system

Abstract

The system according to this embodiment aims to streamline the preparation for opening a business and propose an optimal flow from opening to the start of operations. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, and an instruction unit. The reception unit receives information from the user regarding the type of business, business model, planned opening area, and preparation funds. The proposal unit analyzes the information received by the reception unit and proposes the optimal flow from opening to commencement of operations. The instruction unit provides instructions for specific actions based on the flow proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] [[ID=E23]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes a lot of time and labor for opening a business, and it is difficult to proceed efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of opening a business and propose an optimal process from opening to the start of operation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, and an instruction unit. The reception unit receives information from the user regarding the type of business, business model, planned opening area, and initial capital. The proposal unit analyzes the information received by the reception unit and proposes the optimal flow from opening to commencement of operations. The instruction unit provides specific instructions based on the flow proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the preparation for opening a business and propose an optimal flow from opening to the start of operations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The business startup support system according to an embodiment of the present invention is an AI-powered service designed to streamline many of the tasks required for business startup. This system allows users to input information such as industry, business type, planned location, and startup capital. The AI ​​analyzes this information and proposes an optimal flow from startup to operation. The proposal includes concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit applications, interior construction and equipment preparation, staff recruitment and training, and promotional activities and information dissemination. This streamlines tasks that typically consume a significant amount of time during the startup phase. For example, if a user is opening a restaurant, they would input information such as "restaurant" as the industry, "cafe" as the business type, "Shibuya-ku, Tokyo" as the planned location, and "10 million yen" as the startup capital. This information is then input into the AI. Next, the AI ​​analyzes the input information and proposes an optimal flow from startup to operation. For example, in the case of opening a restaurant, the AI ​​proposes the following flow: First, as concept design, the industry, business type, target audience, and strengths of the restaurant are clarified. Next, as business plan formulation, a concrete business plan is created, and preparations for fundraising are made. This is also necessary for applying for loans and subsidies. Furthermore, regarding property search and contract, you will need to find a suitable property for the store and sign a contract. It is important to choose carefully, taking into account rent and location conditions. In addition, as part of menu development, you will devise the menu to be offered and prepare the kitchen equipment and supplies accordingly. As part of obtaining qualifications and permits, you will obtain qualifications such as food hygiene manager and fire safety manager, and obtain the necessary permits from the public health center and fire department. As part of interior construction and equipment preparation, you will carry out interior construction of the store and prepare the necessary kitchen equipment and seating area furnishings. As part of staff recruitment and training, you will recruit and train staff before opening. Recruitment should ideally be done one month before opening. As part of sales promotion and information dissemination, you will conduct promotional activities to increase awareness of the store through flyers, social media, etc. In this way, by proceeding with opening preparations according to the flow suggested by AI, it is possible to streamline tasks that take up a lot of time when opening a business. For example, by following the property search flow suggested by AI, you can find a suitable property in a short period of time. Also, by following the menu development flow suggested by AI, you can efficiently devise a menu and prepare the necessary equipment.Furthermore, by following the staff recruitment and training process suggested by the AI, it is possible to recruit the right staff and conduct effective training. This system makes it possible to streamline many of the tasks required for opening a business, allowing entrepreneurs to proceed with the opening more smoothly. For example, in the preparation for opening a restaurant, following the process suggested by the AI ​​can shorten the time until opening and allow preparations to proceed efficiently. Also, by following the process suggested by the AI, the costs associated with opening a business can be reduced and funds can be managed efficiently. As a result, entrepreneurs can proceed with the opening more smoothly and increase their chances of success. In short, the business preparation support system enables entrepreneurs to proceed with preparations efficiently and increases the success rate of their business.

[0029] The business startup support system according to this embodiment comprises a reception unit, a proposal unit, and an instruction unit. The reception unit receives information from the user regarding the type of business, business format, planned business area, and startup capital. For example, if the user is opening a restaurant, the reception unit can receive information such as "restaurant" as the type of business, "cafe" as the business format, "Shibuya Ward, Tokyo" as the planned business area, and "10 million yen" as the startup capital. The proposal unit analyzes the information received by the reception unit and proposes the optimal flow from opening to operation. For example, the proposal unit can propose concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit application, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. For example, in the case of opening a restaurant, the proposal unit can propose concept design that clarifies the type of business, business format, target audience, and strengths of the store. The proposal unit can also propose the formulation of a business plan that creates a concrete business plan and prepares for fundraising. Furthermore, the proposal unit can propose property search and contract for a suitable store. The proposal department can propose menu development, including devising menus to be offered and preparing corresponding kitchen equipment and supplies. The proposal department can propose obtaining qualifications such as food hygiene manager and fire safety manager, and applying for necessary permits and licenses from public health centers and fire departments. The proposal department can propose interior construction and equipment preparation, including carrying out interior construction of the store and equipping it with necessary kitchen equipment and seating fixtures. The proposal department can propose staff recruitment and training, including hiring and training staff before opening. The proposal department can propose sales promotion activities and information dissemination, including promotional activities to increase awareness of the store through flyers and social media. The instruction department provides specific instructions based on the flow proposed by the proposal department. For example, the instruction department can provide instructions for specific actions such as property search, menu development, staff recruitment and training. The instruction department can provide instructions for specific actions such as obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination. In this way, the business opening preparation support system according to the embodiment can streamline business opening preparations by proposing the optimal flow from opening to operation start based on user information and providing instructions for specific actions.

[0030] The reception desk receives information from users regarding their industry, business type, planned opening area, and initial capital. Specifically, users access the system and provide the necessary information through a dedicated input form. For example, if a user is opening a restaurant, they would enter information such as "restaurant" as the industry, "cafe" as the business type, "Shibuya Ward, Tokyo" as the planned opening area, and "10 million yen" as the initial capital. The reception desk stores this information in a database and uses it for subsequent processing. Furthermore, the reception desk has a function to check the consistency of the information entered by the user and prompt the user to correct or add any missing or inaccurate information. For example, if the planned opening area is not specific, the system will instruct the user to enter a specific area name. The reception desk can also provide initial feedback based on the information entered by the user. For example, if the initial capital is insufficient, the system will suggest methods of fundraising or budget revisions to the user. This allows the reception desk to efficiently collect information from users and prepare the data necessary for subsequent suggestions and instructions. Furthermore, the reception department has implemented security measures to safely manage user information, including data encryption and access control to protect user privacy.

[0031] The Proposal Department analyzes the information received by the Reception Department and proposes the optimal flow from opening to operational commencement. Specifically, the Proposal Department uses AI to generate an optimal opening plan based on information provided by the user, such as the type of business, business model, planned opening area, and preparation funds. For example, in the case of opening a restaurant, the Proposal Department can propose a concept design that clarifies the type of business, business model, target audience, and strengths of the store. The AI ​​analyzes past success stories and market data to propose the optimal concept for the user. The Proposal Department can also propose the development of a business plan that includes creating a concrete business plan and preparing for fundraising. The AI ​​generates a realistic business plan considering the user's preparation funds and market conditions. Furthermore, the Proposal Department can propose the search and contracting of a property suitable for the store. The AI ​​analyzes real estate data of the planned opening area and proposes the optimal property for the user. The Proposal Department can propose menu development, including devising the menu to be offered and preparing the corresponding kitchen equipment and supplies. The AI ​​analyzes trends and the menus of competing restaurants to propose the optimal menu for the user. The proposal department can propose obtaining qualifications such as food hygiene manager and fire safety manager certifications, and applying for necessary permits and licenses from public health centers and fire departments. The AI ​​automates the procedures for obtaining necessary qualifications and licenses, and provides users with specific steps. The proposal department can propose interior construction and equipment preparation, including the installation of necessary kitchen equipment and seating area furnishings. The AI ​​optimizes the interior design and equipment layout, and provides users with a concrete plan. The proposal department can propose staff recruitment and training, including hiring and training staff before opening. The AI ​​analyzes job postings and provides advice to find the most suitable staff. The proposal department can propose promotional activities and information dissemination, such as flyers and social media, to increase awareness of the store. The AI ​​generates effective promotional strategies and provides users with concrete action plans. In this way, the proposal department can provide users with a comprehensive and specific opening plan, streamlining the opening preparations.

[0032] The Instruction Department directs specific actions based on the flow proposed by the Proposal Department. Specifically, the Instruction Department can direct specific actions for property search, menu development, and staff recruitment and training. For example, in property search, it provides the user with a list of specific properties and coordinates viewing schedules. In menu development, it directs the user to create prototype menus and hold tasting sessions. In staff recruitment and training, it directs the creation of job advertisements, scheduling interviews, and implementing training programs. The Instruction Department can also direct specific actions for obtaining qualifications and applying for licenses, interior construction and equipment preparation, and sales promotion activities and information dissemination. For example, in obtaining qualifications and applying for licenses, it directs the user to prepare and submit the necessary documents. In interior construction and equipment preparation, it directs meetings with contractors and management of construction progress. In sales promotion activities and information dissemination, it directs the design and distribution of flyers and the timing and content of information dissemination on social media. Furthermore, the Instruction Department monitors the user's progress in real time and provides additional instructions and support as needed. For example, if things are not progressing as planned or problems arise, the instruction department will quickly propose solutions and provide specific instructions to the user. In this way, the instruction department can support the user in smoothly preparing for the opening of their business and help ensure its success.

[0033] The proposal department can propose concept design, business plan formulation, property search and contract, menu development, qualification acquisition and license application, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. For example, as part of concept design, the proposal department can clarify the type of restaurant to be opened, business model, target audience, and strengths of the store. For example, as part of business plan formulation, the proposal department can create a concrete business plan and prepare for fundraising. For example, as part of property search and contract, the proposal department can find a suitable property for the store and conclude a contract. For example, as part of menu development, the proposal department can devise a menu to be offered and prepare the corresponding kitchen equipment and supplies. For example, as part of qualification acquisition and license application, the proposal department can acquire qualifications such as food hygiene manager and fire safety manager and obtain the necessary licenses from the public health center and fire department. For example, as part of interior construction and equipment preparation, the proposal department can carry out interior construction of the store and procure the necessary kitchen equipment and seating area furnishings. For example, as part of staff recruitment and training, the proposal department can recruit staff and conduct training before opening. The proposal department can, for example, conduct promotional activities to increase store awareness through flyers, social media, and other means as part of sales promotion and information dissemination. This allows users to efficiently proceed with their store opening preparations by having the proposal department suggest specific steps for each stage of preparation.

[0034] The instruction department can direct specific actions such as property search, menu development, and staff recruitment and training, based on the flow proposed by the proposal department. For example, the instruction department can direct specific actions such as finding a suitable property for the store and signing a contract. For example, the instruction department can direct specific actions such as devising a menu to be offered and preparing the corresponding kitchen equipment and supplies for menu development. For example, the instruction department can direct specific actions such as recruiting and training staff before opening. In this way, the instruction department directs specific actions, allowing users to efficiently proceed with opening preparations.

[0035] The proposal department can propose the optimal flow from opening to operation based on the industry, business type, planned opening area, and initial capital. For example, if opening a restaurant, the proposal department can propose the optimal flow based on information such as the industry being "restaurant," the business type being "cafe," the planned opening area being "Shibuya Ward, Tokyo," and the initial capital being "10 million yen." The proposal department can propose, for example, concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit applications, interior construction and equipment preparation, staff recruitment and training, and promotional activities and information dissemination. In this way, the proposal department can streamline the opening preparation process by proposing the optimal flow based on the user's information.

[0036] The instruction department can direct specific actions based on the flow proposed by the proposal department, such as obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination. For example, regarding obtaining qualifications and applying for permits, the instruction department can direct specific actions such as obtaining qualifications as a food hygiene manager and fire safety manager, and obtaining necessary permits from the public health center and fire department. For example, regarding interior construction and equipment preparation, the instruction department can direct specific actions such as carrying out interior construction of the store and acquiring necessary kitchen equipment and seating fixtures. For example, regarding sales promotion activities and information dissemination, the instruction department can direct specific actions such as conducting promotional activities to increase awareness of the store through flyers and social media. In this way, the instruction department directs specific actions, allowing users to efficiently proceed with opening preparations.

[0037] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can automatically complete information previously entered by the user, simplifying the input process. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows for more efficient input by selecting the optimal input method based on the user's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0038] The reception desk can customize input fields based on the user's current situation and areas of interest when information is entered. For example, if the user wishes to open a restaurant, the reception desk can prioritize displaying input fields related to restaurants. For example, if the user wishes to open a business in a specific area, the reception desk can display information related to that area as input fields. For example, if the user wishes to open a business within a specific budget, the reception desk can display input fields based on that budget. This makes the input process more efficient by customizing input fields according to the user's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current situation data into a generating AI and have the generating AI perform the customization of input fields.

[0039] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when data is entered. For example, if a user wishes to open a business in a specific region, the reception desk can prioritize inputting information related to that region. For example, if a user wishes to open a business in an area close to their current location, the reception desk can prioritize inputting information related to that area. For example, if a user wishes to open a business in a specific city, the reception desk can prioritize inputting information related to that city. This makes the data entry process more efficient by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0040] The reception desk can analyze the user's social media activity and input relevant information when data is entered. For example, the reception desk can suggest relevant input fields based on information the user has shared on social media. For example, the reception desk can suggest relevant input fields based on information about accounts the user follows on social media. For example, the reception desk can suggest relevant input fields based on information about groups the user participates in on social media. This makes the data entry process more efficient by inputting relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0041] The proposal unit can adjust the level of detail in its proposals based on the importance of the business opening. For example, if the business opening is highly important, the proposal unit can provide a detailed proposal. If the business opening is of moderate importance, the proposal unit can provide a concise proposal. If the business opening is of low importance, the proposal unit can provide a brief proposal. By adjusting the level of detail in the proposals according to the importance of the business opening, the proposal unit can provide the most suitable proposal for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input business opening importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0042] The proposal unit can apply different proposal algorithms depending on the industry and business type when making proposals. For example, in the case of a restaurant, the proposal unit can propose menu development and kitchen equipment. For example, in the case of a retail store, the proposal unit can propose product display and inventory management. For example, in the case of a service industry, the proposal unit can propose service content and staff training. By applying a proposal algorithm tailored to the industry and business type, the unit can provide the most suitable proposals for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input industry and business type data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0043] The proposal department can determine the priority of proposals based on the characteristics of the planned development area. For example, if the planned development area is a commercial area, the proposal department can prioritize proposals for attracting customers and location conditions. If the planned development area is a residential area, the proposal department can prioritize proposals for services and promotions aimed at local residents. If the planned development area is a tourist area, the proposal department can prioritize proposals for services and promotions aimed at tourists. By determining the priority of proposals based on the characteristics of the planned development area, the proposal department can provide the most suitable proposal for the user. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input characteristic data of the planned development area into a generating AI and have the generating AI determine the priority of proposals.

[0044] The proposal department can adjust the order of proposals based on the status of available funds. For example, if there are ample funds, the proposal department can prioritize interior construction and equipment preparation. If funds are limited, the proposal department can prioritize low-cost and effective promotional activities. If funds are insufficient, the proposal department can prioritize proposals for fundraising and loans. By adjusting the order of proposals based on the status of available funds, the proposal department can provide the most suitable proposal for the user. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input data on the status of available funds into a generating AI and have the generating AI adjust the order of proposals.

[0045] The instruction unit can analyze the user's past behavioral history and select the optimal instruction method when issuing instructions. For example, the instruction unit can provide optimal instructions based on the user's past successful behavioral patterns. For example, the instruction unit can provide instructions to avoid the user's past unsuccessful behavioral patterns. For example, the instruction unit can provide optimal instructions for a specific time period based on the user's past behavioral history. This improves the accuracy of instructions by selecting the optimal instruction method based on the user's past behavioral history. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal instruction method.

[0046] The instruction unit can customize the instructions based on the user's current situation when issuing action instructions. For example, the instruction unit can provide the most appropriate action instructions according to the user's current situation. For example, if the user is in a specific situation, the instruction unit can provide instructions appropriate to that situation. For example, the instruction unit can provide the most appropriate action instructions based on the user's current situation. This allows for improved accuracy of action instructions by customizing the instructions according to the user's current situation. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the instructions.

[0047] The instruction unit can select the optimal instruction method when issuing instructions, taking into account the user's geographical location information. For example, if the user is in a specific region, the instruction unit can issue instructions related to that region. For example, if the user is in an area close to their current location, the instruction unit can issue instructions related to that area. For example, if the user is in a specific city, the instruction unit can issue instructions related to that city. By selecting the optimal instruction method while considering the user's geographical location information, the accuracy of the instructions can be improved. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal instruction method.

[0048] The instruction unit can analyze the user's social media activity and propose instructions when issuing action instructions. For example, the instruction unit can issue relevant action instructions based on information shared by the user on social media. For example, the instruction unit can issue relevant action instructions based on information about accounts the user follows on social media. For example, the instruction unit can issue relevant action instructions based on information about groups the user participates in on social media. This improves the accuracy of action instructions by providing relevant instructions based on the user's social media activity. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's social media data into a generating AI and have the generating AI execute the suggestion of action content.

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

[0050] The suggestion unit can analyze the user's past behavioral history and make optimal suggestions. For example, the suggestion unit can make similar suggestions based on the steps the user successfully took to prepare for opening a business in the past. For example, the suggestion unit can make suggestions to help the user avoid steps that they have failed at in the past. For example, the suggestion unit can make optimal suggestions for a specific time period based on the user's past behavioral history. This improves the accuracy of suggestions by making optimal suggestions based on the user's past behavioral history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal suggestion.

[0051] The proposal unit can make highly relevant suggestions by considering the user's geographical location information when making suggestions. For example, if the user wishes to open a business in a specific region, the proposal unit can prioritize suggestions related to that region. For example, if the user wishes to open a business in an area close to their current location, the proposal unit can prioritize suggestions related to that area. For example, if the user wishes to open a business in a specific city, the proposal unit can prioritize suggestions related to that city. By considering the user's geographical location information and making highly relevant suggestions, the accuracy of the suggestions can be improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant suggestions.

[0052] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on information the user has shared on social media. For example, the suggestion unit can make relevant suggestions based on information about accounts the user follows on social media. For example, the suggestion unit can make relevant suggestions based on information about groups the user participates in on social media. This improves the accuracy of suggestions by making relevant suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI select relevant suggestions.

[0053] The instruction unit can analyze the user's past behavioral history and select the optimal instruction method when issuing instructions. For example, the instruction unit can provide optimal instructions based on the user's past successful behavioral patterns. For example, the instruction unit can provide instructions to avoid the user's past unsuccessful behavioral patterns. For example, the instruction unit can provide optimal instructions for a specific time period based on the user's past behavioral history. This improves the accuracy of instructions by selecting the optimal instruction method based on the user's past behavioral history. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal instruction method.

[0054] The instruction unit can customize the instructions based on the user's current situation when issuing action instructions. For example, the instruction unit can provide the most appropriate action instructions according to the user's current situation. For example, if the user is in a specific situation, the instruction unit can provide instructions appropriate to that situation. For example, the instruction unit can provide the most appropriate action instructions based on the user's current situation. This allows for improved accuracy of action instructions by customizing the instructions according to the user's current situation. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the instructions.

[0055] The instruction unit can select the optimal instruction method when issuing instructions, taking into account the user's geographical location information. For example, if the user is in a specific region, the instruction unit can issue instructions related to that region. For example, if the user is in an area close to their current location, the instruction unit can issue instructions related to that area. For example, if the user is in a specific city, the instruction unit can issue instructions related to that city. By selecting the optimal instruction method while considering the user's geographical location information, the accuracy of the instructions can be improved. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal instruction method.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives information from users regarding their industry, business type, planned location for opening, and initial capital. For example, if a user is opening a restaurant, the reception desk can receive information such as "Restaurant" as the industry, "Cafe" as the business type, "Shibuya Ward, Tokyo" as the planned location, and "10 million yen" as the initial capital. Step 2: The proposal department analyzes the information received by the reception department and proposes the optimal flow from opening to operational commencement. For example, they can propose concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit applications, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. Step 3: The instruction team directs specific actions based on the flow proposed by the proposal team. For example, they can direct specific actions such as property search, menu development, staff recruitment and training. Furthermore, they can direct specific actions such as obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination.

[0058] (Example of form 2) The business startup support system according to an embodiment of the present invention is an AI-powered service designed to streamline many of the tasks required for business startup. This system allows users to input information such as industry, business type, planned location, and startup capital. The AI ​​analyzes this information and proposes an optimal flow from startup to operation. The proposal includes concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit applications, interior construction and equipment preparation, staff recruitment and training, and promotional activities and information dissemination. This streamlines tasks that typically consume a significant amount of time during the startup phase. For example, if a user is opening a restaurant, they would input information such as "restaurant" as the industry, "cafe" as the business type, "Shibuya-ku, Tokyo" as the planned location, and "10 million yen" as the startup capital. This information is then input into the AI. Next, the AI ​​analyzes the input information and proposes an optimal flow from startup to operation. For example, in the case of opening a restaurant, the AI ​​proposes the following flow: First, as concept design, the industry, business type, target audience, and strengths of the restaurant are clarified. Next, as business plan formulation, a concrete business plan is created, and preparations for fundraising are made. This is also necessary for applying for loans and subsidies. Furthermore, regarding property search and contract, you will need to find a suitable property for the store and sign a contract. It is important to choose carefully, taking into account rent and location conditions. In addition, as part of menu development, you will devise the menu to be offered and prepare the kitchen equipment and supplies accordingly. As part of obtaining qualifications and permits, you will obtain qualifications such as food hygiene manager and fire safety manager, and obtain the necessary permits from the public health center and fire department. As part of interior construction and equipment preparation, you will carry out interior construction of the store and prepare the necessary kitchen equipment and seating area furnishings. As part of staff recruitment and training, you will recruit and train staff before opening. Recruitment should ideally be done one month before opening. As part of sales promotion and information dissemination, you will conduct promotional activities to increase awareness of the store through flyers, social media, etc. In this way, by proceeding with opening preparations according to the flow suggested by AI, it is possible to streamline tasks that take up a lot of time when opening a business. For example, by following the property search flow suggested by AI, you can find a suitable property in a short period of time. Also, by following the menu development flow suggested by AI, you can efficiently devise a menu and prepare the necessary equipment.Furthermore, by following the staff recruitment and training process suggested by the AI, it is possible to recruit the right staff and conduct effective training. This system makes it possible to streamline many of the tasks required for opening a business, allowing entrepreneurs to proceed with the opening more smoothly. For example, in the preparation for opening a restaurant, following the process suggested by the AI ​​can shorten the time until opening and allow preparations to proceed efficiently. Also, by following the process suggested by the AI, the costs associated with opening a business can be reduced and funds can be managed efficiently. As a result, entrepreneurs can proceed with the opening more smoothly and increase their chances of success. In short, the business preparation support system enables entrepreneurs to proceed with preparations efficiently and increases the success rate of their business.

[0059] The business startup support system according to this embodiment comprises a reception unit, a proposal unit, and an instruction unit. The reception unit receives information from the user regarding the type of business, business format, planned business area, and startup capital. For example, if the user is opening a restaurant, the reception unit can receive information such as "restaurant" as the type of business, "cafe" as the business format, "Shibuya Ward, Tokyo" as the planned business area, and "10 million yen" as the startup capital. The proposal unit analyzes the information received by the reception unit and proposes the optimal flow from opening to operation. For example, the proposal unit can propose concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit application, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. For example, in the case of opening a restaurant, the proposal unit can propose concept design that clarifies the type of business, business format, target audience, and strengths of the store. The proposal unit can also propose the formulation of a business plan that creates a concrete business plan and prepares for fundraising. Furthermore, the proposal unit can propose property search and contract for a suitable store. The proposal department can propose menu development, including devising menus to be offered and preparing corresponding kitchen equipment and supplies. The proposal department can propose obtaining qualifications such as food hygiene manager and fire safety manager, and applying for necessary permits and licenses from public health centers and fire departments. The proposal department can propose interior construction and equipment preparation, including carrying out interior construction of the store and equipping it with necessary kitchen equipment and seating fixtures. The proposal department can propose staff recruitment and training, including hiring and training staff before opening. The proposal department can propose sales promotion activities and information dissemination, including promotional activities to increase awareness of the store through flyers and social media. The instruction department provides specific instructions based on the flow proposed by the proposal department. For example, the instruction department can provide instructions for specific actions such as property search, menu development, staff recruitment and training. The instruction department can provide instructions for specific actions such as obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination. In this way, the business opening preparation support system according to the embodiment can streamline business opening preparations by proposing the optimal flow from opening to operation start based on user information and providing instructions for specific actions.

[0060] The reception desk receives information from users regarding their industry, business type, planned opening area, and initial capital. Specifically, users access the system and provide the necessary information through a dedicated input form. For example, if a user is opening a restaurant, they would enter information such as "restaurant" as the industry, "cafe" as the business type, "Shibuya Ward, Tokyo" as the planned opening area, and "10 million yen" as the initial capital. The reception desk stores this information in a database and uses it for subsequent processing. Furthermore, the reception desk has a function to check the consistency of the information entered by the user and prompt the user to correct or add any missing or inaccurate information. For example, if the planned opening area is not specific, the system will instruct the user to enter a specific area name. The reception desk can also provide initial feedback based on the information entered by the user. For example, if the initial capital is insufficient, the system will suggest methods of fundraising or budget revisions to the user. This allows the reception desk to efficiently collect information from users and prepare the data necessary for subsequent suggestions and instructions. Furthermore, the reception department has implemented security measures to safely manage user information, including data encryption and access control to protect user privacy.

[0061] The Proposal Department analyzes the information received by the Reception Department and proposes the optimal flow from opening to operational commencement. Specifically, the Proposal Department uses AI to generate an optimal opening plan based on information provided by the user, such as the type of business, business model, planned opening area, and preparation funds. For example, in the case of opening a restaurant, the Proposal Department can propose a concept design that clarifies the type of business, business model, target audience, and strengths of the store. The AI ​​analyzes past success stories and market data to propose the optimal concept for the user. The Proposal Department can also propose the development of a business plan that includes creating a concrete business plan and preparing for fundraising. The AI ​​generates a realistic business plan considering the user's preparation funds and market conditions. Furthermore, the Proposal Department can propose the search and contracting of a property suitable for the store. The AI ​​analyzes real estate data of the planned opening area and proposes the optimal property for the user. The Proposal Department can propose menu development, including devising the menu to be offered and preparing the corresponding kitchen equipment and supplies. The AI ​​analyzes trends and the menus of competing restaurants to propose the optimal menu for the user. The proposal department can propose obtaining qualifications such as food hygiene manager and fire safety manager certifications, and applying for necessary permits and licenses from public health centers and fire departments. The AI ​​automates the procedures for obtaining necessary qualifications and licenses, and provides users with specific steps. The proposal department can propose interior construction and equipment preparation, including the installation of necessary kitchen equipment and seating area furnishings. The AI ​​optimizes the interior design and equipment layout, and provides users with a concrete plan. The proposal department can propose staff recruitment and training, including hiring and training staff before opening. The AI ​​analyzes job postings and provides advice to find the most suitable staff. The proposal department can propose promotional activities and information dissemination, such as flyers and social media, to increase awareness of the store. The AI ​​generates effective promotional strategies and provides users with concrete action plans. In this way, the proposal department can provide users with a comprehensive and specific opening plan, streamlining the opening preparations.

[0062] The Instruction Department directs specific actions based on the flow proposed by the Proposal Department. Specifically, the Instruction Department can direct specific actions for property search, menu development, and staff recruitment and training. For example, in property search, it provides the user with a list of specific properties and coordinates viewing schedules. In menu development, it directs the user to create prototype menus and hold tasting sessions. In staff recruitment and training, it directs the creation of job advertisements, scheduling interviews, and implementing training programs. The Instruction Department can also direct specific actions for obtaining qualifications and applying for licenses, interior construction and equipment preparation, and sales promotion activities and information dissemination. For example, in obtaining qualifications and applying for licenses, it directs the user to prepare and submit the necessary documents. In interior construction and equipment preparation, it directs meetings with contractors and management of construction progress. In sales promotion activities and information dissemination, it directs the design and distribution of flyers and the timing and content of information dissemination on social media. Furthermore, the Instruction Department monitors the user's progress in real time and provides additional instructions and support as needed. For example, if things are not progressing as planned or problems arise, the instruction department will quickly propose solutions and provide specific instructions to the user. In this way, the instruction department can support the user in smoothly preparing for the opening of their business and help ensure its success.

[0063] The proposal department can propose concept design, business plan formulation, property search and contract, menu development, qualification acquisition and license application, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. For example, as part of concept design, the proposal department can clarify the type of restaurant to be opened, business model, target audience, and strengths of the store. For example, as part of business plan formulation, the proposal department can create a concrete business plan and prepare for fundraising. For example, as part of property search and contract, the proposal department can find a suitable property for the store and conclude a contract. For example, as part of menu development, the proposal department can devise a menu to be offered and prepare the corresponding kitchen equipment and supplies. For example, as part of qualification acquisition and license application, the proposal department can acquire qualifications such as food hygiene manager and fire safety manager and obtain the necessary licenses from the public health center and fire department. For example, as part of interior construction and equipment preparation, the proposal department can carry out interior construction of the store and procure the necessary kitchen equipment and seating area furnishings. For example, as part of staff recruitment and training, the proposal department can recruit staff and conduct training before opening. The proposal department can, for example, conduct promotional activities to increase store awareness through flyers, social media, and other means as part of sales promotion and information dissemination. This allows users to efficiently proceed with their store opening preparations by having the proposal department suggest specific steps for each stage of preparation.

[0064] The instruction department can direct specific actions such as property search, menu development, and staff recruitment and training, based on the flow proposed by the proposal department. For example, the instruction department can direct specific actions such as finding a suitable property for the store and signing a contract. For example, the instruction department can direct specific actions such as devising a menu to be offered and preparing the corresponding kitchen equipment and supplies for menu development. For example, the instruction department can direct specific actions such as recruiting and training staff before opening. In this way, the instruction department directs specific actions, allowing users to efficiently proceed with opening preparations.

[0065] The proposal department can propose the optimal flow from opening to operation based on the industry, business type, planned opening area, and initial capital. For example, if opening a restaurant, the proposal department can propose the optimal flow based on information such as the industry being "restaurant," the business type being "cafe," the planned opening area being "Shibuya Ward, Tokyo," and the initial capital being "10 million yen." The proposal department can propose, for example, concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit applications, interior construction and equipment preparation, staff recruitment and training, and promotional activities and information dissemination. In this way, the proposal department can streamline the opening preparation process by proposing the optimal flow based on the user's information.

[0066] The instruction department can direct specific actions based on the flow proposed by the proposal department, such as obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination. For example, regarding obtaining qualifications and applying for permits, the instruction department can direct specific actions such as obtaining qualifications as a food hygiene manager and fire safety manager, and obtaining necessary permits from the public health center and fire department. For example, regarding interior construction and equipment preparation, the instruction department can direct specific actions such as carrying out interior construction of the store and acquiring necessary kitchen equipment and seating fixtures. For example, regarding sales promotion activities and information dissemination, the instruction department can direct specific actions such as conducting promotional activities to increase awareness of the store through flyers and social media. In this way, the instruction department directs specific actions, allowing users to efficiently proceed with opening preparations.

[0067] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception unit can pause input and display a relaxing interface. For example, if the user is focused, the reception unit can adjust the interface to allow continuous information input. For example, if the user is tired, the reception unit can simplify input and request only the minimum necessary information. This reduces the user's burden by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can automatically complete information previously entered by the user, simplifying the input process. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows for more efficient input by selecting the optimal input method based on the user's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0069] The reception desk can customize input fields based on the user's current situation and areas of interest when information is entered. For example, if the user wishes to open a restaurant, the reception desk can prioritize displaying input fields related to restaurants. For example, if the user wishes to open a business in a specific area, the reception desk can display information related to that area as input fields. For example, if the user wishes to open a business within a specific budget, the reception desk can display input fields based on that budget. This makes the input process more efficient by customizing input fields according to the user's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current situation data into a generating AI and have the generating AI perform the customization of input fields.

[0070] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk can prioritize the input of important information and allow for details to be added later. For example, if the user is relaxed, the reception desk can allow for the input of detailed information first. For example, if the user is in a hurry, the reception desk can prioritize the input of only the most important information. This allows for the priority of input of important information by determining the priority of the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when data is entered. For example, if a user wishes to open a business in a specific region, the reception desk can prioritize inputting information related to that region. For example, if a user wishes to open a business in an area close to their current location, the reception desk can prioritize inputting information related to that area. For example, if a user wishes to open a business in a specific city, the reception desk can prioritize inputting information related to that city. This makes the data entry process more efficient by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0072] The reception desk can analyze the user's social media activity and input relevant information when data is entered. For example, the reception desk can suggest relevant input fields based on information the user has shared on social media. For example, the reception desk can suggest relevant input fields based on information about accounts the user follows on social media. For example, the reception desk can suggest relevant input fields based on information about groups the user participates in on social media. This makes the data entry process more efficient by inputting relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0073] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can use a simple and easy-to-understand presentation. For example, if the user is relaxed, the suggestion unit can use a presentation that includes detailed information. For example, if the user is in a hurry, the suggestion unit can use a concise presentation that gets straight to the point. By adjusting the presentation of the suggestion according to the user's emotions, the suggestion can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The proposal unit can adjust the level of detail in its proposals based on the importance of the business opening. For example, if the business opening is highly important, the proposal unit can provide a detailed proposal. If the business opening is of moderate importance, the proposal unit can provide a concise proposal. If the business opening is of low importance, the proposal unit can provide a brief proposal. By adjusting the level of detail in the proposals according to the importance of the business opening, the proposal unit can provide the most suitable proposal for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input business opening importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0075] The proposal unit can apply different proposal algorithms depending on the industry and business type when making proposals. For example, in the case of a restaurant, the proposal unit can propose menu development and kitchen equipment. For example, in the case of a retail store, the proposal unit can propose product display and inventory management. For example, in the case of a service industry, the proposal unit can propose service content and staff training. By applying a proposal algorithm tailored to the industry and business type, the unit can provide the most suitable proposals for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input industry and business type data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0076] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the suggestion unit can provide the most suitable suggestion for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The proposal department can determine the priority of proposals based on the characteristics of the planned development area. For example, if the planned development area is a commercial area, the proposal department can prioritize proposals for attracting customers and location conditions. If the planned development area is a residential area, the proposal department can prioritize proposals for services and promotions aimed at local residents. If the planned development area is a tourist area, the proposal department can prioritize proposals for services and promotions aimed at tourists. By determining the priority of proposals based on the characteristics of the planned development area, the proposal department can provide the most suitable proposal for the user. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input characteristic data of the planned development area into a generating AI and have the generating AI determine the priority of proposals.

[0078] The proposal department can adjust the order of proposals based on the status of available funds. For example, if there are ample funds, the proposal department can prioritize interior construction and equipment preparation. If funds are limited, the proposal department can prioritize low-cost and effective promotional activities. If funds are insufficient, the proposal department can prioritize proposals for fundraising and loans. By adjusting the order of proposals based on the status of available funds, the proposal department can provide the most suitable proposal for the user. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input data on the status of available funds into a generating AI and have the generating AI adjust the order of proposals.

[0079] The instruction unit can estimate the user's emotions and adjust the method of giving instructions based on the estimated emotions. For example, if the user is nervous, the instruction unit can give simple and easy-to-understand instructions. For example, if the user is relaxed, the instruction unit can give instructions that include detailed information. For example, if the user is in a hurry, the instruction unit can give concise instructions that get straight to the point. In this way, by adjusting the method of giving instructions according to the user's emotions, instructions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The instruction unit can analyze the user's past behavioral history and select the optimal instruction method when issuing instructions. For example, the instruction unit can provide optimal instructions based on the user's past successful behavioral patterns. For example, the instruction unit can provide instructions to avoid the user's past unsuccessful behavioral patterns. For example, the instruction unit can provide optimal instructions for a specific time period based on the user's past behavioral history. This improves the accuracy of instructions by selecting the optimal instruction method based on the user's past behavioral history. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal instruction method.

[0081] The instruction unit can customize the instructions based on the user's current situation when issuing action instructions. For example, the instruction unit can provide the most appropriate action instructions according to the user's current situation. For example, if the user is in a specific situation, the instruction unit can provide instructions appropriate to that situation. For example, the instruction unit can provide the most appropriate action instructions based on the user's current situation. This allows for improved accuracy of action instructions by customizing the instructions according to the user's current situation. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the instructions.

[0082] The instruction unit can estimate the user's emotions and determine the priority of action instructions based on the estimated emotions. For example, if the user is tense, the instruction unit can prioritize important action instructions. For example, if the user is relaxed, the instruction unit can prioritize detailed action instructions. For example, if the user is in a hurry, the instruction unit can prioritize the most important action instructions. In this way, by determining the priority of action instructions according to the user's emotions, important actions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The instruction unit can select the optimal instruction method when issuing instructions, taking into account the user's geographical location information. For example, if the user is in a specific region, the instruction unit can issue instructions related to that region. For example, if the user is in an area close to their current location, the instruction unit can issue instructions related to that area. For example, if the user is in a specific city, the instruction unit can issue instructions related to that city. By selecting the optimal instruction method while considering the user's geographical location information, the accuracy of the instructions can be improved. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal instruction method.

[0084] The instruction unit can analyze the user's social media activity and propose instructions when issuing action instructions. For example, the instruction unit can issue relevant action instructions based on information shared by the user on social media. For example, the instruction unit can issue relevant action instructions based on information about accounts the user follows on social media. For example, the instruction unit can issue relevant action instructions based on information about groups the user participates in on social media. This improves the accuracy of action instructions by providing relevant instructions based on the user's social media activity. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's social media data into a generating AI and have the generating AI execute the suggestion of action content.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The suggestion unit can estimate the user's emotions and adjust the priority of suggestions based on the estimated emotions. For example, if the user is feeling anxious, the suggestion unit can prioritize suggestions that provide reassurance. For example, if the user is excited, the suggestion unit can prioritize suggesting concrete action plans. For example, if the user is tired, the suggestion unit can prioritize simple and easy-to-implement suggestions. In this way, by adjusting the priority of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The suggestion unit can analyze the user's past behavioral history and make optimal suggestions. For example, the suggestion unit can make similar suggestions based on the steps the user successfully took to prepare for opening a business in the past. For example, the suggestion unit can make suggestions to help the user avoid steps that they have failed at in the past. For example, the suggestion unit can make optimal suggestions for a specific time period based on the user's past behavioral history. This improves the accuracy of suggestions by making optimal suggestions based on the user's past behavioral history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal suggestion.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can use a simple and easy-to-understand presentation. For example, if the user is relaxed, the suggestion unit can use a presentation that includes detailed information. For example, if the user is in a hurry, the suggestion unit can use a concise presentation that gets straight to the point. By adjusting the presentation of the suggestion according to the user's emotions, the suggestion can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The proposal unit can make highly relevant suggestions by considering the user's geographical location information when making suggestions. For example, if the user wishes to open a business in a specific region, the proposal unit can prioritize suggestions related to that region. For example, if the user wishes to open a business in an area close to their current location, the proposal unit can prioritize suggestions related to that area. For example, if the user wishes to open a business in a specific city, the proposal unit can prioritize suggestions related to that city. By considering the user's geographical location information and making highly relevant suggestions, the accuracy of the suggestions can be improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant suggestions.

[0090] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on information the user has shared on social media. For example, the suggestion unit can make relevant suggestions based on information about accounts the user follows on social media. For example, the suggestion unit can make relevant suggestions based on information about groups the user participates in on social media. This improves the accuracy of suggestions by making relevant suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI select relevant suggestions.

[0091] The instruction unit can estimate the user's emotions and adjust the method of giving instructions based on the estimated emotions. For example, if the user is nervous, the instruction unit can give simple and easy-to-understand instructions. For example, if the user is relaxed, the instruction unit can give instructions that include detailed information. For example, if the user is in a hurry, the instruction unit can give concise instructions that get straight to the point. In this way, by adjusting the method of giving instructions according to the user's emotions, instructions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The instruction unit can analyze the user's past behavioral history and select the optimal instruction method when issuing instructions. For example, the instruction unit can provide optimal instructions based on the user's past successful behavioral patterns. For example, the instruction unit can provide instructions to avoid the user's past unsuccessful behavioral patterns. For example, the instruction unit can provide optimal instructions for a specific time period based on the user's past behavioral history. This improves the accuracy of instructions by selecting the optimal instruction method based on the user's past behavioral history. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal instruction method.

[0093] The instruction unit can customize the instructions based on the user's current situation when issuing action instructions. For example, the instruction unit can provide the most appropriate action instructions according to the user's current situation. For example, if the user is in a specific situation, the instruction unit can provide instructions appropriate to that situation. For example, the instruction unit can provide the most appropriate action instructions based on the user's current situation. This allows for improved accuracy of action instructions by customizing the instructions according to the user's current situation. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the instructions.

[0094] The instruction unit can estimate the user's emotions and determine the priority of action instructions based on the estimated emotions. For example, if the user is tense, the instruction unit can prioritize important action instructions. For example, if the user is relaxed, the instruction unit can prioritize detailed action instructions. For example, if the user is in a hurry, the instruction unit can prioritize the most important action instructions. In this way, by determining the priority of action instructions according to the user's emotions, important actions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The instruction unit can select the optimal instruction method when issuing instructions, taking into account the user's geographical location information. For example, if the user is in a specific region, the instruction unit can issue instructions related to that region. For example, if the user is in an area close to their current location, the instruction unit can issue instructions related to that area. For example, if the user is in a specific city, the instruction unit can issue instructions related to that city. By selecting the optimal instruction method while considering the user's geographical location information, the accuracy of the instructions can be improved. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal instruction method.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception desk receives information from users regarding their industry, business type, planned location for opening, and initial capital. For example, if a user is opening a restaurant, the reception desk can receive information such as "Restaurant" as the industry, "Cafe" as the business type, "Shibuya Ward, Tokyo" as the planned location, and "10 million yen" as the initial capital. Step 2: The proposal department analyzes the information received by the reception department and proposes the optimal flow from opening to operational commencement. For example, they can propose concept design, business plan formulation, property search and contract, menu development, qualification acquisition and permit applications, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. Step 3: The instruction team directs specific actions based on the flow proposed by the proposal team. For example, they can direct specific actions such as property search, menu development, staff recruitment and training. Furthermore, they can direct specific actions such as obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination.

[0098] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the reception unit, proposal unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information from the user regarding the type of business, business model, planned opening area, and preparation funds. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to propose the optimal flow from opening to operation commencement. The instruction unit is implemented by the control unit 46A of the smart device 14 and instructs specific actions based on the flow proposed by the proposal unit. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 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.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, proposal unit, and instruction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information from the user regarding the type of business, business model, planned opening area, and preparation funds. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to propose the optimal flow from opening to operation commencement. The instruction unit is implemented by the control unit 46A of the smart glasses 214 and instructs specific actions based on the flow proposed by the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, proposal unit, and instruction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information from the user regarding the type of business, business model, planned opening area, and preparation funds. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the information received by the reception unit and proposes the optimal flow from opening to operation commencement. The instruction unit is implemented by, for example, the control unit 46A of the headset terminal 314 and instructs specific actions based on the flow proposed by the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, proposal unit, and instruction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information from the user regarding the type of business, business model, planned opening area, and preparation funds. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the information received by the reception unit and proposes the optimal flow from opening to operation commencement. The instruction unit is implemented by, for example, the control unit 46A of the robot 414 and instructs specific actions based on the flow proposed by the proposal unit. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0151] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A reception desk that receives information from users regarding their industry, business type, planned business area, and startup capital, The proposal department analyzes the information received by the aforementioned reception department and proposes the optimal flow from opening to commencement of operations, The system includes an instruction unit that provides instructions for specific actions based on the flow proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose concept design, business plan development, property search and contracting, menu development, qualification acquisition and license application, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. The system described in Appendix 1, characterized by the features described herein. (Note 3) The indicator unit is, Based on the proposed process by the aforementioned proposal department, specific actions such as property search, menu development, and staff recruitment and training will be instructed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on your industry, business type, planned location, and initial capital, we will propose the optimal flow from opening to operational commencement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The indicator unit is, Based on the process proposed by the aforementioned proposal department, specific actions will be instructed regarding obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering information, input fields are customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the business opening. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the industry and business type. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting proposals, prioritize them based on the characteristics of the area where the facility is planned to open. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting proposals, adjust the order of proposals based on the status of available funds. The system described in Appendix 1, characterized by the features described herein. (Note 18) The indicator unit is, It estimates the user's emotions and adjusts the method of giving instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The indicator unit is, When issuing instructions, the system analyzes the user's past behavioral history to select the most appropriate instruction method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, When issuing action instructions, customize the instructions based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, It estimates the user's emotions and determines the priority of action instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, When issuing instructions, the system selects the optimal instruction method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, When issuing instructions, the system analyzes the user's social media activity to suggest appropriate instructions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users regarding their industry, business type, planned business area, and startup capital, The proposal department analyzes the information received by the aforementioned reception department and proposes the optimal flow from opening to commencement of operations, The system includes an instruction unit that provides instructions for specific actions based on the flow proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose concept design, business plan development, property search and contracting, menu development, qualification acquisition and license application, interior construction and equipment preparation, staff recruitment and training, and sales promotion activities and information dissemination. The system according to feature 1.

3. The indicator unit is, Based on the proposed process by the aforementioned proposal department, specific actions such as property search, menu development, and staff recruitment and training will be instructed. The system according to feature 1.

4. The aforementioned proposal section is, Based on your industry, business type, planned location, and initial capital, we will propose the optimal flow from opening to operational commencement. The system according to feature 1.

5. The indicator unit is, Based on the process proposed by the aforementioned proposal department, specific actions will be instructed regarding obtaining qualifications and applying for permits, interior construction and equipment preparation, and sales promotion activities and information dissemination. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering information, input fields are customized based on the user's current situation and areas of interest. The system according to feature 1.

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