System

Abstract

An object of a system according to an embodiment is to support selection of an appropriate business area and matching of customers when starting an individual business.SOLUTION: A system includes a personal business support part, a proper area selection part, and a customer matching part. The private business support division supports the beginning of private business. The proper area selection unit selects a proper business area based on the skill and experience of the user. The customer matching unit matches a customer suitable for the business content of the user.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] With conventional technology, it was difficult to select the appropriate business area and match customers when starting a personal business, and support was insufficient.

[0005] The system according to the embodiment aims to support the selection of an appropriate business field and matching with customers when starting a personal business. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal business support unit, an appropriate business field selection unit, and a customer matching unit. The personal business support unit supports users in starting their own businesses. The appropriate business field selection unit selects an appropriate business field based on the user's skills and experience. The customer matching unit matches the user with customers suitable for their business. [Effects of the Invention]

[0007] The system according to the embodiment can support the selection of an appropriate business field and matching with customers when starting a personal business. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The personal business support system according to an embodiment of the present invention is a system that supports personal businesses that generate revenue without relying on an employer. This system uses generative AI to realize how to start a personal business, select an appropriate field, and match with customers. As a result, the personal business support system can support individuals in generating revenue without relying on an employer.

[0029] The personal business support system according to the embodiment includes a personal business support unit, an appropriate business domain selection unit, and a customer matching unit. The personal business support unit supports users in starting their personal business. For example, the generation AI provides specific steps and necessary information for starting a personal business. The generation AI guides users on, for example, how to create a business plan, how to raise the necessary funds, and how to proceed with legal procedures. The generation AI provides appropriate information based on prompts containing instructions on what the user wants the generation AI to do. For example, if a user inputs the prompt, "What are the steps to start a personal business?", the generation AI responds with, "First, create a business plan, then raise the necessary funds, and finally proceed with legal procedures." The appropriate business domain selection unit selects an appropriate business domain based on the user's skills and experience. For example, the generation AI suggests the most appropriate business domain based on the user's skills, interests, and experience. The generation AI analyzes information about the user's skills and experience to suggest an appropriate business domain. For example, if a user inputs information such as, "I'm good at programming," the generation AI would suggest, "I recommend that you use your programming skills to start a web development or app development business." The customer matching unit matches the user with customers suitable for their business. For example, the generation AI identifies customers interested in the services or products offered by the user and suggests ways to approach them. The generation AI analyzes information about the user's business and target customers to match appropriate customers. For example, if the user inputs information such as "I sell handmade accessories," the generation AI suggests, "I recommend identifying customers interested in handmade accessories and approaching them through social media or online marketplaces." This allows the personal business support system according to the embodiment to support individuals in generating revenue without relying on an employer. For example, individuals can start a business that best suits their skills and interests and increase revenue by approaching the right customers. Furthermore, businesses can be operated efficiently based on the information and suggestions provided by the generation AI.

[0030] The personal business support department can analyze the user's past failures and provide specific advice to avoid failure. For example, the generation AI collects the user's past business failures from a database and analyzes the causes of failure. For example, it identifies specific factors that led to failure, such as insufficient funds or market research, and provides advice to avoid them. The personal business support department also inputs a detailed history of the user's past business ventures, and the generation AI uses that data to analyze failure patterns. For example, it identifies trends in failure in specific industries or markets and suggests countermeasures. The personal business support department also uses the generation AI to provide specific steps to avoid similar failures based on the user's past failures. For example, it presents a specific action plan, such as reviewing the business plan or improving fundraising methods. This allows the user to receive specific advice to avoid past failures.

[0031] The Individual Business Support Department can propose a business plan optimized for the user's region, taking into account local laws, regulations, and market trends. For example, the generation AI collects local laws, regulations, and market trends based on the user's location information and proposes a business plan based on that information. For example, it provides information on local tax systems and licensing procedures. Furthermore, the generation AI analyzes local market trends based on the local information entered by the user and proposes an optimal business plan. For example, it presents a business strategy that takes into account local consumer needs and competitive conditions. Furthermore, the generation AI collects local laws, regulations, and market trends in real time and proposes a business plan based on the latest information to the user. For example, it provides advice that addresses new local laws, regulations, and market trends. This allows the proposal of a business plan that takes into account local laws, regulations, and market trends.

[0032] The personal business support department can customize how to start a personal business, taking into account the user's lifestyle and values. For example, the generation AI collects information about the user's lifestyle and values ​​and customizes how to start a personal business based on that information. For example, for a user who prioritizes a balance between work and family, the generation AI suggests a business that can be done from home. The personal business support department also proposes the optimal business plan based on the information about the values ​​and lifestyle entered by the user. For example, for a user who is interested in environmental protection, the generation AI suggests an eco-friendly business. The personal business support department also customizes how to start a personal business, taking into account the user's lifestyle and values. For example, for a health-conscious user, the generation AI suggests a fitness-related business. This makes it possible to customize how to start a personal business based on the user's lifestyle and values.

[0033] The personal business support department can refer to other success stories and suggest specific steps to the user based on those success stories. For example, in the personal business support department, the generation AI collects past success stories from a database and suggests specific steps to the user based on those stories. For example, it provides a business plan that references successful stories in the same industry. In addition, in the personal business support department, the generation AI searches for related success stories based on the information entered by the user and suggests specific steps based on those stories. For example, it introduces successful marketing strategies and fundraising methods. In addition, in the personal business support department, the generation AI refers to other success stories and suggests specific steps to the user based on those success stories. For example, it provides an action plan modeled on the growth process of a successful business. This makes it possible to suggest specific steps based on other success stories.

[0034] The appropriate area selection unit can analyze the user's skill set in detail and suggest new business areas based on the combination of skills. For example, the generation AI in the appropriate area selection unit analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a web design business is suggested for a user with programming and design skills. Furthermore, the appropriate area selection unit analyzes the skill combination based on the skill information entered by the user and suggests a new business area. For example, a digital marketing business is suggested for a user with marketing and data analysis skills. Furthermore, the appropriate area selection unit analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a cooking class and food photography business is suggested for a user with cooking and photography skills. This allows the generation AI to analyze the user's skill set in detail and suggest new business areas.

[0035] The appropriate area selection unit can evaluate the user's past projects and deliverables and select an appropriate area based on that. For example, the generation AI collects the user's past projects and deliverables from a database, evaluates them, and selects an appropriate area. For example, it suggests a similar business area based on a past successful project. The appropriate area selection unit also evaluates the information on past projects and deliverables entered by the user and selects an appropriate area. For example, it suggests a business in a specific field based on a project that received high praise in that field. The appropriate area selection unit also evaluates the user's past projects and deliverables and selects an appropriate area based on that. For example, it analyzes the success factors of past projects and suggests a business area where they can be utilized. This allows the generation AI to evaluate the user's past projects and deliverables and select an appropriate area.

[0036] The appropriate area selection unit analyzes trends in different industries and can suggest emerging markets where the user's skills can be utilized. For example, the generation AI collects trends in different industries from a database and suggests emerging markets where the user's skills can be utilized. For example, it suggests AI-related businesses based on trends in AI technology. The appropriate area selection unit also analyzes trends in different industries based on skill information entered by the user and suggests emerging markets. For example, it suggests financial technology-related businesses based on trends in fintech. The appropriate area selection unit also analyzes trends in different industries and suggests emerging markets where the user's skills can be utilized. For example, it suggests remote work support services based on trends in remote work. This makes it possible to analyze trends in different industries and suggest emerging markets where the user's skills can be utilized.

[0037] The appropriate domain selection unit considers the user's hobbies and interests and can suggest business domains based on them. In the appropriate domain selection unit, for example, the generation AI collects information about the user's hobbies and interests and suggests business domains based on that. For example, for a user whose hobby is gardening, the generation AI suggests a gardening-related business. In addition, the appropriate domain selection unit considers the user's hobbies and interests and suggests a business domain based on that. For example, for a user who likes gardening, the generation AI suggests a music-related business. In addition, the appropriate domain selection unit considers the user's hobbies and interests and suggests a business domain based on that. For example, for a user who likes traveling, the generation AI suggests a travel-related business. This makes it possible to suggest business domains based on the user's hobbies and interests.

[0038] The customer matching unit can analyze the user's past customer data and extract characteristics of repeat customers and highly rated customers to identify new customers. In the customer matching unit, for example, the generation AI collects the user's past customer data and analyzes the characteristics of repeat customers and highly rated customers. For example, it finds commonalities between customers based on purchase history and feedback to identify new customers. In addition, the customer matching unit extracts characteristics of repeat customers and highly rated customers based on past customer data entered by the user and identifies new customers based on that. For example, it targets customers with specific attributes and behavioral patterns. In addition, the customer matching unit analyzes the user's past customer data and extracts characteristics of repeat customers and highly rated customers to identify new customers. For example, it finds similar new customers based on the customer's purchasing trends and preferences. This makes it possible to extract characteristics of repeat customers and highly rated customers and identify new customers.

[0039] The customer matching unit can analyze in detail the characteristics of the services and products provided by the user and propose the most suitable customer segment. For example, the generation AI in the customer matching unit analyzes in detail the characteristics of the services and products provided by the user and proposes the most suitable customer segment. For example, it identifies target customers based on the product's functions and features. The customer matching unit also analyzes the characteristics of the service or product based on the information entered by the user, and proposes the most suitable customer segment. For example, it targets customer segments with specific needs and preferences. The customer matching unit also analyzes in detail the characteristics of the services and products provided by the user and proposes the most suitable customer segment. For example, it finds the suitable customer segment based on the product's usage scenario and convenience. This allows the generation AI to analyze the characteristics of the service or product in detail and propose the most suitable customer segment.

[0040] The customer matching unit can analyze different platforms and channels and propose the optimal customer acquisition method. For example, in the customer matching unit, the generation AI collects different platforms and channels from a database, analyzes them, and proposes the optimal customer acquisition method. For example, it provides a customer acquisition strategy that utilizes social media and online marketplaces. Furthermore, the customer matching unit analyzes different platforms and channels based on information entered by the user regarding the business content and target customers, and proposes the optimal customer acquisition method. For example, it proposes an advertising strategy for a specific platform. Furthermore, the customer matching unit analyzes different platforms and channels and proposes the optimal customer acquisition method. For example, it provides a cross-channel marketing strategy that combines multiple channels. This allows the generation AI to analyze different platforms and channels and propose the optimal customer acquisition method.

[0041] The customer matching unit can identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, the generation AI collects online communities and forums related to the user's business from a database, analyzes them, and suggests ways to attract customers. For example, it can recommend activities and posts in specific forums. The customer matching unit can also identify relevant communities and forums based on information entered by the user about the business and target customers, and suggest ways to attract customers. For example, it can suggest events and campaigns within the community. The customer matching unit can also identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, it can recommend sharing expertise and Q&A sessions within the forum. This allows the generation AI to identify communities and forums related to the business and suggest ways to attract customers.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The personal business support department can customize how to start a personal business, taking into account the user's lifestyle and values. For example, the generation AI collects information about the user's lifestyle and values ​​and customizes how to start a personal business based on that information. For example, for a user who prioritizes a balance between work and family, the generation AI can suggest a business that can be done from home. The personal business support department also proposes the optimal business plan based on the information about the values ​​and lifestyle entered by the user. For example, for a user who is interested in environmental protection, the generation AI can suggest an eco-friendly business. The personal business support department also customizes how to start a personal business, taking into account the user's lifestyle and values. For example, for a health-conscious user, the generation AI can suggest a fitness-related business. This makes it possible to customize how to start a personal business based on the user's lifestyle and values.

[0044] The personal business support department can refer to other success stories and suggest specific steps to the user based on those success stories. For example, the generation AI collects past success stories from a database and suggests specific steps to the user based on those stories. For example, it can provide a business plan that references successful cases in the same industry. The personal business support department also uses the generation AI to search for related success stories based on the information entered by the user and suggest specific steps based on those stories. For example, it can introduce successful marketing strategies and fundraising methods. The personal business support department can also refer to other success stories and suggest specific steps to the user based on those success stories. For example, it can provide an action plan modeled on the growth process of a successful business. This makes it possible to suggest specific steps based on other success stories.

[0045] The appropriate area selection unit analyzes the user's skill set in detail and can suggest new business areas based on the combination of skills. For example, the generation AI analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a web design business is suggested for a user with programming and design skills. The appropriate area selection unit also analyzes the skill combination based on the skill information entered by the user and suggests new business areas. For example, a digital marketing business is suggested for a user with marketing and data analysis skills. The appropriate area selection unit also analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a cooking class and food photography business is suggested for a user with cooking and photography skills. This allows the generation AI to analyze the user's skill set in detail and suggest new business areas.

[0046] The customer matching unit can analyze the user's past customer data and extract characteristics of repeat customers and highly rated customers to identify new customers. For example, the generation AI collects the user's past customer data and analyzes the characteristics of repeat customers and highly rated customers. For example, it finds commonalities between customers based on purchase history and feedback to identify new customers. The customer matching unit also extracts characteristics of repeat customers and highly rated customers based on the past customer data entered by the user, and identifies new customers based on that. For example, it targets customers with specific attributes and behavioral patterns. The customer matching unit also analyzes the user's past customer data and extracts characteristics of repeat customers and highly rated customers to identify new customers. For example, it finds similar new customers based on the customer's purchasing trends and preferences. This makes it possible to extract characteristics of repeat customers and highly rated customers and identify new customers.

[0047] The customer matching unit can analyze different platforms and channels and propose the optimal method of customer acquisition. For example, the generation AI collects different platforms and channels from a database, analyzes them, and proposes the optimal method of customer acquisition. For example, it can provide a customer acquisition strategy that utilizes social media and online marketplaces. The customer matching unit can also analyze different platforms and channels based on information entered by the user about the business content and target customers, and propose the optimal method of customer acquisition. For example, it can propose an advertising strategy for a specific platform. The customer matching unit can also analyze different platforms and channels and propose the optimal method of customer acquisition. For example, it can provide a cross-channel marketing strategy that combines multiple channels. This allows it to analyze different platforms and channels and propose the optimal method of customer acquisition.

[0048] The customer matching unit can identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, the generation AI collects online communities and forums related to the user's business from a database, analyzes them, and suggests ways to attract customers. For example, it can recommend activities and posts in specific forums. The customer matching unit can also identify relevant communities and forums based on information entered by the user about the business and target customers, and suggest ways to attract customers from them. For example, it can suggest events and campaigns within the community. The customer matching unit can also identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, it can recommend sharing expertise and Q&A sessions within the forum. This allows the generation AI to identify communities and forums related to the business and suggest ways to attract customers.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The personal business support department supports users in starting their own business. For example, the generation AI provides specific steps and necessary information for users to start their own business. The generation AI guides users on how to create a business plan, how to raise the necessary funds, how to proceed with legal procedures, and so on. It provides appropriate information based on prompts containing instructions on what the user wants the generation AI to do. For example, if the user enters the prompt, "What are the steps to start a personal business?", the generation AI will respond in the form of, "First, you need to create a business plan, then raise the necessary funds, and finally proceed with legal procedures." Step 2: The appropriate field selection unit selects an appropriate business field based on the user's skills and experience. For example, the generation AI suggests the most suitable business field based on the user's skills, interests, and experience. The generation AI analyzes information about the user's skills and experience to suggest an appropriate business field. For example, if you enter information such as "I'm good at programming," the generation AI will suggest something like, "I recommend that you use your programming skills to start a web development or app development business." Step 3: The customer matching unit matches customers who are suitable for the user's business. For example, the generation AI identifies customers who are interested in the services and products provided by the user and suggests ways to approach them. The generation AI analyzes information about the user's business and target customers to match appropriate customers. For example, if you enter information such as "I sell handmade accessories," the generation AI will suggest something like, "I recommend identifying customers who are interested in handmade accessories and approaching them through social media or online marketplaces."

[0051] (Example 2) The personal business support system according to an embodiment of the present invention is a system that supports personal businesses that generate revenue without relying on an employer. This system uses generative AI to realize how to start a personal business, select an appropriate field, and match with customers. As a result, the personal business support system can support individuals in generating revenue without relying on an employer.

[0052] The personal business support system according to the embodiment includes a personal business support unit, an appropriate business domain selection unit, and a customer matching unit. The personal business support unit supports users in starting their personal business. For example, the generation AI provides specific steps and necessary information for starting a personal business. The generation AI guides users on, for example, how to create a business plan, how to raise the necessary funds, and how to proceed with legal procedures. The generation AI provides appropriate information based on prompts containing instructions on what the user wants the generation AI to do. For example, if a user inputs the prompt, "What are the steps to start a personal business?", the generation AI responds with, "First, create a business plan, then raise the necessary funds, and finally proceed with legal procedures." The appropriate business domain selection unit selects an appropriate business domain based on the user's skills and experience. For example, the generation AI suggests the most appropriate business domain based on the user's skills, interests, and experience. The generation AI analyzes information about the user's skills and experience to suggest an appropriate business domain. For example, if a user inputs information such as, "I'm good at programming," the generation AI would suggest, "I recommend that you use your programming skills to start a web development or app development business." The customer matching unit matches the user with customers suitable for their business. For example, the generation AI identifies customers interested in the services or products offered by the user and suggests ways to approach them. The generation AI analyzes information about the user's business and target customers to match appropriate customers. For example, if the user inputs information such as "I sell handmade accessories," the generation AI suggests, "I recommend identifying customers interested in handmade accessories and approaching them through social media or online marketplaces." This allows the personal business support system according to the embodiment to support individuals in generating revenue without relying on an employer. For example, individuals can start a business that best suits their skills and interests and increase revenue by approaching the right customers. Furthermore, businesses can be operated efficiently based on the information and suggestions provided by the generation AI.

[0053] The personal business support department can analyze the user's past failures and provide specific advice to avoid failure. For example, the generation AI collects the user's past business failures from a database and analyzes the causes of failure. For example, it identifies specific factors that led to failure, such as insufficient funds or market research, and provides advice to avoid them. The personal business support department also inputs a detailed history of the user's past business ventures, and the generation AI uses that data to analyze failure patterns. For example, it identifies trends in failure in specific industries or markets and suggests countermeasures. The personal business support department also uses the generation AI to provide specific steps to avoid similar failures based on the user's past failures. For example, it presents a specific action plan, such as reviewing the business plan or improving fundraising methods. This allows the user to receive specific advice to avoid past failures.

[0054] The Individual Business Support Department can propose a business plan optimized for the user's region, taking into account local laws, regulations, and market trends. For example, the generation AI collects local laws, regulations, and market trends based on the user's location information and proposes a business plan based on that information. For example, it provides information on local tax systems and licensing procedures. Furthermore, the generation AI analyzes local market trends based on the local information entered by the user and proposes an optimal business plan. For example, it presents a business strategy that takes into account local consumer needs and competitive conditions. Furthermore, the generation AI collects local laws, regulations, and market trends in real time and proposes a business plan based on the latest information to the user. For example, it provides advice that addresses new local laws, regulations, and market trends. This allows the proposal of a business plan that takes into account local laws, regulations, and market trends.

[0055] The personal business support unit can use the emotion estimation function to detect the user's anxieties and doubts and provide encouragement and specific solutions to those concerns. For example, the personal business support unit can use the emotion estimation function to detect the user's anxieties and doubts when starting a business in real time and provide encouraging messages in response. For example, it can display positive messages such as, "You can succeed!" The personal business support unit can also analyze text and voice data entered by the user and use the emotion estimation function to identify the anxieties and doubts. For example, for an anxiety such as, "I'm worried about fundraising," it can suggest specific fundraising methods. The personal business support unit can also use the emotion estimation function to detect the user's anxieties and doubts and provide specific solutions to those concerns. For example, for an anxiety such as, "Your market research is insufficient," it can provide guidance on detailed market research methods. This makes it possible to provide encouragement and specific solutions to the user's anxieties and doubts.

[0056] The personal business support department can customize how to start a personal business, taking into account the user's lifestyle and values. For example, the generation AI collects information about the user's lifestyle and values ​​and customizes how to start a personal business based on that information. For example, for a user who prioritizes a balance between work and family, the generation AI suggests a business that can be done from home. The personal business support department also proposes the optimal business plan based on the information about the values ​​and lifestyle entered by the user. For example, for a user who is interested in environmental protection, the generation AI suggests an eco-friendly business. The personal business support department also customizes how to start a personal business, taking into account the user's lifestyle and values. For example, for a health-conscious user, the generation AI suggests a fitness-related business. This makes it possible to customize how to start a personal business based on the user's lifestyle and values.

[0057] The personal business support department can refer to other success stories and suggest specific steps to the user based on those success stories. For example, in the personal business support department, the generation AI collects past success stories from a database and suggests specific steps to the user based on those stories. For example, it provides a business plan that references successful stories in the same industry. In addition, in the personal business support department, the generation AI searches for related success stories based on the information entered by the user and suggests specific steps based on those stories. For example, it introduces successful marketing strategies and fundraising methods. In addition, in the personal business support department, the generation AI refers to other success stories and suggests specific steps to the user based on those success stories. For example, it provides an action plan modeled on the growth process of a successful business. This makes it possible to suggest specific steps based on other success stories.

[0058] The personal business support department can use the emotion estimation function to suggest the timing for starting a business when the user feels most motivated. For example, the personal business support department uses the emotion estimation function to identify the timing when the user feels most motivated and suggests starting a business at that timing. For example, it finds out when the user has positive emotions and advises the user to start a business at that time. The personal business support department also analyzes the user's emotion data to identify the timing when the user is most motivated. For example, it suggests starting a business in conjunction with a specific event or season. The personal business support department also uses the emotion estimation function to suggest the timing for starting a business when the user feels most motivated. For example, it advises the user to start a business at a similar timing based on a time when the user had a successful experience in the past. This allows the user to start a business at a time when they feel most motivated.

[0059] The appropriate area selection unit can analyze the user's skill set in detail and suggest new business areas based on the combination of skills. For example, the generation AI in the appropriate area selection unit analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a web design business is suggested for a user with programming and design skills. Furthermore, the appropriate area selection unit analyzes the skill combination based on the skill information entered by the user and suggests a new business area. For example, a digital marketing business is suggested for a user with marketing and data analysis skills. Furthermore, the appropriate area selection unit analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a cooking class and food photography business is suggested for a user with cooking and photography skills. This allows the generation AI to analyze the user's skill set in detail and suggest new business areas.

[0060] The appropriate area selection unit can evaluate the user's past projects and deliverables and select an appropriate area based on that. For example, the generation AI collects the user's past projects and deliverables from a database, evaluates them, and selects an appropriate area. For example, it suggests a similar business area based on a past successful project. The appropriate area selection unit also evaluates the information on past projects and deliverables entered by the user and selects an appropriate area. For example, it suggests a business in a specific field based on a project that received high praise in that field. The appropriate area selection unit also evaluates the user's past projects and deliverables and selects an appropriate area based on that. For example, it analyzes the success factors of past projects and suggests a business area where they can be utilized. This allows the generation AI to evaluate the user's past projects and deliverables and select an appropriate area.

[0061] The appropriate area selection unit can use the emotion estimation function to identify the area in which the user feels most passionate and propose business development in that area. The appropriate area selection unit, for example, uses the emotion estimation function to identify the area in which the user feels most passionate and propose business development in that area. For example, it proposes a business based on a hobby or interest that the user is very passionate about. The appropriate area selection unit also analyzes the user's emotion data to identify the area in which the user feels most passionate. For example, it finds an area in which past experiences and activities have strong positive emotions and proposes a business in that area. The appropriate area selection unit also uses the emotion estimation function to identify the area in which the user feels most passionate and proposes business development in that area. For example, it provides a business plan that references success stories in an area in which the user feels passionate. This makes it possible to propose business development in an area in which the user feels most passionate.

[0062] The appropriate area selection unit analyzes trends in different industries and can suggest emerging markets where the user's skills can be utilized. For example, the generation AI collects trends in different industries from a database and suggests emerging markets where the user's skills can be utilized. For example, it suggests AI-related businesses based on trends in AI technology. The appropriate area selection unit also analyzes trends in different industries based on skill information entered by the user and suggests emerging markets. For example, it suggests financial technology-related businesses based on trends in fintech. The appropriate area selection unit also analyzes trends in different industries and suggests emerging markets where the user's skills can be utilized. For example, it suggests remote work support services based on trends in remote work. This makes it possible to analyze trends in different industries and suggest emerging markets where the user's skills can be utilized.

[0063] The appropriate domain selection unit considers the user's hobbies and interests and can suggest business domains based on them. In the appropriate domain selection unit, for example, the generation AI collects information about the user's hobbies and interests and suggests business domains based on that. For example, for a user whose hobby is gardening, the generation AI suggests a gardening-related business. In addition, the appropriate domain selection unit considers the user's hobbies and interests and suggests a business domain based on that. For example, for a user who likes gardening, the generation AI suggests a music-related business. In addition, the appropriate domain selection unit considers the user's hobbies and interests and suggests a business domain based on that. For example, for a user who likes traveling, the generation AI suggests a travel-related business. This makes it possible to suggest business domains based on the user's hobbies and interests.

[0064] The appropriate area selection unit can use the emotion estimation function to identify the business area in which the user will be most satisfied and propose business development in that area. The appropriate area selection unit can, for example, use the emotion estimation function to identify the business area in which the user will be most satisfied and propose business development in that area. For example, it can propose a business based on activities that the user has felt highly satisfied in the past. The appropriate area selection unit can also analyze the user's emotion data to identify the business area in which the user will be most satisfied. For example, it can find an area in which a specific activity or experience has strong positive emotions and propose a business in that area. The appropriate area selection unit can also use the emotion estimation function to identify the business area in which the user will be most satisfied and propose business development in that area. For example, it can provide a business plan based on the hobbies and interests that the user finds satisfying. This makes it possible to propose business development in a business area in which the user will be most satisfied.

[0065] The customer matching unit can analyze the user's past customer data and extract characteristics of repeat customers and highly rated customers to identify new customers. In the customer matching unit, for example, the generation AI collects the user's past customer data and analyzes the characteristics of repeat customers and highly rated customers. For example, it finds commonalities between customers based on purchase history and feedback to identify new customers. In addition, the customer matching unit extracts characteristics of repeat customers and highly rated customers based on past customer data entered by the user and identifies new customers based on that. For example, it targets customers with specific attributes and behavioral patterns. In addition, the customer matching unit analyzes the user's past customer data and extracts characteristics of repeat customers and highly rated customers to identify new customers. For example, it finds similar new customers based on the customer's purchasing trends and preferences. This makes it possible to extract characteristics of repeat customers and highly rated customers and identify new customers.

[0066] The customer matching unit can analyze in detail the characteristics of the services and products provided by the user and propose the most suitable customer segment. For example, the generation AI in the customer matching unit analyzes in detail the characteristics of the services and products provided by the user and proposes the most suitable customer segment. For example, it identifies target customers based on the product's functions and features. The customer matching unit also analyzes the characteristics of the service or product based on the information entered by the user, and proposes the most suitable customer segment. For example, it targets customer segments with specific needs and preferences. The customer matching unit also analyzes in detail the characteristics of the services and products provided by the user and proposes the most suitable customer segment. For example, it finds the suitable customer segment based on the product's usage scenario and convenience. This allows the generation AI to analyze the characteristics of the service or product in detail and propose the most suitable customer segment.

[0067] The customer matching unit can use the emotion estimation function to analyze customers' emotional responses and identify the customer segment that will show the most positive responses. The customer matching unit, for example, uses the emotion estimation function to analyze customers' emotional responses in real time and identify the customer segment that will show the most positive responses. For example, the customer matching unit analyzes customers' facial expressions and voices and targets customers with a high positive emotion score. The customer matching unit also collects customers' emotional responses to services and products provided by users and uses the emotion estimation function to identify the customer segment that will show the most positive responses. For example, the customer matching unit analyzes customer feedback and reviews. The customer matching unit also uses the emotion estimation function to analyze customers' emotional responses and identify the customer segment that will show the most positive responses. For example, the customer matching unit finds customers with positive emotions based on their purchasing behavior and usage experience. This makes it possible to analyze customers' emotional responses and identify the customer segment that will show the most positive responses.

[0068] The customer matching unit can analyze different platforms and channels and propose the optimal customer acquisition method. For example, in the customer matching unit, the generation AI collects different platforms and channels from a database, analyzes them, and proposes the optimal customer acquisition method. For example, it provides a customer acquisition strategy that utilizes social media and online marketplaces. Furthermore, the customer matching unit analyzes different platforms and channels based on information entered by the user regarding the business content and target customers, and proposes the optimal customer acquisition method. For example, it proposes an advertising strategy for a specific platform. Furthermore, the customer matching unit analyzes different platforms and channels and proposes the optimal customer acquisition method. For example, it provides a cross-channel marketing strategy that combines multiple channels. This allows the generation AI to analyze different platforms and channels and propose the optimal customer acquisition method.

[0069] The customer matching unit can identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, the generation AI collects online communities and forums related to the user's business from a database, analyzes them, and suggests ways to attract customers. For example, it can recommend activities and posts in specific forums. The customer matching unit can also identify relevant communities and forums based on information entered by the user about the business and target customers, and suggest ways to attract customers. For example, it can suggest events and campaigns within the community. The customer matching unit can also identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, it can recommend sharing expertise and Q&A sessions within the forum. This allows the generation AI to identify communities and forums related to the business and suggest ways to attract customers.

[0070] The customer matching unit can use the emotion estimation function to propose a marketing strategy based on the customer's emotions and attract the customer's attention. The customer matching unit, for example, uses the emotion estimation function to analyze customer emotion data and propose a marketing strategy based on the data. For example, it proposes advertising copy and visuals that elicit positive emotions. The customer matching unit also collects customers' emotional responses to services and products provided by users and proposes a marketing strategy using the emotion estimation function. For example, it implements a campaign targeted at customers with high emotion scores. The customer matching unit also uses the emotion estimation function to propose a marketing strategy based on the customer's emotions and attract the customer's attention. For example, it creates content that is likely to resonate emotionally based on the customer's emotion data. This makes it possible to propose a marketing strategy based on the customer's emotions and attract the customer's attention.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The personal business support department can customize how to start a personal business, taking into account the user's lifestyle and values. For example, the generation AI collects information about the user's lifestyle and values ​​and customizes how to start a personal business based on that information. For example, for a user who prioritizes a balance between work and family, the generation AI can suggest a business that can be done from home. The personal business support department also proposes the optimal business plan based on the information about the values ​​and lifestyle entered by the user. For example, for a user who is interested in environmental protection, the generation AI can suggest an eco-friendly business. The personal business support department also customizes how to start a personal business, taking into account the user's lifestyle and values. For example, for a health-conscious user, the generation AI can suggest a fitness-related business. This makes it possible to customize how to start a personal business based on the user's lifestyle and values.

[0073] The personal business support department can refer to other success stories and suggest specific steps to the user based on those success stories. For example, the generation AI collects past success stories from a database and suggests specific steps to the user based on those stories. For example, it can provide a business plan that references successful cases in the same industry. The personal business support department also uses the generation AI to search for related success stories based on the information entered by the user and suggest specific steps based on those stories. For example, it can introduce successful marketing strategies and fundraising methods. The personal business support department can also refer to other success stories and suggest specific steps to the user based on those success stories. For example, it can provide an action plan modeled on the growth process of a successful business. This makes it possible to suggest specific steps based on other success stories.

[0074] The personal business support department can use the emotion estimation function to detect the user's anxieties and doubts and provide encouragement and specific solutions to those concerns. For example, the emotion estimation function can be used to detect the user's anxieties and doubts when starting a business in real time and provide encouraging messages in response. For example, a positive message such as "You can succeed!" can be displayed. The personal business support department can also analyze text and voice data entered by the user and use the emotion estimation function to identify the anxieties and doubts. For example, for an anxiety such as "I'm worried about fundraising," the department can suggest specific fundraising methods. The personal business support department can also use the emotion estimation function to detect the user's anxieties and doubts and provide specific solutions to those concerns. For example, for a concern such as "Your market research is insufficient," the department can provide guidance on detailed market research methods. This makes it possible to provide encouragement and specific solutions to the user's anxieties and doubts.

[0075] The appropriate area selection unit analyzes the user's skill set in detail and can suggest new business areas based on the combination of skills. For example, the generation AI analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a web design business is suggested for a user with programming and design skills. The appropriate area selection unit also analyzes the skill combination based on the skill information entered by the user and suggests new business areas. For example, a digital marketing business is suggested for a user with marketing and data analysis skills. The appropriate area selection unit also analyzes the user's skill set in detail and suggests new business areas based on the combination of skills. For example, a cooking class and food photography business is suggested for a user with cooking and photography skills. This allows the generation AI to analyze the user's skill set in detail and suggest new business areas.

[0076] The appropriate area selection unit can use the emotion estimation function to identify the area in which the user feels most passionate and propose business development in that area. For example, the emotion estimation function can be used to identify the area in which the user feels most passionate and propose business development in that area. For example, a business based on a hobby or interest that the user is very passionate about can be proposed. The appropriate area selection unit can also analyze the user's emotion data to identify the area in which the user feels most passionate. For example, it can find an area in which past experiences and activities have strong positive emotions and propose a business in that area. The appropriate area selection unit can also use the emotion estimation function to identify the area in which the user feels most passionate and propose business development in that area. For example, it can provide a business plan that references success stories in an area in which the user is passionate. This makes it possible to propose business development in an area in which the user feels most passionate.

[0077] The customer matching unit can analyze the user's past customer data and extract characteristics of repeat customers and highly rated customers to identify new customers. For example, the generation AI collects the user's past customer data and analyzes the characteristics of repeat customers and highly rated customers. For example, it finds commonalities between customers based on purchase history and feedback to identify new customers. The customer matching unit also extracts characteristics of repeat customers and highly rated customers based on the past customer data entered by the user, and identifies new customers based on that. For example, it targets customers with specific attributes and behavioral patterns. The customer matching unit also analyzes the user's past customer data and extracts characteristics of repeat customers and highly rated customers to identify new customers. For example, it finds similar new customers based on the customer's purchasing trends and preferences. This makes it possible to extract characteristics of repeat customers and highly rated customers and identify new customers.

[0078] The customer matching unit can use the emotion estimation function to analyze customers' emotional responses and identify the customer segment that will have the most positive responses. For example, the emotion estimation function can be used to analyze customers' emotional responses in real time and identify the customer segment that will have the most positive responses. For example, the emotion estimation function can be used to analyze customers' emotional responses in real time and identify the customer segment that will have the most positive responses. For example, the emotion estimation function can be used to analyze customers' facial expressions and voices and target customers with high positive emotion scores. The customer matching unit can also collect customers' emotional responses to services and products provided by users and use the emotion estimation function to identify the customer segment that will have the most positive responses. For example, the emotion estimation function can be used to analyze customers' emotional responses and identify the customer segment that will have the most positive responses. For example, the emotion estimation function can be used to find customers with positive emotions based on their purchasing behavior and usage experience. This makes it possible to analyze customers' emotional responses and identify the customer segment that will have the most positive responses.

[0079] The customer matching unit can analyze different platforms and channels and propose the optimal method of customer acquisition. For example, the generation AI collects different platforms and channels from a database, analyzes them, and proposes the optimal method of customer acquisition. For example, it can provide a customer acquisition strategy that utilizes social media and online marketplaces. The customer matching unit can also analyze different platforms and channels based on information entered by the user about the business content and target customers, and propose the optimal method of customer acquisition. For example, it can propose an advertising strategy for a specific platform. The customer matching unit can also analyze different platforms and channels and propose the optimal method of customer acquisition. For example, it can provide a cross-channel marketing strategy that combines multiple channels. This allows it to analyze different platforms and channels and propose the optimal method of customer acquisition.

[0080] The customer matching unit can identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, the generation AI collects online communities and forums related to the user's business from a database, analyzes them, and suggests ways to attract customers. For example, it can recommend activities and posts in specific forums. The customer matching unit can also identify relevant communities and forums based on information entered by the user about the business and target customers, and suggest ways to attract customers from them. For example, it can suggest events and campaigns within the community. The customer matching unit can also identify communities and forums related to the user's business and suggest ways to attract customers from them. For example, it can recommend sharing expertise and Q&A sessions within the forum. This allows the generation AI to identify communities and forums related to the business and suggest ways to attract customers.

[0081] The customer matching unit can use the emotion estimation function to propose a marketing strategy based on the customer's emotions and attract their interest. For example, the emotion estimation function can be used to analyze customer emotion data and propose a marketing strategy based on that. For example, advertising copy and visuals that elicit positive emotions can be proposed. The customer matching unit also collects customers' emotional responses to the services and products provided by the user and uses the emotion estimation function to propose a marketing strategy. For example, a campaign targeted at customers with high emotion scores can be implemented. The customer matching unit also uses the emotion estimation function to propose a marketing strategy based on the customer's emotions and attract their interest. For example, content that is likely to resonate emotionally can be created based on the customer's emotion data. This makes it possible to propose a marketing strategy based on the customer's emotions and attract their interest.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The personal business support department supports users in starting their own business. For example, the generation AI provides specific steps and necessary information for users to start their own business. The generation AI guides users on how to create a business plan, how to raise the necessary funds, how to proceed with legal procedures, and so on. It provides appropriate information based on prompts containing instructions on what the user wants the generation AI to do. For example, if the user enters the prompt, "What are the steps to start a personal business?", the generation AI will respond in the form of, "First, you need to create a business plan, then raise the necessary funds, and finally proceed with legal procedures." Step 2: The appropriate field selection unit selects an appropriate business field based on the user's skills and experience. For example, the generation AI suggests the most suitable business field based on the user's skills, interests, and experience. The generation AI analyzes information about the user's skills and experience to suggest an appropriate business field. For example, if you enter information such as "I'm good at programming," the generation AI will suggest something like, "I recommend that you use your programming skills to start a web development or app development business." Step 3: The customer matching unit matches customers who are suitable for the user's business. For example, the generation AI identifies customers who are interested in the services and products provided by the user and suggests ways to approach them. The generation AI analyzes information about the user's business and target customers to match appropriate customers. For example, if you enter information such as "I sell handmade accessories," the generation AI will suggest something like, "I recommend identifying customers who are interested in handmade accessories and approaching them through social media or online marketplaces."

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

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

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. The Personal Business Support Department provides support for starting personal businesses, an appropriate business field selection department that selects an appropriate business field based on the user's skills and experience; A customer matching unit that matches customers suitable for the user's business content. A system characterized by:

2. The Personal Business Support Department: Analyze the user's past failure experiences and provide specific advice to avoid failure 2. The system of claim 1.

3. The Personal Business Support Department: Propose business plans optimized for the user's region, taking into consideration local regulations and market trends.

2. The system of claim 1.

4. The Personal Business Support Department: Detecting the user's anxieties and doubts and providing encouragement and specific solutions to them 2. The system of claim 1.

5. The Personal Business Support Department: Customize how users start their own businesses, taking into account their lifestyles and values.

2. The system of claim 1.

Images