system

Abstract

The system according to the embodiment aims to evaluate a business plan and suggest improvements when starting a new business. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, an evaluating unit, and a proposing unit. The receiving unit receives input of a business idea. The generating unit analyzes the business idea received by the receiving unit and generates a business plan. The evaluating unit evaluates the business plan generated by the generating unit. The proposing unit proposes improvements to the business plan based on the results of the evaluation by the evaluating unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to evaluate business plans and identify areas for improvement when starting a new business.

[0005] The system according to the embodiment aims to evaluate a business plan and suggest improvements when starting a new business. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, an evaluating unit, and a proposing unit. The receiving unit receives input of a business idea. The generating unit analyzes the business idea received by the receiving unit and generates a business plan. The evaluating unit evaluates the business plan generated by the generating unit. The proposing unit proposes improvements to the business plan based on the results of the evaluation by the evaluating unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate a business plan and suggest improvements when starting a new business. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business advisor system according to an embodiment of the present invention utilizes a generative AI as a business advisor for people aspiring to start a new business. It assists them in developing a business plan from a business idea seed, objectively evaluates the completed business plan, and suggests improvements for further improvement. This business advisor system begins with a user inputting a business idea seed. The generative AI analyzes the idea and generates the outline of a business plan. For example, if a user inputs "I want to create an online education platform," the generative AI performs market analysis, competitive analysis, identifies target users, proposes a revenue model, and so on, to create the outline of the business plan. The generative AI then objectively evaluates the created business plan. This evaluation includes factors such as the business's likelihood of success, risk factors, and competitive advantages. For example, the generative AI analyzes market growth potential and the strength of competitors to clarify the strengths and weaknesses of the business plan. Furthermore, the generative AI suggests improvements to the business plan. For example, it suggests specific improvements such as revising the revenue model, redefining the target users, and strengthening the marketing strategy. This allows users to further solidify their business plans. With this system, people aspiring to start a new business can, with the assistance of a generative AI, transform their business idea into a concrete business plan, receiving an objective evaluation and suggestions for improvement. This increases the business's chances of success and minimizes risk. For example, a user inputs the seed of a business idea, such as "I want to create an online education platform." This information is then entered into the generative AI. The generative AI then analyzes the input information and generates the outline of a business plan. The generative AI then performs market analysis, competitive analysis, identifies target users, proposes revenue models, and other processes to create the outline of the business plan. For example, if a user inputs "I want to create an online education platform," the generative AI analyzes market growth potential and competitive strength to clarify the strengths and weaknesses of the business plan. The generated business plan is then objectively evaluated by the generative AI.The evaluation includes the business's likelihood of success, risk factors, and competitive advantages. For example, the generative AI analyzes market growth potential and the strength of competition to clarify the strengths and weaknesses of the business plan. Furthermore, the generative AI suggests improvements to the business plan. For example, it suggests specific improvements such as revising the revenue model, redefining target users, and strengthening the marketing strategy. This allows users to further strengthen their own business plans. With this system, people aspiring to start a new business can, with the assistance of the generative AI, develop their business ideas into concrete business plans and receive objective evaluations and suggestions for improvements. This increases the business's chances of success and minimizes risks. The business advisor system can then develop users' business ideas into concrete business plans and provide objective evaluations and suggestions for improvements.

[0029] A business advisor system according to an embodiment includes a reception unit, a generation unit, an evaluation unit, and a proposal unit. The reception unit receives a business idea input by a user. Examples of business ideas include, but are not limited to, new business ideas, product improvement ideas, and marketing strategy ideas. The reception unit receives the business idea input by the user in text format, for example. The reception unit can also support multiple input formats, such as voice input and image input. For example, the reception unit can convert a business idea dictated by a user into text data using voice recognition technology. The reception unit can also convert a handwritten business idea into digital data using image recognition technology. The generation unit uses a generation AI to analyze the business idea received by the reception unit and generate a business plan outline. The generation unit performs, for example, market analysis, competitive analysis, target user identification, and revenue model proposal. The generation AI analyzes the business idea and generates a business plan outline using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes market growth potential and competitive strength based on a business idea input by a user, and clarifies the strengths and weaknesses of the business plan. The evaluation unit objectively evaluates the business plan generated by the generation unit. The evaluation unit evaluates, for example, the business's likelihood of success, risk factors, competitive advantage, etc. The evaluation unit uses the generation AI to evaluate each element of the business plan and provide a comprehensive evaluation result. For example, the evaluation unit uses the generation AI to analyze market growth potential and competitive strength, and clarifies the strengths and weaknesses of the business plan. The proposal unit proposes improvements to the business plan based on the results of the evaluation by the evaluation unit. The proposal unit presents specific improvements, such as reviewing the revenue model, redefining the target users, and strengthening the marketing strategy. The proposal unit uses the generation AI to make specific proposals for improving each element of the business plan. For example, the proposal unit uses the generation AI to present specific improvements, such as reviewing the revenue model, redefining the target users, and strengthening the marketing strategy.As a result, the business advisor system according to the embodiment can transform the user's business idea into a concrete business plan, and provide an objective evaluation and suggestions for improvements.

[0030] The reception unit can analyze the user's past business idea submission history and select the optimal reception method. For example, the reception unit can analyze trends in business ideas submitted by the user in the past and suggest the optimal input format. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. This makes it possible to provide the optimal reception method based on the user's past submission history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0031] When receiving business ideas, the reception unit can filter the ideas based on the user's current project or area of ​​interest. For example, the reception unit can prioritize receiving business ideas related to a project currently underway by the user. The reception unit can also filter and receive related business ideas based on the user's area of ​​interest. The reception unit can also prioritize receiving highly relevant business ideas by referring to the user's past project history. This allows business ideas related to the user's current project or area of ​​interest to be preferentially received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's project history data into a generation AI and have the generation AI perform filtering.

[0032] When receiving business ideas, the reception unit can prioritize receiving highly relevant ideas by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving business ideas related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving business ideas related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving business ideas related to the area around the user's home. This makes it possible to prioritize receiving highly relevant business ideas based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant ideas.

[0033] When receiving a business idea, the reception unit can analyze the user's social media activity and receive related ideas. The reception unit can receive related business ideas based on, for example, content shared by the user on social media. The reception unit can also receive related business ideas based on the content of accounts the user follows on social media. The reception unit can also receive related business ideas based on the content of groups the user participates in on social media. This makes it possible to receive related business ideas based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related ideas.

[0034] When generating a business plan, the generation unit can adjust the level of detail of the generated business plan based on the importance of the idea. For example, the generation unit generates a detailed business plan for an idea with high importance. The generation unit can also generate a concise business plan for an idea with low importance. The generation unit can also generate a business plan with an appropriate level of detail for an idea with medium importance. This makes it possible to adjust the level of detail of the business plan depending on the importance of the idea. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input idea importance data into the generation AI and cause the generation AI to adjust the level of detail.

[0035] When generating a business plan, the generation unit can apply different generation algorithms depending on the category of the idea. For example, the generation unit can apply a generation algorithm that emphasizes technical details to technology-related ideas. The generation unit can also apply a generation algorithm that emphasizes customer experience to service-related ideas. The generation unit can also apply a generation algorithm that emphasizes production processes to manufacturing-related ideas. This makes it possible to apply the optimal generation algorithm depending on the category of the idea. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input idea category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0036] When generating business plans, the generation unit can determine the priority of generation based on the submission date of the ideas. For example, the generation unit can generate business plans with priority for ideas submitted early. The generation unit can also postpone the generation of business plans for ideas submitted late. The generation unit can also generate business plans with appropriate priority for ideas submitted at an intermediate time. This makes it possible to determine the priority of generating business plans based on the submission date of the ideas. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission date of ideas into the generation AI and have the generation AI determine the priority.

[0037] The generation unit can adjust the order of generation based on the relevance of ideas when generating business plans. For example, the generation unit can generate business plans with priority for highly relevant ideas. The generation unit can also generate business plans later for ideas with low relevance. The generation unit can also generate business plans in an appropriate order for ideas with medium relevance. This makes it possible to adjust the order of generation of business plans based on the relevance of ideas. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input idea relevance data into the generation AI and have the generation AI adjust the order.

[0038] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between business plans during the evaluation. The evaluation unit can, for example, analyze the interrelationships between multiple business plans to improve the accuracy of the evaluation. The evaluation unit can also improve the accuracy of the evaluation by taking into account the relevance of the business plans. The evaluation unit can also improve the accuracy of the evaluation by taking into account the interdependence of the business plans. In this way, the accuracy of the evaluation can be improved by taking into account the interrelationships between business plans. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input interrelationship data between business plans into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0039] When making an evaluation, the evaluation unit can take into account the attribute information of the person who submitted the business plan. The evaluation unit can make the evaluation, for example, by taking into account the industry experience of the person who submitted the business plan. The evaluation unit can also make the evaluation by taking into account the submitter's past success stories. The evaluation unit can also make the evaluation by taking into account the submitter's expertise. In this way, by taking into account the attribute information of the submitter, a more appropriate evaluation can be made. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the attribute information data of the submitter into the generation AI and have the generation AI perform the evaluation.

[0040] The evaluation unit can perform the evaluation by taking into account the geographic distribution of the business plan. For example, the evaluation unit analyzes the geographic distribution of the business plan and performs the evaluation. The evaluation unit can also perform the evaluation by taking into account geographic market characteristics. The evaluation unit can also perform the evaluation by taking into account the geographical competitive situation. In this way, by taking into account the geographic distribution of the business plan, a more appropriate evaluation can be performed. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input geographic distribution data of the business plan into the generation AI and have the generation AI perform the evaluation.

[0041] During the evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the business plan. For example, the evaluation unit can improve the accuracy of the evaluation by referring to academic papers related to the business plan. The evaluation unit can also improve the accuracy of the evaluation by referring to market reports related to the business plan. The evaluation unit can also improve the accuracy of the evaluation by referring to patent documents related to the business plan. In this way, the accuracy of the evaluation can be improved by referring to literature related to the business plan. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0042] The proposal unit can adjust the level of detail of the proposal based on the importance of the business plan when making the proposal. For example, the proposal unit makes a detailed proposal for a business plan with high importance. The proposal unit can also make a concise proposal for a business plan with low importance. The proposal unit can also make a proposal with an appropriate level of detail for a business plan with medium importance. This makes it possible to provide an optimal level of detail for the proposal depending on the importance of the business plan. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input importance data of the business plan to the generation AI and cause the generation AI to adjust the level of detail.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the business plan. For example, the proposal unit can make proposals that emphasize technical details for technology-related business plans. The proposal unit can also make proposals that emphasize customer experience for service-related business plans. The proposal unit can also make proposals that emphasize production processes for manufacturing-related business plans. This makes it possible to apply the optimal proposal algorithm depending on the category of the business plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input business plan category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0044] The proposal unit can determine the priority of proposals based on the submission date of the business plans when making proposals. For example, the proposal unit can prioritize business plans that are submitted early. The proposal unit can also postpone proposals for business plans that are submitted late. The proposal unit can also give appropriate priority to business plans that are submitted at an intermediate time. This makes it possible to determine the priority of proposals based on the submission date of the business plans. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input business plan submission date data into the generation AI and have the generation AI determine the priority.

[0045] The proposal unit can adjust the order of proposals based on the relevance of the business plans when making proposals. For example, the proposal unit prioritizes proposals for highly relevant business plans. The proposal unit can also postpone proposals for less relevant business plans. The proposal unit can also make proposals in an appropriate order for medium-relevant business plans. This makes it possible to adjust the order of proposals based on the relevance of the business plans. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input relevance data of the business plans to a generation AI and cause the generation AI to adjust the order.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the success rate of the user's past business ideas and prioritize accepting ideas with a high success rate. For example, it can learn the patterns of past successful business ideas and prioritize accepting similar ideas. The reception unit can also analyze the user's past failed ideas and provide feedback to help avoid similar failures. Furthermore, the reception unit can take into account the frequency with which the user has submitted ideas in the past and prioritize accepting ideas from users who have submitted ideas more frequently. This makes it possible to provide an optimal reception method based on the success rate of the user's past business ideas.

[0048] When evaluating a business plan, the evaluation unit can adjust the evaluation criteria taking into account the user's level of industry knowledge. For example, specialized evaluation criteria can be applied to a user with extensive industry knowledge. On the other hand, basic evaluation criteria can be applied to a user with little industry knowledge. Furthermore, the evaluation unit can adjust the level of detail of the evaluation results depending on the user's level of industry knowledge. This makes it possible to provide the user with optimal evaluation criteria according to their level of industry knowledge.

[0049] When generating a business plan, the generator can adjust the generation algorithm taking into account the user's past feedback. For example, the generator can learn the features of business plans that users have liked in the past and generate a business plan with similar features. It can also avoid features of business plans for which users have given negative feedback in the past. Furthermore, the generator can dynamically adjust the parameters of the generation algorithm based on the user's past feedback. This makes it possible to generate an optimal business plan based on the user's past feedback.

[0050] When proposing a business plan, the proposal unit can analyze the user's past proposal acceptance history and select the optimal proposal method. For example, it can learn the characteristics of proposals that the user has accepted in the past and make similar proposals. It can also avoid the characteristics of proposals that the user has rejected in the past. Furthermore, it can dynamically adjust the level of detail and expression of the proposal based on the user's past proposal acceptance history. This makes it possible to provide the optimal proposal method based on the user's past proposal acceptance history.

[0051] When generating a business plan, the generation unit can adjust the content of the plan to be generated taking into account the user's current project progress. For example, if the project is in the early stages, a basic business plan can be generated. If the project is in the middle stages, a detailed business plan can be generated. Furthermore, if the project is in the final stages, an executable action plan can be generated. This makes it possible to provide the user with an optimal business plan depending on the project progress.

[0052] When evaluating a business plan, the evaluation unit can adjust the evaluation criteria taking into account the geographical market characteristics of the user. For example, the evaluation can be performed taking into account the market characteristics of a specific region. The evaluation can also be performed taking into account the competitive situation in each region. Furthermore, the evaluation can also be performed taking into account consumer behavior in each region. This makes it possible to provide optimal evaluation criteria according to the geographical market characteristics of the user.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives business ideas entered by the user. Business ideas include ideas for new businesses, product improvements, marketing strategies, etc. The reception unit supports multiple input formats, including text, voice input, and image input. For example, it uses voice recognition technology to convert dictated business ideas into text data, and image recognition technology to convert handwritten business ideas into digital data. Step 2: The generation unit uses generation AI to analyze the business idea received by the reception unit and generate the outline of a business plan. The generation unit performs market analysis, competitive analysis, target user identification, and revenue model proposals. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to analyze the business idea and clarify the strengths and weaknesses of the business plan. Step 3: The evaluation unit objectively evaluates the business plan generated by the generation unit. The evaluation unit evaluates the business's likelihood of success, risk factors, competitive advantages, etc. The evaluation unit uses the generation AI to evaluate each element of the business plan and provides a comprehensive evaluation result. Step 4: The proposal department proposes improvements to the business plan based on the results of the evaluation by the evaluation department. The proposal department presents specific improvements, such as reviewing the revenue model, redefining the target users, and strengthening the marketing strategy. The proposal department uses generative AI to make specific proposals for improving each element of the business plan.

[0055] (Example 2) A business advisor system according to an embodiment of the present invention utilizes a generative AI as a business advisor for people aspiring to start a new business. It assists them in developing a business plan from a business idea seed, objectively evaluates the completed business plan, and suggests improvements for further improvement. This business advisor system begins with a user inputting a business idea seed. The generative AI analyzes the idea and generates the outline of a business plan. For example, if a user inputs "I want to create an online education platform," the generative AI performs market analysis, competitive analysis, identifies target users, proposes a revenue model, and so on, to create the outline of the business plan. The generative AI then objectively evaluates the created business plan. This evaluation includes factors such as the business's likelihood of success, risk factors, and competitive advantages. For example, the generative AI analyzes market growth potential and the strength of competitors to clarify the strengths and weaknesses of the business plan. Furthermore, the generative AI suggests improvements to the business plan. For example, it suggests specific improvements such as revising the revenue model, redefining the target users, and strengthening the marketing strategy. This allows users to further solidify their business plans. With this system, people aspiring to start a new business can, with the assistance of a generative AI, transform their business idea into a concrete business plan, receiving an objective evaluation and suggestions for improvement. This increases the business's chances of success and minimizes risk. For example, a user inputs the seed of a business idea, such as "I want to create an online education platform." This information is then entered into the generative AI. The generative AI then analyzes the input information and generates the outline of a business plan. The generative AI then performs market analysis, competitive analysis, identifies target users, proposes revenue models, and other processes to create the outline of the business plan. For example, if a user inputs "I want to create an online education platform," the generative AI analyzes market growth potential and competitive strength to clarify the strengths and weaknesses of the business plan. The generated business plan is then objectively evaluated by the generative AI.The evaluation includes the business's likelihood of success, risk factors, and competitive advantages. For example, the generative AI analyzes market growth potential and the strength of competition to clarify the strengths and weaknesses of the business plan. Furthermore, the generative AI suggests improvements to the business plan. For example, it suggests specific improvements such as revising the revenue model, redefining target users, and strengthening the marketing strategy. This allows users to further strengthen their own business plans. With this system, people aspiring to start a new business can, with the assistance of the generative AI, develop their business ideas into concrete business plans and receive objective evaluations and suggestions for improvements. This increases the business's chances of success and minimizes risks. The business advisor system can then develop users' business ideas into concrete business plans and provide objective evaluations and suggestions for improvements.

[0056] A business advisor system according to an embodiment includes a reception unit, a generation unit, an evaluation unit, and a proposal unit. The reception unit receives a business idea input by a user. Examples of business ideas include, but are not limited to, new business ideas, product improvement ideas, and marketing strategy ideas. The reception unit receives the business idea input by the user in text format, for example. The reception unit can also support multiple input formats, such as voice input and image input. For example, the reception unit can convert a business idea dictated by a user into text data using voice recognition technology. The reception unit can also convert a handwritten business idea into digital data using image recognition technology. The generation unit uses a generation AI to analyze the business idea received by the reception unit and generate a business plan outline. The generation unit performs, for example, market analysis, competitive analysis, target user identification, and revenue model proposal. The generation AI analyzes the business idea and generates a business plan outline using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes market growth potential and competitive strength based on a business idea input by a user, and clarifies the strengths and weaknesses of the business plan. The evaluation unit objectively evaluates the business plan generated by the generation unit. The evaluation unit evaluates, for example, the business's likelihood of success, risk factors, competitive advantage, etc. The evaluation unit uses the generation AI to evaluate each element of the business plan and provide a comprehensive evaluation result. For example, the evaluation unit uses the generation AI to analyze market growth potential and competitive strength, and clarifies the strengths and weaknesses of the business plan. The proposal unit proposes improvements to the business plan based on the results of the evaluation by the evaluation unit. The proposal unit presents specific improvements, such as reviewing the revenue model, redefining the target users, and strengthening the marketing strategy. The proposal unit uses the generation AI to make specific proposals for improving each element of the business plan. For example, the proposal unit uses the generation AI to present specific improvements, such as reviewing the revenue model, redefining the target users, and strengthening the marketing strategy.As a result, the business advisor system according to the embodiment can transform the user's business idea into a concrete business plan, and provide an objective evaluation and suggestions for improvements.

[0057] The reception unit can estimate the user's emotions and adjust the timing of inputting a business idea based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input a business idea at a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can also prompt the user to input a business idea at that time. Furthermore, if the user is tired, the reception unit can also prompt the user to input a business idea after a break. This allows the user to be prompted to input a business idea at the optimal time depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0058] The reception unit can analyze the user's past business idea submission history and select the optimal reception method. For example, the reception unit can analyze trends in business ideas submitted by the user in the past and suggest the optimal input format. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. This makes it possible to provide the optimal reception method based on the user's past submission history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0059] When receiving business ideas, the reception unit can filter the ideas based on the user's current project or area of ​​interest. For example, the reception unit can prioritize receiving business ideas related to a project currently underway by the user. The reception unit can also filter and receive related business ideas based on the user's area of ​​interest. The reception unit can also prioritize receiving highly relevant business ideas by referring to the user's past project history. This allows business ideas related to the user's current project or area of ​​interest to be preferentially received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's project history data into a generation AI and have the generation AI perform filtering.

[0060] The reception unit can estimate the user's emotions and determine the priority of the business ideas to be received based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize new business ideas by utilizing the user's emotions. Furthermore, if the user is relaxed, the reception unit can prioritize detailed business ideas. Furthermore, if the user is stressed, the reception unit can prioritize simple business ideas. This allows the priority of business ideas to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0061] When receiving business ideas, the reception unit can prioritize receiving highly relevant ideas by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving business ideas related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving business ideas related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving business ideas related to the area around the user's home. This makes it possible to prioritize receiving highly relevant business ideas based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant ideas.

[0062] When receiving a business idea, the reception unit can analyze the user's social media activity and receive related ideas. The reception unit can receive related business ideas based on, for example, content shared by the user on social media. The reception unit can also receive related business ideas based on the content of accounts the user follows on social media. The reception unit can also receive related business ideas based on the content of groups the user participates in on social media. This makes it possible to receive related business ideas based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related ideas.

[0063] The generation unit can estimate the user's emotions and adjust the business plan generation method based on the estimated user emotions. For example, the generation unit can generate a detailed business plan when the user is relaxed. The generation unit can also generate a concise business plan when the user is in a hurry. The generation unit can also generate a visually appealing business plan when the user is excited. This allows the generation of an optimal business plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0064] When generating a business plan, the generation unit can adjust the level of detail of the generated business plan based on the importance of the idea. For example, the generation unit generates a detailed business plan for an idea with high importance. The generation unit can also generate a concise business plan for an idea with low importance. The generation unit can also generate a business plan with an appropriate level of detail for an idea with medium importance. This makes it possible to adjust the level of detail of the business plan depending on the importance of the idea. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input idea importance data into the generation AI and cause the generation AI to adjust the level of detail.

[0065] When generating a business plan, the generation unit can apply different generation algorithms depending on the category of the idea. For example, the generation unit can apply a generation algorithm that emphasizes technical details to technology-related ideas. The generation unit can also apply a generation algorithm that emphasizes customer experience to service-related ideas. The generation unit can also apply a generation algorithm that emphasizes production processes to manufacturing-related ideas. This makes it possible to apply the optimal generation algorithm depending on the category of the idea. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input idea category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0066] The generation unit can estimate the user's emotions and adjust the length of the business plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise business plan. If the user is relaxed, the generation unit can generate a longer business plan with detailed explanations. If the user is excited, the generation unit can generate a business plan with visually stimulating effects. This allows the length of the business plan to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0067] When generating business plans, the generation unit can determine the priority of generation based on the submission date of the ideas. For example, the generation unit can generate business plans with priority for ideas submitted early. The generation unit can also postpone the generation of business plans for ideas submitted late. The generation unit can also generate business plans with appropriate priority for ideas submitted at an intermediate time. This makes it possible to determine the priority of generating business plans based on the submission date of the ideas. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission date of ideas into the generation AI and have the generation AI determine the priority.

[0068] The generation unit can adjust the order of generation based on the relevance of ideas when generating business plans. For example, the generation unit can generate business plans with priority for highly relevant ideas. The generation unit can also generate business plans later for ideas with low relevance. The generation unit can also generate business plans in an appropriate order for ideas with medium relevance. This makes it possible to adjust the order of generation of business plans based on the relevance of ideas. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input idea relevance data into the generation AI and have the generation AI adjust the order.

[0069] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can apply detailed evaluation criteria when the user is relaxed. The evaluation unit can also apply concise evaluation criteria when the user is in a hurry. The evaluation unit can also apply visually appealing evaluation criteria when the user is excited. This allows the evaluation criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between business plans during the evaluation. The evaluation unit can, for example, analyze the interrelationships between multiple business plans to improve the accuracy of the evaluation. The evaluation unit can also improve the accuracy of the evaluation by taking into account the relevance of the business plans. The evaluation unit can also improve the accuracy of the evaluation by taking into account the interdependence of the business plans. In this way, the accuracy of the evaluation can be improved by taking into account the interrelationships between business plans. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input interrelationship data between business plans into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0071] When making an evaluation, the evaluation unit can take into account the attribute information of the person who submitted the business plan. The evaluation unit can make the evaluation, for example, by taking into account the industry experience of the person who submitted the business plan. The evaluation unit can also make the evaluation by taking into account the submitter's past success stories. The evaluation unit can also make the evaluation by taking into account the submitter's expertise. In this way, by taking into account the attribute information of the submitter, a more appropriate evaluation can be made. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the attribute information data of the submitter into the generation AI and have the generation AI perform the evaluation.

[0072] The evaluation unit can estimate the user's emotions and adjust the display order of the evaluation results based on the estimated user's emotions. For example, when the user is relaxed, the evaluation unit can prioritize displaying detailed evaluation results. Furthermore, when the user is in a hurry, the evaluation unit can prioritize displaying evaluation results that focus on the main points. Furthermore, when the user is excited, the evaluation unit can prioritize displaying visually appealing evaluation results. This allows the display order of the evaluation results to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] The evaluation unit can perform the evaluation by taking into account the geographic distribution of the business plan. For example, the evaluation unit analyzes the geographic distribution of the business plan and performs the evaluation. The evaluation unit can also perform the evaluation by taking into account geographic market characteristics. The evaluation unit can also perform the evaluation by taking into account the geographical competitive situation. In this way, by taking into account the geographic distribution of the business plan, a more appropriate evaluation can be performed. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input geographic distribution data of the business plan into the generation AI and have the generation AI perform the evaluation.

[0074] During the evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the business plan. For example, the evaluation unit can improve the accuracy of the evaluation by referring to academic papers related to the business plan. The evaluation unit can also improve the accuracy of the evaluation by referring to market reports related to the business plan. The evaluation unit can also improve the accuracy of the evaluation by referring to patent documents related to the business plan. In this way, the accuracy of the evaluation can be improved by referring to literature related to the business plan. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0075] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually appealing suggestions when the user is excited. This makes it possible to provide an optimal way to express the suggestion depending on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, or can be performed without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The proposal unit can adjust the level of detail of the proposal based on the importance of the business plan when making the proposal. For example, the proposal unit makes a detailed proposal for a business plan with high importance. The proposal unit can also make a concise proposal for a business plan with low importance. The proposal unit can also make a proposal with an appropriate level of detail for a business plan with medium importance. This makes it possible to provide an optimal level of detail for the proposal depending on the importance of the business plan. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input importance data of the business plan to the generation AI and cause the generation AI to adjust the level of detail.

[0077] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the business plan. For example, the proposal unit can make proposals that emphasize technical details for technology-related business plans. The proposal unit can also make proposals that emphasize customer experience for service-related business plans. The proposal unit can also make proposals that emphasize production processes for manufacturing-related business plans. This makes it possible to apply the optimal proposal algorithm depending on the category of the business plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input business plan category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0078] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the length of the suggestions to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The proposal unit can determine the priority of proposals based on the submission date of the business plans when making proposals. For example, the proposal unit can prioritize business plans that are submitted early. The proposal unit can also postpone proposals for business plans that are submitted late. The proposal unit can also give appropriate priority to business plans that are submitted at an intermediate time. This makes it possible to determine the priority of proposals based on the submission date of the business plans. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input business plan submission date data into the generation AI and have the generation AI determine the priority.

[0080] The proposal unit can adjust the order of proposals based on the relevance of the business plans when making proposals. For example, the proposal unit prioritizes proposals for highly relevant business plans. The proposal unit can also postpone proposals for less relevant business plans. The proposal unit can also make proposals in an appropriate order for medium-relevant business plans. This makes it possible to adjust the order of proposals based on the relevance of the business plans. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input relevance data of the business plans to a generation AI and cause the generation AI to adjust the order. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, evaluation unit, and proposal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a business idea input by a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the outline of a business plan using a generation AI. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and objectively evaluates the generated business plan. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes improvements to the business plan. For example, the reception unit can estimate the user's emotions using the control unit 46A of the smart device 14 and adjust the timing of inputting the business idea. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, evaluation unit, and proposal unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a business idea input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the outline of a business plan using a generation AI. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and objectively evaluates the generated business plan. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes improvements to the business plan. For example, the reception unit can estimate the user's emotions using the control unit 46A of the smart glasses 214 and adjust the timing of inputting the business idea. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, evaluation unit, and proposal unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives a business idea input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the outline of a business plan using a generation AI. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and objectively evaluates the generated business plan. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes improvements to the business plan. For example, the reception unit can estimate the user's emotions using the control unit 46A of the headset-type terminal 314 and adjust the timing of input of the business idea. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, evaluation unit, and proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a business idea input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the outline of a business plan using a generation AI. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and objectively evaluates the generated business plan. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes improvements to the business plan. For example, the reception unit can estimate the user's emotions using the control unit 46A of the robot 414 and adjust the timing of input of the business idea.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the success rate of the user's past business ideas and prioritize accepting ideas with a high success rate. For example, it can learn the patterns of past successful business ideas and prioritize accepting similar ideas. The reception unit can also analyze the user's past failed ideas and provide feedback to help avoid similar failures. Furthermore, the reception unit can take into account the frequency with which the user has submitted ideas in the past and prioritize accepting ideas from users who have submitted ideas more frequently. This makes it possible to provide an optimal reception method based on the success rate of the user's past business ideas.

[0083] The generation unit can estimate the user's emotions and adjust the order in which business plans are generated based on the estimated user's emotions. For example, if the user is excited, creative ideas can be generated with priority. If the user is relaxed, a business plan including detailed analysis can be generated. Furthermore, if the user is stressed, a concise and feasible business plan can be generated with priority. This makes it possible to provide an optimal order in which business plans are generated according to the user's emotions.

[0084] When evaluating a business plan, the evaluation unit can adjust the evaluation criteria taking into account the user's level of industry knowledge. For example, specialized evaluation criteria can be applied to a user with extensive industry knowledge. On the other hand, basic evaluation criteria can be applied to a user with little industry knowledge. Furthermore, the evaluation unit can adjust the level of detail of the evaluation results depending on the user's level of industry knowledge. This makes it possible to provide the user with optimal evaluation criteria according to their level of industry knowledge.

[0085] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can select the timing to provide detailed suggestions. If the user is in a hurry, the suggestion unit can select the timing to provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can select the timing to provide visually appealing suggestions. This makes it possible to provide optimal suggestion timing according to the user's emotions.

[0086] When generating a business plan, the generator can adjust the generation algorithm taking into account the user's past feedback. For example, the generator can learn the features of business plans that users have liked in the past and generate a business plan with similar features. It can also avoid features of business plans for which users have given negative feedback in the past. Furthermore, the generator can dynamically adjust the parameters of the generation algorithm based on the user's past feedback. This makes it possible to generate an optimal business plan based on the user's past feedback.

[0087] The evaluation unit can estimate the user's emotions and adjust the display format of the evaluation results based on the estimated user's emotions. For example, if the user is relaxed, the evaluation results can be displayed in a detailed text format. If the user is in a hurry, the evaluation results can be displayed in a bulleted list format that focuses on the main points. Furthermore, if the user is excited, the evaluation results can be displayed in a visually appealing graphical format. This makes it possible to provide an optimal display format for the evaluation results depending on the user's emotions.

[0088] When proposing a business plan, the proposal unit can analyze the user's past proposal acceptance history and select the optimal proposal method. For example, it can learn the characteristics of proposals that the user has accepted in the past and make similar proposals. It can also avoid the characteristics of proposals that the user has rejected in the past. Furthermore, it can dynamically adjust the level of detail and expression of the proposal based on the user's past proposal acceptance history. This makes it possible to provide the optimal proposal method based on the user's past proposal acceptance history.

[0089] When generating a business plan, the generation unit can adjust the content of the plan to be generated taking into account the user's current project progress. For example, if the project is in the early stages, a basic business plan can be generated. If the project is in the middle stages, a detailed business plan can be generated. Furthermore, if the project is in the final stages, an executable action plan can be generated. This makes it possible to provide the user with an optimal business plan depending on the project progress.

[0090] When evaluating a business plan, the evaluation unit can adjust the evaluation criteria taking into account the geographical market characteristics of the user. For example, the evaluation can be performed taking into account the market characteristics of a specific region. The evaluation can also be performed taking into account the competitive situation in each region. Furthermore, the evaluation can also be performed taking into account consumer behavior in each region. This makes it possible to provide optimal evaluation criteria according to the geographical market characteristics of the user.

[0091] The suggestion unit can estimate the user's emotions and adjust the priority of suggestions based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be given priority. If the user is in a hurry, brief suggestions can be given priority. Furthermore, if the user is excited, visually appealing suggestions can be given priority. In this way, it is possible to provide optimal suggestion priorities according to the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives business ideas entered by the user. Business ideas include ideas for new businesses, product improvements, marketing strategies, etc. The reception unit supports multiple input formats, including text, voice input, and image input. For example, it uses voice recognition technology to convert dictated business ideas into text data, and image recognition technology to convert handwritten business ideas into digital data. Step 2: The generation unit uses generation AI to analyze the business idea received by the reception unit and generate the outline of a business plan. The generation unit performs market analysis, competitive analysis, target user identification, and revenue model proposals. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to analyze the business idea and clarify the strengths and weaknesses of the business plan. Step 3: The evaluation unit objectively evaluates the business plan generated by the generation unit. The evaluation unit evaluates the business's likelihood of success, risk factors, competitive advantages, etc. The evaluation unit uses the generation AI to evaluate each element of the business plan and provides a comprehensive evaluation result. Step 4: The proposal department proposes improvements to the business plan based on the results of the evaluation by the evaluation department. The proposal department presents specific improvements, such as reviewing the revenue model, redefining the target users, and strengthening the marketing strategy. The proposal department uses generative AI to make specific proposals for improving each element of the business plan.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of business ideas; a generation unit that analyzes the business idea received by the reception unit and generates a business plan; an evaluation unit that evaluates the business plan generated by the generation unit; a proposal unit that proposes improvements to the business plan based on the results of the evaluation by the evaluation unit. A system characterized by:

2. The reception unit Estimate user emotions and adjust the timing of business idea input based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's past business idea submission history and select the optimal reception method 2. The system of claim 1.

4. The reception unit Filter business ideas based on your current projects and interests 2. The system of claim 1.

5. The reception unit Estimate user emotions and prioritize business ideas based on the estimated user emotions 2. The system of claim 1.

6. The reception unit When accepting business ideas, the app takes into account the user's geographic location information to prioritize relevant ideas.

2. The system of claim 1.

7. The reception unit When accepting business ideas, analyze users' social media activity and accept related ideas.

2. The system of claim 1.

8. The generation unit Estimate user sentiment and adjust the business plan generation method based on the estimated user sentiment.

2. The system of claim 1.

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