed. The advertisement display unit can also analyze the user's past behavioral history and display the most relevant advertisements. The privacy protection unit protects user data. For example, the privacy protection unit protects user personal information using encryption technology. The privacy protection unit can also perform access control to prevent unauthorized access by third parties. The marketing strategy unit encourages the participation of experts through social media. For example, the marketing strategy unit invites experts via social media to provide users with reliable information. The marketing strategy unit can also collect user feedback during the free service period and use it to improve the service. As a result, the support system according to the embodiment can provide comprehensive support to novice entrepreneurs and promote business success. For example, users can instantly obtain expert information through a chat client that utilizes knowledge graph technology. Furthermore, a free service based on an advertising model allows users to receive high-quality support at no cost.You can use the service with peace of mind in an environment where privacy and security are ensured.

[0060] In response to a user's question, the generative AI can refer to past success stories and failure stories from the knowledge graph and provide specific advice. For example, in response to a user's question, the generative AI can extract past success stories and failure stories from the knowledge graph and provide specific advice. For example, if asked about how to create a business plan, the generative AI will present examples of successful business plans and improvements to unsuccessful plans. The generative AI can also refer to past project data and industry best practices to provide specific advice to the user. This allows the AI ​​to provide specific advice to users and support their business success.

[0061] In response to user questions, generative AI can provide relevant industry news and trend information from the knowledge graph in real time. For example, in response to a user question, generative AI can extract and provide relevant industry news and trend information from the knowledge graph in real time. For example, when asked about a new marketing method, generative AI can generate an answer based on the latest industry news and trend information. Generative AI can also refer to news feeds and real-time data to provide users with the latest information. This can support business success by providing users with the latest industry news and trend information.

[0062] Generative AI can analyze the user's emotions when asking a question and generate an answer that elicits positive emotions. For example, if the user is feeling anxious, generative AI can elicit positive emotions by presenting encouraging words or success stories. Generative AI can also analyze the user's emotions using an emotion analysis algorithm and generate an appropriate answer. This can improve the user experience by analyzing the user's emotions and eliciting positive emotions.

[0063] The chat client has a voice input function, allowing the user to input questions by voice, and the generation AI to provide answers by voice. The chat client, for example, has a voice input function, allowing the user to input questions by voice. For example, the user may ask by voice, "Please tell me how to create a business plan," and the generation AI will provide answers by voice. The chat client can also use voice recognition technology to convert the user's voice into text and send it to the generation AI. This allows questions and answers to be asked by voice, improving user convenience.

[0064] The chat client is equipped with visual elements, and the generation AI can provide answers using diagrams and videos. The chat client, for example, is equipped with visual elements, and the generation AI can provide answers using diagrams and videos. For example, if a question is asked about how to create a business plan, specific steps can be explained using diagrams and videos. The chat client can also provide users with visually easy-to-understand information using infographics and animations. This allows users to deepen their understanding by providing answers using diagrams and videos.

[0065] The chat client can use the emotion estimation function to customize the interface according to the user's emotions. For example, the chat client can use the emotion estimation function to customize the interface according to the user's emotions. For example, if the user is feeling stressed, the color or design of the interface can be changed to enhance the relaxation effect. The chat client can also analyze the user's emotions using an emotion analysis algorithm and customize the interface. This allows the user experience to be improved by customizing the interface according to the user's emotions.

[0066] In response to a user's question, the generative AI can automatically collect and provide relevant expert opinions and comments from a knowledge graph. For example, in response to a user's question, the generative AI can automatically collect and provide relevant expert opinions and comments from a knowledge graph. For example, if a question is asked about marketing strategy, the generative AI can provide specific advice based on the expert opinions and comments. The generative AI can also collect expert opinions and comments using an expert database or API integration and provide them to the user. This allows the provision of expert opinions and comments to provide highly reliable information to the user.

[0067] The generation AI can refer to the contents of past discussions and forums from the knowledge graph to generate the optimal answer to a user's question. For example, when a user asks a question, the generation AI can refer to the contents of past discussions and forums from the knowledge graph to generate the optimal answer. For example, when a question is asked about how to create a business plan, the generation AI can provide specific steps based on the contents of past discussions and forums. The generation AI can also refer to the forum database and past threads to provide the optimal answer to the user. This makes it possible to provide the optimal answer to the user by referring to the contents of past discussions and forums.

[0068] The generative AI can use its emotion estimation function to provide customized advice based on the user's emotions. For example, if the user is feeling anxious, the generative AI can elicit positive emotions by presenting encouraging words or success stories. The generative AI can also use an emotion analysis algorithm to analyze the user's emotions and provide appropriate advice. This can improve the user experience by providing customized advice based on the user's emotions.

[0069] Knowledge sharing platforms are equipped with a real-time chat function, allowing users to communicate directly with each other. For example, a user can ask a question in real time, and other users can immediately respond. Knowledge sharing platforms can also implement the real-time chat function using WebSocket or a real-time messaging protocol. This allows users to communicate directly with each other, promoting knowledge sharing.

[0070] The knowledge sharing platform is equipped with a blog function, allowing users to post their own experiences and knowledge. The knowledge sharing platform is equipped with, for example, a blog function, allowing users to post their own experiences and knowledge. For example, users may share their own business experiences and success stories in blog format. The knowledge sharing platform is also equipped with a posting editor and comment function, allowing users to leave comments on other users' posts. This allows users to post their own experiences and knowledge, thereby promoting knowledge sharing.

[0071] The generative AI can use its emotion estimation function to suggest knowledge sharing methods that correspond to the user's emotions. For example, if the user is feeling anxious, the generative AI can suggest knowledge sharing methods that will help the user relax. The generative AI can also use an emotion analysis algorithm to analyze the user's emotions and suggest appropriate knowledge sharing methods. This can improve the user experience by suggesting knowledge sharing methods that correspond to the user's emotions.

[0072] The advertisement display unit can analyze the user's past behavior history and display the most relevant advertisement. The advertisement display unit can, for example, analyze the user's past behavior history and display the most relevant advertisement. For example, the advertisement is displayed based on keywords the user has previously searched for or pages the user has viewed. The advertisement display unit can also refer to click data and website browsing history to display the most relevant advertisement to the user. In this way, by analyzing the user's past behavior history, the most relevant advertisement can be displayed, improving the user experience.

[0073] The advertisement display unit can display advertisements related to the user's current question in real time. For example, if a user asks about how to create a business plan, advertisements for related books and seminars are displayed. The advertisement display unit can also analyze the user's question using keyword matching and context analysis and display related advertisements. This can improve the user experience by displaying advertisements related to the user's current question in real time.

[0074] The advertisement display unit can use the emotion estimation function to display advertisements that correspond to the user's emotions and elicit a positive response. The advertisement display unit, for example, uses the emotion estimation function to display advertisements that correspond to the user's emotions. For example, if the user has positive emotions, it displays related advertisements to elicit a positive response. The advertisement display unit can also analyze the user's emotions using an emotion analysis algorithm and display appropriate advertisements. In this way, by displaying advertisements that correspond to the user's emotions, it is possible to elicit a positive response and improve the user experience.

[0075] The advertisement display unit may introduce a reward system, allowing users to earn points by clicking on advertisements. The advertisement display unit may, for example, introduce a reward system, allowing users to earn points by clicking on advertisements. For example, points may be added each time a user clicks on an advertisement, and a special benefit may be obtained when a certain number of points are accumulated. The advertisement display unit may also design a point system and types of rewards, and provide the reward system to users. This allows users to earn points by clicking on advertisements, thereby improving user engagement.

[0076] The advertisement display unit adds a feedback function, allowing users to rate advertisements. The advertisement display unit, for example, adds a feedback function, allowing users to rate advertisements. For example, users can rate advertisements, thereby improving the quality of advertisements. The advertisement display unit can also design an evaluation form or a comment function to provide users with a feedback function. This allows users to rate advertisements, thereby improving the quality of advertisements.

[0077] The advertisement display unit can adjust the timing of advertisement display based on the user's emotions using the emotion estimation function. The advertisement display unit, for example, adjusts the timing of advertisement display based on the user's emotions using the emotion estimation function. For example, if the user has positive emotions, the advertisement display unit adjusts the timing of advertisement display. The advertisement display unit can also analyze the user's emotions using an emotion analysis algorithm and adjust the timing of advertisement display. This makes it possible to improve the user experience by adjusting the timing of advertisement display based on the user's emotions.

[0078] The privacy protection unit can store user data in distributed storage to enhance security. The privacy protection unit can, for example, store user data in distributed storage to enhance security. For example, data can be distributed and stored across multiple servers to improve security. The privacy protection unit can also store user data using blockchain technology or a distributed file system. As a result, security can be enhanced by storing user data in distributed storage.

[0079] The privacy protection unit can conduct regular security audits of user data to detect and fix vulnerabilities early. The privacy protection unit can, for example, conduct regular security audits of user data to detect and fix vulnerabilities early. For example, it can conduct regular security checks to detect and fix vulnerabilities. The privacy protection unit can also conduct security audits using vulnerability scans and penetration tests. By conducting regular security audits, vulnerabilities in user data can be detected and fixed early, thereby strengthening security.

[0080] The privacy protection unit can provide a security alert according to the user's emotions using the emotion estimation function. The privacy protection unit, for example, can provide a security alert according to the user's emotions using the emotion estimation function. For example, if the user feels anxious, the privacy protection unit can provide a security alert to give the user a sense of security. The privacy protection unit can also analyze the user's emotions using an emotion analysis algorithm and provide an appropriate security alert. This can increase the user's sense of security by providing a security alert according to the user's emotions.

[0081] The privacy protection unit can provide a deletion function that allows a user to completely delete their own data. The privacy protection unit provides, for example, a deletion function that allows a user to completely delete their own data. For example, a user sends a request to delete their own data, and the data is completely deleted. The privacy protection unit can also completely delete data and delete backups. This makes it possible to strengthen privacy protection by providing a function that allows a user to completely delete their own data.

[0082] The privacy protection unit can introduce two-factor authentication to strengthen security and protect user accounts. The privacy protection unit can, for example, introduce two-factor authentication to strengthen security and protect user accounts. For example, when a user logs in, two-factor authentication is performed using SMS or an authentication app in addition to a password. The privacy protection unit can also design two-factor authentication technology and implementation methods to provide users with enhanced security. As a result, by introducing two-factor authentication, user accounts can be protected and security can be strengthened.

[0083] The privacy protection unit can use the emotion estimation function to suggest privacy settings according to the user's emotions. The privacy protection unit, for example, uses the emotion estimation function to suggest privacy settings according to the user's emotions. For example, if the user feels anxious, the privacy protection unit suggests strengthening the privacy settings. The privacy protection unit can also analyze the user's emotions using an emotion analysis algorithm and suggest appropriate privacy settings. This makes it possible to increase the user's sense of security by suggesting privacy settings according to the user's emotions.

[0084] The marketing strategy department can periodically hold events where experts share their knowledge through live streaming via social media. The marketing strategy department, for example, periodically holds events where experts share their knowledge through live streaming via social media. For example, experts give live-streamed lectures on how to create a business plan or marketing strategies. The marketing strategy department can also implement live streaming using streaming protocols and distribution platforms. This allows for the provision of highly reliable information to users by periodically holding events where experts share their knowledge through live streaming.

[0085] The marketing strategy department can collect user feedback during the period when the service is provided free of charge and use it to improve the service. The marketing strategy department, for example, collects user feedback during the period when the service is provided free of charge and uses it to improve the service. For example, the marketing strategy department collects feedback on users' impressions of using the service and areas for improvement as feedback. The marketing strategy department can also collect feedback using questionnaire surveys and user interviews and use it to improve the service. In this way, by collecting user feedback and using it to improve the service, it is possible to improve the user experience.

[0086] The marketing strategy unit can use the emotion estimation function to create a marketing message based on the user's emotions. For example, the marketing strategy unit uses the emotion estimation function to create a marketing message based on the user's emotions. For example, if the user has positive emotions, a positive message is created. The marketing strategy unit can also analyze the user's emotions using an emotion analysis algorithm to create an appropriate marketing message. In this way, creating a marketing message based on the user's emotions can attract the user's attention and increase marketing effectiveness.

[0087] The marketing strategy department can introduce a referral program as an initial marketing strategy, in which users can receive rewards by inviting their friends. For example, the marketing strategy department introduces a referral program as an initial marketing strategy, in which users can receive rewards by inviting their friends. For example, each time a user invites a friend, points are added and rewards are obtained. The marketing strategy department can also generate invitation links and design types of rewards to provide the referral program to users. By introducing a referral program in which users can receive rewards by inviting their friends, user engagement can be improved.

[0088] The marketing strategy department can introduce a loyalty program as an initial marketing strategy, whereby users can earn points by using the service. For example, the marketing strategy department introduces a loyalty program as an initial marketing strategy, whereby users can earn points by using the service. For example, points are added each time a user uses the service, and benefits are obtained. The marketing strategy department can also design the criteria for awarding points and the types of rewards, and provide the loyalty program to users. In this way, by introducing a loyalty program where users can earn points by using the service, it is possible to improve user engagement.

[0089] The marketing strategy unit can use the emotion estimation function to implement a marketing campaign according to the user's emotions. The marketing strategy unit, for example, uses the emotion estimation function to implement a marketing campaign according to the user's emotions. For example, if the user has positive emotions, a positive campaign is implemented. The marketing strategy unit can also analyze the user's emotions using an emotion analysis algorithm and implement an appropriate marketing campaign. In this way, by implementing a marketing campaign according to the user's emotions, it is possible to attract the user's attention and increase the marketing effectiveness.

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

[0091] The assistance system can provide relevant patent information in response to user questions. For example, if a user asks about a new business idea, it can provide relevant patent information and provide information on existing patents and technologies for which patents are pending. The assistance system can also refer to a patent database to provide the user with advice on how to obtain a patent and the procedures for filing a patent application. This allows the user to obtain information to determine whether their business idea is patentable.

[0092] The assistance system can provide relevant legal and regulatory information in response to user questions. For example, if a user asks about how to sell a new product, the assistance system can provide relevant legal and regulatory information and provide information about the permits and licenses required to sell the product. The assistance system can also refer to a legal and regulatory database to provide the user with advice on how to comply with laws and regulations and the procedures required. This allows the user to obtain information to confirm whether their business is in compliance with laws and regulations.

[0093] The assistance system can provide relevant market research data in response to a user's question. For example, if a user asks about the marketability of a new product, the assistance system can provide relevant market research data and information about the product's market size and competitive situation. The assistance system can also refer to a market research database and provide the user with advice on how to conduct market research and analyze the data. This allows the user to obtain information to determine how their product will be received in the market.

[0094] The support system can provide relevant financial information in response to a user's question. For example, if a user asks about how to raise funds for a new business, the support system can provide relevant financial information and provide information on fundraising methods and investors. The support system can also refer to a financial database and provide the user with advice on how to create a financial plan and how to manage funds. This allows the user to understand the financial situation of their business and obtain information to select an appropriate fundraising method.

[0095] The assistance system can provide relevant technical information in response to a user's questions. For example, if a user asks about how to develop new technology, the assistance system will provide relevant technical information, such as information on technology development methods and the latest technological trends. The assistance system can also refer to a technology database to provide the user with advice on the technology development process and necessary resources. This allows the user to obtain information to make their technology development project a success.

[0096] The support system can estimate the user's emotions and provide appropriate resources to the user based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxation resources or stress management methods. The support system can also analyze the user's emotions using an emotion estimation algorithm and provide appropriate resources. This can reduce the user's stress by providing resources according to the user's emotions, thereby supporting business success.

[0097] The assistance system can estimate the user's emotions and provide appropriate feedback to the user based on the estimated emotions. For example, if the user is feeling anxious, it can elicit positive emotions by providing words of encouragement or examples of success. The assistance system can also analyze the user's emotions using an emotion estimation algorithm and provide appropriate feedback. This can improve the user's motivation and support business success by providing feedback that corresponds to the user's emotions.

[0098] The support system can estimate the user's emotions and provide appropriate learning resources to the user based on the estimated emotions. For example, if the user is excited, it can provide interesting learning resources and challenging tasks. The support system can also analyze the user's emotions using an emotion estimation algorithm and provide appropriate learning resources. By providing learning resources that correspond to the user's emotions, the system can increase the user's motivation to learn and support business success.

[0099] The support system can estimate a user's emotions and provide appropriate networking opportunities to the user based on the estimated emotions. For example, if the user feels lonely, the support system can introduce networking events or online communities. The support system can also analyze the user's emotions using an emotion estimation algorithm and provide appropriate networking opportunities. This can reduce the user's sense of loneliness and support business success by providing networking opportunities that correspond to the user's emotions.

[0100] The support system can estimate a user's emotions and provide appropriate mental health support to the user based on the estimated emotions. For example, if the user is feeling anxious, the support system can refer the user to a mental health professional or counseling service. The support system can also analyze the user's emotions using an emotion estimation algorithm and provide appropriate mental health support. By providing mental health support according to the user's emotions, the system can maintain the user's mental health and support business success.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The chat client accepts a question from the user. For example, the user can input the question in text format. The chat client also has a voice input function, allowing the user to input the question by voice. Step 2: The generation AI analyzes the question received by the chat client. For example, the generation AI uses natural language processing technology to understand the intent of the question and generate an appropriate answer. The generation AI can also use machine learning algorithms to analyze the content of the question and provide related information. Step 3: The knowledge graph extracts relevant information based on the question analyzed by the generation AI. For example, the knowledge graph can search for relevant information from a database and provide it to the generation AI. The knowledge graph can also obtain information from an external API and provide it to the generation AI. Step 4: The ad display unit displays ads based on the questions analyzed by the generation AI. For example, if a user asks about how to create a business plan, ads for related books and seminars will be displayed. The ad display unit can also analyze the user's past behavioral history to display the most relevant ads. Step 5: The privacy protection unit protects the user's data. For example, the privacy protection unit protects the user's personal information using encryption technology. The privacy protection unit can also perform access control to prevent unauthorized access by third parties. Step 6: The Marketing Strategy Department encourages the participation of experts through social media. For example, the Marketing Strategy Department can invite experts via social media to provide users with reliable information. The Marketing Strategy Department can also collect user feedback during the free service period and use it to improve the service.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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. Chat client and Generative AI and Knowledge graphs and An advertising display unit; Privacy Protection Department, A marketing strategy department is also included. The chat client Accepting user questions, The generated AI is analyzing the question received by the chat client; Extracting relevant information from the knowledge graph; The advertisement display unit Displaying an advertisement based on the question analyzed by the generating AI; The privacy protection unit Protect your data, The marketing strategy department Encouraging experts to participate through social media A system characterized by:

2. The chat client Equipped with voice input function, the user inputs the question by voice; The generative AI provides answers via voice 2. The system of claim 1.

3. The generated AI is Automatically collect and provide relevant expert opinions and comments from the knowledge graph in response to the user's question.

2. The system of claim 1.

4. The advertisement display unit Analyze the user's past behavior history and display the most relevant advertisements 2. The system of claim 1.

5. The privacy protection unit Store the user's data in a distributed storage system to enhance security.

2. The system of claim 1.

6. The marketing strategy department Through the SNS, the experts will regularly hold live-streamed knowledge sharing events.

2. The system of claim 1.

7. The generated AI is Analyze the user's emotions when asking a question and generate an answer that elicits positive emotions 2. The system of claim 1.

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