System

Abstract

An object of the system according to the embodiment is to identify a success factor of a business idea, make a strategic recommendation, and respond to a change in the market in real time.SOLUTION: A system includes a business idea providing part, a data analysis part, a strategy recommendation part, a successful case analysis part, and a market monitoring part. The business idea providing unit provides a business idea. The data analysis section analyzes the business idea provided by the business idea providing section. The strategy recommender provides strategic recommendations based on the data analyzed by the data analyzer. The success case analysis part specifies a success factor on the basis of the success case analyzed by the data analysis part. The market monitoring component monitors market changes and competition and updates the advice in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have the drawback of making it difficult to respond to market changes in real time, as the process of identifying the success factors for a business idea and making strategic recommendations is complex.

[0005] The system according to the embodiment aims to identify the success factors of business ideas, make strategic recommendations, and respond to market changes in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a business idea providing unit, a data analysis unit, a strategy proposal unit, a success story analysis unit, and a market monitoring unit. The business idea providing unit provides business ideas. The data analysis unit analyzes the business ideas provided by the business idea providing unit. The strategy proposal unit provides strategic recommendations based on data analyzed by the data analysis unit. The success story analysis unit identifies success factors based on the success stories analyzed by the data analysis unit. The market monitoring unit monitors market changes and competitive conditions and updates advice in real time. [Effects of the Invention]

[0007] The system according to the embodiment can identify the success factors of business ideas, provide strategic recommendations, and respond to market changes in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The consultation system according to an embodiment of the present invention utilizes generative AI and a past database to learn from similar failed services both domestically and internationally, leading to success. This allows the consultation system to provide specific advice to optimize a customer's business idea and lead it to success.

[0029] A consultation system according to an embodiment includes a business idea providing unit, a data analysis unit, a strategy proposal unit, a success story analysis unit, and a market monitoring unit. The business idea providing unit receives business ideas provided by customers. For example, if a customer has an idea for a new restaurant, the business idea providing unit can receive the idea. The business idea providing unit can also receive business model and product ideas provided by customers. The data analysis unit analyzes the business ideas provided by the business idea providing unit. For example, the data analysis unit collects and analyzes data on similar services from a past database. The data analysis unit can also collect related data from social media and news articles to perform analysis from a broader perspective. The strategy proposal unit provides strategic recommendations based on the data analyzed by the data analysis unit. For example, the strategy proposal unit analyzes data from failed services and provides specific improvement measures and market positioning. The strategy proposal unit can also simulate the feasibility of proposals and perform risk assessments. The success story analysis unit identifies success factors based on the success stories analyzed by the data analysis unit. For example, the success case analysis unit analyzes successful service cases and identifies the factors behind their success. The success case analysis unit can also track the factors behind success cases over time to clarify the process of success. The market monitoring unit monitors market changes and competitive conditions and updates advice in real time. For example, the market monitoring unit monitors recent market trends and competitor activities and provides advice in real time. The market monitoring unit can also collect data from social media and news articles to provide advice from a broader perspective. This allows the consultation system according to the embodiment to provide specific advice to optimize a customer's business idea and lead it to success. For example, if a customer is planning to open a new restaurant, they can increase the chances of success by learning from past failures and referring to success stories. Furthermore, business resilience can be enhanced by quickly responding to market changes.

[0030] The data analysis unit collects relevant data not only from the database but also from social media and news articles, allowing for analysis from a broader perspective. For example, when the generative AI analyzes a business idea, the data analysis unit collects related posts and comments from social media and integrates them with the database for analysis. For example, it analyzes posts on Twitter and Facebook to reflect trends and user opinions. The data analysis unit also automatically collects relevant information from news articles, which the generative AI uses to analyze the business idea. For example, it extracts the latest market trends and competitor actions from news articles and reflects them in the analysis. The data analysis unit also allows the generative AI to analyze the business idea based on the data collected from social media and news articles, providing insights from a broader perspective. For example, it provides analysis results that reflect user interests and market changes. This enables data analysis from a broader perspective, allowing for more accurate advice.

[0031] The data analysis department can identify the factors for success and failure of each region by taking into account the cultural background and regional characteristics of the idea. For example, when the generation AI analyzes a business idea, the data analysis department considers the cultural background of each region and identifies the factors for success and failure. For example, it reflects consumer behavior and cultural customs in a specific region in the analysis. The data analysis department also performs data analysis that takes into account regional characteristics, allowing the generation AI to identify the factors for success and failure of the business idea. For example, it incorporates market needs and competitive situations in each region into the analysis. The data analysis department also collects data that takes into account cultural background and regional characteristics when the generation AI analyzes a business idea, and uses this data in the analysis. For example, it provides analysis results that reflect consumer preferences and trends in each region. This enables analysis that takes into account regional characteristics, allowing for more appropriate advice to be provided.

[0032] The business idea providing unit can accept input in different formats, such as voice input or visual notes. For example, the business idea providing unit adds a function to accept voice input when the generation AI receives a business idea. For example, a user can use a microphone to input an idea by voice, and the generation AI can analyze the voice and save it in a database. The business idea providing unit can also add a function to accept visual notes, and when the generation AI receives a business idea, it can analyze handwritten notes or sketches. For example, a user can upload handwritten notes, and the generation AI can digitize and analyze the contents. The business idea providing unit can also add a function to accept input in different formats, and when the generation AI receives a business idea, it can analyze the voice or visual notes and save them in a database. For example, a user can upload voice memos or visual notes, and the generation AI can analyze and save the contents. This allows for input in different formats, improving user convenience.

[0033] The data analysis unit can refer to databases from different industries across sectors and incorporate learnings from those sectors. For example, when the generative AI analyzes a business idea, the data analysis unit refers to databases from different industries and incorporates learnings from those sectors. For example, when analyzing ideas from the food and beverage industry, the data analysis unit also refers to data from the IT and medical industries. The data analysis unit also collects success and failure cases from other industries from the database, which the generative AI uses to analyze business ideas. For example, the success and failure factors from those sectors are reflected in the analysis. The data analysis unit also refers to databases from different industries across sectors and incorporates learnings from those sectors when the generative AI analyzes a business idea. For example, market trends and technology trends from those sectors are reflected in the analysis. Incorporating learnings from those sectors makes it possible to provide advice from a more multifaceted perspective.

[0034] The Strategic Proposal Department can compare and analyze past success stories and failure stories to propose more specific improvement measures. For example, when the Generative AI provides strategic recommendations, the Strategic Proposal Department compares and analyzes past success stories and failure stories to propose specific improvement measures. For example, it compares the factors of success stories with the factors of failure stories to identify areas for improvement. The Strategic Proposal Department also collects past success stories and failure stories from a database, and the Generative AI performs a comparative analysis to propose specific improvement measures. For example, it analyzes the commonalities between success stories and failure stories to propose areas for improvement. The Strategic Proposal Department also compares and analyzes past success stories and failure stories to propose specific improvement measures when the Generative AI provides strategic recommendations. For example, it makes suggestions to incorporate the factors of success stories and avoid the factors of failure stories. In this way, more specific improvement measures can be proposed by comparing and analyzing past stories.

[0035] The strategic proposal department can simulate the feasibility of proposals and perform risk assessments. For example, when the generation AI provides a strategic recommendation, the strategic proposal department simulates the feasibility of proposals and performs risk assessments. For example, it evaluates the risks associated with executing the proposal and proposes risk mitigation measures. The strategic proposal department also builds a system that simulates the feasibility of proposals and allows the generation AI to perform risk assessments. For example, it simulates the resources and costs required to execute the proposal and evaluates the risks. The strategic proposal department also simulates the feasibility of proposals and performs risk assessments when the generation AI provides a strategic recommendation. For example, it evaluates the risks associated with executing the proposal and proposes risk mitigation measures. In this way, by simulating the feasibility of proposals and performing risk assessments, safer proposals can be made.

[0036] The Strategic Proposal Department can refer to success stories from different industries and incorporate learnings from those industries. For example, when the Generative AI provides strategic recommendations, the Strategic Proposal Department refers to success stories from different industries and incorporates learnings from those industries. For example, when analyzing ideas for the food and beverage industry, success stories from the IT and medical industries are referenced. The Strategic Proposal Department also collects success stories from other industries from a database, which the Generative AI uses in its strategic recommendations. For example, the success factors of those industries are analyzed and reflected in proposals. The Strategic Proposal Department also refers to success stories from different industries and incorporates learnings from those industries when the Generative AI provides strategic recommendations. For example, market trends and technology trends from those industries are reflected in the analysis. In this way, by incorporating learnings from those industries, it becomes possible to make proposals from a more multifaceted perspective.

[0037] The success story analysis unit takes into account the cultural background and market characteristics behind success stories, allowing it to provide deeper insights. For example, when the generation AI analyzes success stories, the success story analysis unit takes cultural background into account and identifies the success factors. For example, consumer behavior and cultural customs in specific regions are reflected in the analysis. The success story analysis unit also performs data analysis that takes market characteristics into account, allowing the generation AI to identify the factors behind success stories. For example, the market needs and competitive situation for each region are incorporated into the analysis. The success story analysis unit also collects data that takes cultural background and market characteristics into account when the generation AI analyzes success stories, and uses this data in the analysis. For example, it provides analysis results that reflect consumer preferences and trends for each region. This makes it possible to conduct analysis that takes cultural background and market characteristics into account, allowing for deeper insights.

[0038] The success case analysis unit tracks the factors behind success cases over time, making it possible to clarify the process of success. For example, when the generation AI analyzes a success case, the success case analysis unit tracks the factors behind success over time and makes clear the process of success. For example, it analyzes each step of the success case over time and identifies the factors behind success. In addition, data on success cases is collected over time, and the generation AI uses that data to make clear the process of success. For example, it analyzes important events and decisions at each stage of the success case. In addition, when the generation AI analyzes a success case, the success case analysis unit tracks the factors behind success over time and makes clear the process of success. For example, it analyzes each step of the success case over time and identifies the factors behind success. In this way, by tracking the factors behind a success case over time, it is possible to make clear the process of success.

[0039] The success case analysis unit can compare success cases from different regions and cultural spheres and provide insights from a global perspective. For example, when the generation AI analyzes success cases, the success case analysis unit compares success cases from different regions and cultural spheres and provides insights from a global perspective. For example, it compares success factors from different regions and identifies similarities and differences. In addition, success cases from different cultural spheres are collected from a database, and the generation AI provides insights from a global perspective based on that data. For example, consumer behavior and market characteristics from different cultural spheres are reflected in the analysis. In addition, when the generation AI analyzes success cases, the success case analysis unit compares success cases from different regions and cultural spheres and provides insights from a global perspective. For example, it compares success factors from different regions and identifies similarities and differences. This makes it possible to provide insights from a global perspective by comparing success cases from different regions and cultural spheres.

[0040] The success case analysis unit can visualize success cases to enable users to intuitively understand them. For example, when the generation AI analyzes a success case, the success case analysis unit visualizes the success case to enable users to intuitively understand it. For example, it displays the factors behind the success case in graphs and charts. In addition, a tool for visualizing success cases is developed and used by the generation AI when analyzing success cases. For example, it displays the factors behind the success case as an infographic. In addition, when the generation AI analyzes a success case, the success case analysis unit visualizes the success case to enable users to intuitively understand it. For example, it displays the factors behind the success case in slide format. In this way, by visualizing the success case, users can intuitively understand it.

[0041] The market monitoring unit also collects data from social media and news articles, allowing it to provide advice from a broader perspective. For example, when the generation AI monitors market changes, the market monitoring unit collects related posts and comments from social media and integrates them with a database to provide advice. For example, it analyzes posts on Twitter and Facebook to reflect trends and user opinions. The market monitoring unit also automatically collects related information from news articles, which the generation AI uses to provide advice in response to market changes. For example, it extracts the latest market trends and competitor actions from news articles and reflects them in the advice. The market monitoring unit also allows the generation AI to provide advice in response to market changes based on the data collected from social media and news articles. For example, it provides advice that reflects user interests and market changes. This enables advice to be provided from a broader perspective by collecting data from social media and news articles.

[0042] The market monitoring unit can track competitors' trends in real time and provide advice to maintain a competitive advantage. For example, when the generation AI monitors changes in the market, the market monitoring unit tracks competitors' trends in real time and provides advice to maintain a competitive advantage. For example, it analyzes the trends of competitors' new products and services and reflects this in the advice. The market monitoring unit also collects competitors' trends from a database and uses them to provide advice to the generation AI to respond to market changes. For example, it analyzes competitors' marketing strategies and pricing and reflects this in the advice. The market monitoring unit can also track competitors' trends in real time when the generation AI monitors changes in the market and provide advice to maintain a competitive advantage. For example, it analyzes the trends of competitors' new products and services and reflects this in the advice. This makes it possible to provide advice to maintain a competitive advantage by tracking competitors' trends in real time.

[0043] The market monitoring department can refer to market trends in different industries and incorporate learnings from those industries. For example, when the generation AI monitors market changes, the market monitoring department refers to market trends in different industries and incorporates learnings from those industries. For example, when analyzing market trends in the food and beverage industry, the market trends in the IT and medical industries are referenced. The market monitoring department also collects market trends in other industries from a database, which the generation AI uses to provide advice in response to market changes. For example, it reflects market trends and technology trends in those industries in its analysis. The market monitoring department also refers to market trends in different industries and incorporates learnings from those industries when the generation AI monitors market changes. For example, it reflects market trends and technology trends in those industries in its analysis. This allows the generation AI to incorporate learnings from those industries, making it possible to provide advice from a more multifaceted perspective.

[0044] The market monitoring unit can visualize the advice content to enable users to intuitively understand it. For example, when the generation AI monitors market changes, the market monitoring unit visualizes the advice content to enable users to intuitively understand it. For example, the advice content is displayed in graphs and charts. In addition, a tool for visualizing the advice content is developed and used when the generation AI provides advice in response to market changes. For example, the advice content is displayed as an infographic. In addition, when the generation AI monitors market changes, the market monitoring unit visualizes the advice content to enable users to intuitively understand it. For example, the advice content is displayed in a slide format. In this way, visualizing the advice content allows users to intuitively understand it.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The consultation system can further include a history analysis unit that analyzes the user's past behavioral history and provides personalized advice. For example, the history analysis unit analyzes business ideas submitted by the user in the past and their results to identify patterns of success and failure. The history analysis unit can also provide more personalized advice based on the user's past feedback and evaluations. Furthermore, the history analysis unit can track the user's past behavioral history chronologically and propose long-term growth strategies. This makes it possible to provide advice that takes the user's past behavioral history into consideration, resulting in more accurate support.

[0047] The consultation system can also provide a peer review function that allows users to share their business ideas with other users and receive feedback. For example, a user can post a business idea and receive comments and ratings from other users. The peer review function also allows users to provide feedback on other users' business ideas. Furthermore, the peer review function can help users generate new ideas by referring to the business ideas of other users. This allows the quality of business ideas to be improved through mutual feedback between users.

[0048] The consultation system can further provide a fundraising support function to help users realize their business ideas. For example, by linking with a crowdfunding platform, users can publish their business ideas and raise funds. The fundraising support function can also provide a matching service that allows users to directly contact investors and sponsors. Furthermore, the fundraising support function can also support users in creating presentation materials and business plans for fundraising. This allows users to efficiently raise funds to realize their business ideas.

[0049] The consultation system can further provide a partnership support function to realize the user's business idea. For example, it can provide a service that matches the user with companies and experts that provide the technology and services the user needs. The partnership support function can also provide collaboration tools that allow the user to work on projects together with other companies and experts. Furthermore, the partnership support function can support the user in creating contracts and agreements to form partnerships. This allows the user to find appropriate partners to realize their business idea and collaborate efficiently.

[0050] The consultation system may further provide a marketing support function to help users realize their business ideas. For example, the system may provide tools to help users identify target markets and develop effective marketing strategies. The marketing support function may also support users in planning and executing advertising campaigns. Furthermore, the marketing support function may provide advice to help users spread their business ideas using social media and online platforms. This may help users develop effective marketing strategies and lead their business ideas to success.

[0051] The consultation system can also provide legal support functions to help users realize their business ideas. For example, it can support users in going through the legal procedures necessary when starting a business. The legal support function can also provide advice when users are drafting contracts and agreements. Furthermore, the legal support function can support users in the procedures for protecting their intellectual property rights. This allows users to clear legal issues and proceed with their business with peace of mind.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The business idea submission department receives business ideas provided by the customer. For example, if a customer has an idea for a new restaurant, the business idea submission department can receive that idea. The business idea submission department can also receive business model and product ideas provided by the customer. Step 2: The Data Analysis Department analyzes the business ideas provided by the Business Idea Provider. For example, the Data Analysis Department collects data on similar services from historical databases and analyzes them. The Data Analysis Department can also collect related data from social media and news articles to conduct analysis from a broader perspective. Step 3: The Strategic Recommendation Department provides strategic recommendations based on the data analyzed by the Data Analysis Department. For example, the Strategic Recommendation Department analyzes data from a failed service and provides specific improvement measures and market positioning. The Strategic Recommendation Department can also simulate the feasibility of proposals and perform risk assessments. Step 4: The success case analysis department identifies the success factors based on the success cases analyzed by the data analysis department. For example, the success case analysis department analyzes successful service cases and identifies the factors that led to their success. The success case analysis department can also track the factors that led to success over time and clarify the process of success. Step 5: The Market Monitoring Department monitors market changes and the competitive landscape and provides real-time advice. For example, the Market Monitoring Department monitors recent market trends and competitor activity and provides real-time advice. The Market Monitoring Department can also collect data from social media and news articles to provide advice from a broader perspective.

[0054] (Example 2) The consultation system according to an embodiment of the present invention utilizes generative AI and a past database to learn from similar failed services both domestically and internationally, leading to success. This allows the consultation system to provide specific advice to optimize a customer's business idea and lead it to success.

[0055] A consultation system according to an embodiment includes a business idea providing unit, a data analysis unit, a strategy proposal unit, a success story analysis unit, and a market monitoring unit. The business idea providing unit receives business ideas provided by customers. For example, if a customer has an idea for a new restaurant, the business idea providing unit can receive the idea. The business idea providing unit can also receive business model and product ideas provided by customers. The data analysis unit analyzes the business ideas provided by the business idea providing unit. For example, the data analysis unit collects and analyzes data on similar services from a past database. The data analysis unit can also collect related data from social media and news articles to perform analysis from a broader perspective. The strategy proposal unit provides strategic recommendations based on the data analyzed by the data analysis unit. For example, the strategy proposal unit analyzes data from failed services and provides specific improvement measures and market positioning. The strategy proposal unit can also simulate the feasibility of proposals and perform risk assessments. The success story analysis unit identifies success factors based on the success stories analyzed by the data analysis unit. For example, the success case analysis unit analyzes successful service cases and identifies the factors behind their success. The success case analysis unit can also track the factors behind success cases over time to clarify the process of success. The market monitoring unit monitors market changes and competitive conditions and updates advice in real time. For example, the market monitoring unit monitors recent market trends and competitor activities and provides advice in real time. The market monitoring unit can also collect data from social media and news articles to provide advice from a broader perspective. This allows the consultation system according to the embodiment to provide specific advice to optimize a customer's business idea and lead it to success. For example, if a customer is planning to open a new restaurant, they can increase the chances of success by learning from past failures and referring to success stories. Furthermore, business resilience can be enhanced by quickly responding to market changes.

[0056] The business idea providing unit estimates the user's emotions and provides feedback to elicit positive emotions. For example, when the generation AI receives a business idea, the business idea providing unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, when the user inputs an idea, the business idea providing unit analyzes emotions using a camera or microphone and provides positive feedback. Furthermore, when the user inputs a business idea, the business idea providing unit provides an interface for the generation AI to estimate emotions and elicit positive emotions. For example, it presents encouraging messages and success stories. Furthermore, when the generation AI receives a business idea, the business idea providing unit provides feedback in real time based on the emotion estimation data and offers advice to strengthen positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This makes it possible to provide feedback that takes the user's emotions into consideration, thereby increasing their motivation.

[0057] The data analysis unit collects relevant data not only from the database but also from social media and news articles, allowing for analysis from a broader perspective. For example, when the generative AI analyzes a business idea, the data analysis unit collects related posts and comments from social media and integrates them with the database for analysis. For example, it analyzes posts on Twitter and Facebook to reflect trends and user opinions. The data analysis unit also automatically collects relevant information from news articles, which the generative AI uses to analyze the business idea. For example, it extracts the latest market trends and competitor actions from news articles and reflects them in the analysis. The data analysis unit also allows the generative AI to analyze the business idea based on the data collected from social media and news articles, providing insights from a broader perspective. For example, it provides analysis results that reflect user interests and market changes. This enables data analysis from a broader perspective, allowing for more accurate advice.

[0058] The data analysis department can identify the factors for success and failure of each region by taking into account the cultural background and regional characteristics of the idea. For example, when the generation AI analyzes a business idea, the data analysis department considers the cultural background of each region and identifies the factors for success and failure. For example, it reflects consumer behavior and cultural customs in a specific region in the analysis. The data analysis department also performs data analysis that takes into account regional characteristics, allowing the generation AI to identify the factors for success and failure of the business idea. For example, it incorporates market needs and competitive situations in each region into the analysis. The data analysis department also collects data that takes into account cultural background and regional characteristics when the generation AI analyzes a business idea, and uses this data in the analysis. For example, it provides analysis results that reflect consumer preferences and trends in each region. This enables analysis that takes into account regional characteristics, allowing for more appropriate advice to be provided.

[0059] The business idea providing unit can accept input in different formats, such as voice input or visual notes. For example, the business idea providing unit adds a function to accept voice input when the generation AI receives a business idea. For example, a user can use a microphone to input an idea by voice, and the generation AI can analyze the voice and save it in a database. The business idea providing unit can also add a function to accept visual notes, and when the generation AI receives a business idea, it can analyze handwritten notes or sketches. For example, a user can upload handwritten notes, and the generation AI can digitize and analyze the contents. The business idea providing unit can also add a function to accept input in different formats, and when the generation AI receives a business idea, it can analyze the voice or visual notes and save them in a database. For example, a user can upload voice memos or visual notes, and the generation AI can analyze and save the contents. This allows for input in different formats, improving user convenience.

[0060] The data analysis unit can refer to databases from different industries across sectors and incorporate learnings from those sectors. For example, when the generative AI analyzes a business idea, the data analysis unit refers to databases from different industries and incorporates learnings from those sectors. For example, when analyzing ideas from the food and beverage industry, the data analysis unit also refers to data from the IT and medical industries. The data analysis unit also collects success and failure cases from other industries from the database, which the generative AI uses to analyze business ideas. For example, the success and failure factors from those sectors are reflected in the analysis. The data analysis unit also refers to databases from different industries across sectors and incorporates learnings from those sectors when the generative AI analyzes a business idea. For example, market trends and technology trends from those sectors are reflected in the analysis. Incorporating learnings from those sectors makes it possible to provide advice from a more multifaceted perspective.

[0061] The business idea providing unit can estimate the user's emotions in real time and provide advice based on those emotions. For example, when the generation AI receives a business idea, the business idea providing unit analyzes the user's facial expressions and voice to estimate their emotions in real time. For example, when the user inputs an idea, the business idea providing unit analyzes their emotions using a camera or microphone and provides advice based on those emotions. Furthermore, when the user inputs a business idea, the generation AI estimates their emotions and provides advice based on those emotions. For example, if the user is feeling anxious, an encouraging message is displayed. Furthermore, when the generation AI receives a business idea, the business idea providing unit provides feedback in real time based on the emotion estimation data and advice based on those emotions. For example, if the user has positive emotions, it presents success stories. This enables more appropriate support by providing advice based on the user's emotions.

[0062] The strategic recommendation unit can estimate the user's emotions and make recommendations based on those emotions. For example, when the generation AI provides strategic recommendations, the strategic recommendation unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user is feeling anxious, the strategic recommendation unit makes recommendations that provide a sense of security. Furthermore, when the user receives a strategic recommendation, the generation AI estimates the user's emotions and makes recommendations based on those emotions. For example, if the user is feeling positive, the strategic recommendation unit makes a challenging recommendation. Furthermore, when the generation AI provides strategic recommendations, the strategic recommendation unit provides feedback in real time based on the emotion estimation data and makes recommendations based on emotions. For example, if the user is feeling down, the strategic recommendation unit makes an encouraging recommendation. This allows for more appropriate strategic recommendations to be provided by making recommendations based on the user's emotions.

[0063] The Strategic Proposal Department can compare and analyze past success stories and failure stories to propose more specific improvement measures. For example, when the Generative AI provides strategic recommendations, the Strategic Proposal Department compares and analyzes past success stories and failure stories to propose specific improvement measures. For example, it compares the factors of success stories with the factors of failure stories to identify areas for improvement. The Strategic Proposal Department also collects past success stories and failure stories from a database, and the Generative AI performs a comparative analysis to propose specific improvement measures. For example, it analyzes the commonalities between success stories and failure stories to propose areas for improvement. The Strategic Proposal Department also compares and analyzes past success stories and failure stories to propose specific improvement measures when the Generative AI provides strategic recommendations. For example, it makes suggestions to incorporate the factors of success stories and avoid the factors of failure stories. In this way, more specific improvement measures can be proposed by comparing and analyzing past stories.

[0064] The strategic proposal department can simulate the feasibility of proposals and perform risk assessments. For example, when the generation AI provides a strategic recommendation, the strategic proposal department simulates the feasibility of proposals and performs risk assessments. For example, it evaluates the risks associated with executing the proposal and proposes risk mitigation measures. The strategic proposal department also builds a system that simulates the feasibility of proposals and allows the generation AI to perform risk assessments. For example, it simulates the resources and costs required to execute the proposal and evaluates the risks. The strategic proposal department also simulates the feasibility of proposals and performs risk assessments when the generation AI provides a strategic recommendation. For example, it evaluates the risks associated with executing the proposal and proposes risk mitigation measures. In this way, by simulating the feasibility of proposals and performing risk assessments, safer proposals can be made.

[0065] The Strategic Proposal Department can refer to success stories from different industries and incorporate learnings from those industries. For example, when the Generative AI provides strategic recommendations, the Strategic Proposal Department refers to success stories from different industries and incorporates learnings from those industries. For example, when analyzing ideas for the food and beverage industry, success stories from the IT and medical industries are referenced. The Strategic Proposal Department also collects success stories from other industries from a database, which the Generative AI uses in its strategic recommendations. For example, the success factors of those industries are analyzed and reflected in proposals. The Strategic Proposal Department also refers to success stories from different industries and incorporates learnings from those industries when the Generative AI provides strategic recommendations. For example, market trends and technology trends from those industries are reflected in the analysis. In this way, by incorporating learnings from those industries, it becomes possible to make proposals from a more multifaceted perspective.

[0066] The strategic proposal unit can estimate a user's emotions in real time and provide feedback based on the emotions. For example, when the generation AI provides a strategic recommendation, the strategic proposal unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user is feeling anxious, the strategic proposal unit provides feedback that gives a sense of security. Furthermore, when the user receives a strategic recommendation, the generation AI estimates emotions and provides feedback based on the emotions. For example, if the user has positive emotions, the strategic proposal unit provides challenging feedback. Furthermore, when the generation AI provides a strategic recommendation, the strategic proposal unit provides feedback based on emotions in real time based on the emotion estimation data. For example, if the user is feeling down, the strategic proposal unit provides encouraging feedback. This enables more appropriate support by providing feedback based on the user's emotions.

[0067] The success case analysis unit can estimate the user's emotions and make optimization suggestions based on those emotions. For example, when the generation AI analyzes a success case, the success case analysis unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user has positive emotions, the success case analysis unit makes suggestions that emphasize the factors behind the success case. Furthermore, when the user receives the analysis results of the success case, the generation AI estimates emotions and makes optimization suggestions based on those emotions. For example, if the user is feeling anxious, the success case analysis unit makes suggestions that give the user a sense of security. Furthermore, when the generation AI analyzes a success case, the success case analysis unit provides feedback in real time based on the emotion estimation data and makes optimization suggestions based on emotions. For example, if the user is feeling down, the success case analysis unit makes suggestions that encourage them. This enables more appropriate support by making optimization suggestions based on the user's emotions.

[0068] The success story analysis unit takes into account the cultural background and market characteristics behind success stories, allowing it to provide deeper insights. For example, when the generation AI analyzes success stories, the success story analysis unit takes cultural background into account and identifies the success factors. For example, consumer behavior and cultural customs in specific regions are reflected in the analysis. The success story analysis unit also performs data analysis that takes market characteristics into account, allowing the generation AI to identify the factors behind success stories. For example, the market needs and competitive situation for each region are incorporated into the analysis. The success story analysis unit also collects data that takes cultural background and market characteristics into account when the generation AI analyzes success stories, and uses this data in the analysis. For example, it provides analysis results that reflect consumer preferences and trends for each region. This makes it possible to conduct analysis that takes cultural background and market characteristics into account, allowing for deeper insights.

[0069] The success case analysis unit tracks the factors behind success cases over time, making it possible to clarify the process of success. For example, when the generation AI analyzes a success case, the success case analysis unit tracks the factors behind success over time and makes clear the process of success. For example, it analyzes each step of the success case over time and identifies the factors behind success. In addition, data on success cases is collected over time, and the generation AI uses that data to make clear the process of success. For example, it analyzes important events and decisions at each stage of the success case. In addition, when the generation AI analyzes a success case, the success case analysis unit tracks the factors behind success over time and makes clear the process of success. For example, it analyzes each step of the success case over time and identifies the factors behind success. In this way, by tracking the factors behind a success case over time, it is possible to make clear the process of success.

[0070] The success case analysis unit can compare success cases from different regions and cultural spheres and provide insights from a global perspective. For example, when the generation AI analyzes success cases, the success case analysis unit compares success cases from different regions and cultural spheres and provides insights from a global perspective. For example, it compares success factors from different regions and identifies similarities and differences. In addition, success cases from different cultural spheres are collected from a database, and the generation AI provides insights from a global perspective based on that data. For example, consumer behavior and market characteristics from different cultural spheres are reflected in the analysis. In addition, when the generation AI analyzes success cases, the success case analysis unit compares success cases from different regions and cultural spheres and provides insights from a global perspective. For example, it compares success factors from different regions and identifies similarities and differences. This makes it possible to provide insights from a global perspective by comparing success cases from different regions and cultural spheres.

[0071] The success case analysis unit can visualize success cases to enable users to intuitively understand them. For example, when the generation AI analyzes a success case, the success case analysis unit visualizes the success case to enable users to intuitively understand it. For example, it displays the factors behind the success case in graphs and charts. In addition, a tool for visualizing success cases is developed and used by the generation AI when analyzing success cases. For example, it displays the factors behind the success case as an infographic. In addition, when the generation AI analyzes a success case, the success case analysis unit visualizes the success case to enable users to intuitively understand it. For example, it displays the factors behind the success case in slide format. In this way, by visualizing the success case, users can intuitively understand it.

[0072] The success case analysis unit can estimate the user's emotions in real time and make optimization suggestions based on those emotions. For example, when the generation AI analyzes a success case, the success case analysis unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user has positive emotions, the success case analysis unit makes suggestions that emphasize the factors behind the success case. Furthermore, when the user receives the analysis results of the success case, the generation AI estimates emotions and makes optimization suggestions based on those emotions. For example, if the user is feeling anxious, the success case analysis unit makes suggestions that give the user a sense of security. Furthermore, when the generation AI analyzes a success case, the success case analysis unit provides feedback in real time based on the emotion estimation data and makes optimization suggestions based on emotions. For example, if the user is feeling down, the success case analysis unit makes suggestions that encourage them. This enables more appropriate support by making optimization suggestions based on the user's emotions.

[0073] The market monitoring unit can estimate the user's emotions and provide real-time advice based on those emotions. For example, when the generation AI monitors market changes, the market monitoring unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user is feeling anxious, the market monitoring unit provides advice that gives a sense of security. Furthermore, when the user responds to market changes, the generation AI estimates emotions and provides real-time advice based on those emotions. For example, if the user is feeling positive, the market monitoring unit provides challenging advice. Furthermore, when the generation AI monitors market changes, the market monitoring unit provides feedback in real time based on the emotion estimation data and provides real-time advice based on those emotions. For example, if the user is feeling down, the market monitoring unit provides encouraging advice. This enables more appropriate support by providing real-time advice based on the user's emotions.

[0074] The market monitoring unit also collects data from social media and news articles, allowing it to provide advice from a broader perspective. For example, when the generation AI monitors market changes, the market monitoring unit collects related posts and comments from social media and integrates them with a database to provide advice. For example, it analyzes posts on Twitter and Facebook to reflect trends and user opinions. The market monitoring unit also automatically collects related information from news articles, which the generation AI uses to provide advice in response to market changes. For example, it extracts the latest market trends and competitor actions from news articles and reflects them in the advice. The market monitoring unit also allows the generation AI to provide advice in response to market changes based on the data collected from social media and news articles. For example, it provides advice that reflects user interests and market changes. This enables advice to be provided from a broader perspective by collecting data from social media and news articles.

[0075] The market monitoring unit can track competitors' trends in real time and provide advice to maintain a competitive advantage. For example, when the generation AI monitors changes in the market, the market monitoring unit tracks competitors' trends in real time and provides advice to maintain a competitive advantage. For example, it analyzes the trends of competitors' new products and services and reflects this in the advice. The market monitoring unit also collects competitors' trends from a database and uses them to provide advice to the generation AI to respond to market changes. For example, it analyzes competitors' marketing strategies and pricing and reflects this in the advice. The market monitoring unit can also track competitors' trends in real time when the generation AI monitors changes in the market and provide advice to maintain a competitive advantage. For example, it analyzes the trends of competitors' new products and services and reflects this in the advice. This makes it possible to provide advice to maintain a competitive advantage by tracking competitors' trends in real time.

[0076] The market monitoring department can refer to market trends in different industries and incorporate learnings from those industries. For example, when the generation AI monitors market changes, the market monitoring department refers to market trends in different industries and incorporates learnings from those industries. For example, when analyzing market trends in the food and beverage industry, the market trends in the IT and medical industries are referenced. The market monitoring department also collects market trends in other industries from a database, which the generation AI uses to provide advice in response to market changes. For example, it reflects market trends and technology trends in those industries in its analysis. The market monitoring department also refers to market trends in different industries and incorporates learnings from those industries when the generation AI monitors market changes. For example, it reflects market trends and technology trends in those industries in its analysis. This allows the generation AI to incorporate learnings from those industries, making it possible to provide advice from a more multifaceted perspective.

[0077] The market monitoring unit can visualize the advice content to enable users to intuitively understand it. For example, when the generation AI monitors market changes, the market monitoring unit visualizes the advice content to enable users to intuitively understand it. For example, the advice content is displayed in graphs and charts. In addition, a tool for visualizing the advice content is developed and used when the generation AI provides advice in response to market changes. For example, the advice content is displayed as an infographic. In addition, when the generation AI monitors market changes, the market monitoring unit visualizes the advice content to enable users to intuitively understand it. For example, the advice content is displayed in a slide format. In this way, visualizing the advice content allows users to intuitively understand it.

[0078] The market monitoring unit can estimate a user's emotions in real time and provide advice based on those emotions. For example, when the generation AI monitors market changes, the market monitoring unit analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user is feeling anxious, the market monitoring unit provides advice that gives a sense of security. Furthermore, when the user responds to market changes, the generation AI estimates emotions and provides advice based on those emotions. For example, if the user is feeling positive, the market monitoring unit provides challenging advice. Furthermore, when the generation AI monitors market changes, the market monitoring unit provides feedback in real time based on the emotion estimation data and provides advice based on those emotions. For example, if the user is feeling down, the market monitoring unit provides encouraging advice. This enables more appropriate support by providing advice based on the user's emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The consultation system can further include a history analysis unit that analyzes the user's past behavioral history and provides personalized advice. For example, the history analysis unit analyzes business ideas submitted by the user in the past and their results to identify patterns of success and failure. The history analysis unit can also provide more personalized advice based on the user's past feedback and evaluations. Furthermore, the history analysis unit can track the user's past behavioral history chronologically and propose long-term growth strategies. This makes it possible to provide advice that takes the user's past behavioral history into consideration, resulting in more accurate support.

[0081] The consultation system can also estimate the user's emotions and introduce gamification elements to increase the user's motivation based on the estimated emotions. For example, if the user has positive emotions, a system can be provided that allows them to earn points or badges. Also, if the user is feeling anxious, motivation can be increased by presenting encouraging messages or success stories. Furthermore, challenges and goals can be set according to the user's emotions, and feedback can be provided that gives the user a sense of accomplishment. In this way, by introducing gamification elements based on the user's emotions, users can be motivated and more proactively move forward with their business ideas.

[0082] The consultation system can also estimate the user's emotions and provide relaxation content to reduce the user's stress based on the estimated emotions. For example, if the user is feeling stressed, relaxation music or a meditation guide can be provided. Also, if the user is feeling anxious, a video or animation with a relaxing effect can be displayed. Furthermore, relaxation exercises and breathing techniques can be provided according to the user's emotions. In this way, by providing relaxation content based on the user's emotions, the user's stress can be reduced and the business idea can be advanced in a more relaxed state.

[0083] The consultation system can further estimate the user's emotions and provide a community function for sharing the user's emotions based on the estimated emotions. For example, if a user has positive emotions, a forum or chat room can be provided for sharing success stories with other users. Also, if a user is feeling anxious, a support group can be provided where the user can receive encouragement and advice from other users. Furthermore, a blog or diary function for sharing emotions can be provided depending on the user's emotions. In this way, by providing a community function based on the user's emotions, it is possible to promote support and empathy among users and support the realization of better business ideas.

[0084] The consultation system can also estimate the user's emotions and provide a dashboard to visualize the user's emotions based on the estimated emotions. For example, if the user is feeling positive, the system can display changes in emotions in graphs and charts. Also, if the user is feeling anxious, the system can track changes in emotions over time and identify areas for improvement. Furthermore, the system can visualize changes in emotions according to the user's emotions, allowing the user to intuitively understand them. By visualizing the user's emotions, it becomes easier to grasp changes in emotions and provide more appropriate support.

[0085] The consultation system can also provide a peer review function that allows users to share their business ideas with other users and receive feedback. For example, a user can post a business idea and receive comments and ratings from other users. The peer review function also allows users to provide feedback on other users' business ideas. Furthermore, the peer review function can help users generate new ideas by referring to the business ideas of other users. This allows the quality of business ideas to be improved through mutual feedback between users.

[0086] The consultation system can further provide a fundraising support function to help users realize their business ideas. For example, by linking with a crowdfunding platform, users can publish their business ideas and raise funds. The fundraising support function can also provide a matching service that allows users to directly contact investors and sponsors. Furthermore, the fundraising support function can also support users in creating presentation materials and business plans for fundraising. This allows users to efficiently raise funds to realize their business ideas.

[0087] The consultation system can further provide a partnership support function to realize the user's business idea. For example, it can provide a service that matches the user with companies and experts that provide the technology and services the user needs. The partnership support function can also provide collaboration tools that allow the user to work on projects together with other companies and experts. Furthermore, the partnership support function can support the user in creating contracts and agreements to form partnerships. This allows the user to find appropriate partners to realize their business idea and collaborate efficiently.

[0088] The consultation system may further provide a marketing support function to help users realize their business ideas. For example, the system may provide tools to help users identify target markets and develop effective marketing strategies. The marketing support function may also support users in planning and executing advertising campaigns. Furthermore, the marketing support function may provide advice to help users spread their business ideas using social media and online platforms. This may help users develop effective marketing strategies and lead their business ideas to success.

[0089] The consultation system can also provide legal support functions to help users realize their business ideas. For example, it can support users in going through the legal procedures necessary when starting a business. The legal support function can also provide advice when users are drafting contracts and agreements. Furthermore, the legal support function can support users in the procedures for protecting their intellectual property rights. This allows users to clear legal issues and proceed with their business with peace of mind.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The business idea submission department receives business ideas provided by the customer. For example, if a customer has an idea for a new restaurant, the business idea submission department can receive that idea. The business idea submission department can also receive business model and product ideas provided by the customer. Step 2: The Data Analysis Department analyzes the business ideas provided by the Business Idea Provider. For example, the Data Analysis Department collects data on similar services from historical databases and analyzes them. The Data Analysis Department can also collect related data from social media and news articles to conduct analysis from a broader perspective. Step 3: The Strategic Recommendation Department provides strategic recommendations based on the data analyzed by the Data Analysis Department. For example, the Strategic Recommendation Department analyzes data from a failed service and provides specific improvement measures and market positioning. The Strategic Recommendation Department can also simulate the feasibility of proposals and perform risk assessments. Step 4: The success case analysis department identifies the success factors based on the success cases analyzed by the data analysis department. For example, the success case analysis department analyzes successful service cases and identifies the factors that led to their success. The success case analysis department can also track the factors that led to success over time and clarify the process of success. Step 5: The Market Monitoring Department monitors market changes and the competitive landscape and provides real-time advice. For example, the Market Monitoring Department monitors recent market trends and competitor activity and provides real-time advice. The Market Monitoring Department can also collect data from social media and news articles to provide advice from a broader perspective.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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 business idea provision department that provides business ideas; a data analysis unit that analyzes the business idea provided by the business idea providing unit; a strategic recommendation unit that provides strategic recommendations based on the data analyzed by the data analysis unit; a success case analysis unit that identifies success factors based on the success cases analyzed by the data analysis unit; A market monitoring department monitors market changes and competitive situations and updates advice in real time. A system characterized by:

2. The business idea providing department: Estimate user emotions and provide feedback to elicit positive emotions 2. The system of claim 1.

3. The data analysis unit Collect relevant data not only from databases but also from social media and news articles to conduct analysis from a broader perspective 2. The system of claim 1.

4. The data analysis unit Consider the cultural background and regional characteristics of the idea and identify the factors that will make it successful or not in each region.

2. The system of claim 1.

5. The business idea providing department: Accept different forms of input, such as voice input and visual notes 2. The system of claim 1.

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