System

Abstract

An object of the system according to the embodiment is to evaluate market suitability of a business idea of a user and provide a specific action plan.SOLUTION: A system includes an idea input part, a market evaluation part, a customer specification part, and a competition analysis part. The idea input unit inputs an idea of a user. A market evaluation part evaluates the market needs on the basis of the idea input by the idea input part. A customer specification part specifies a target customer layer on the basis of the market needs evaluated by the market evaluation part. The competition analysis unit analyzes a situation of a competitor based on the target customer layer specified by the customer specification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for small and medium-sized enterprises and entrepreneurs to evaluate the market relevance of their business ideas and obtain concrete action plans.

[0005] The system according to the embodiment aims to evaluate the market suitability of a user's business idea and provide a concrete action plan. [Means for solving the problem]

[0006] The system according to the embodiment includes an idea input unit, a market evaluation unit, a customer identification unit, and a competitive analysis unit. The idea input unit inputs a user's idea. The market evaluation unit evaluates market needs based on the idea input by the idea input unit. The customer identification unit identifies a target customer segment based on the market needs evaluated by the market evaluation unit. The competitive analysis unit analyzes the status of competitors based on the target customer segment identified by the customer identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the market relevance of a user's business idea and provide a specific action plan. [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 market suitability assessment system according to an embodiment of the present invention is a system that evaluates the market needs, target customer segments, and the status of competitors for a side business idea input by a user, and provides a specific action plan. In this way, the market suitability assessment system allows the user to evaluate the market suitability of their own side business idea and receive a specific action plan.

[0029] A market suitability assessment system according to an embodiment includes an idea input unit, a market evaluation unit, a customer identification unit, and a competitive analysis unit. The idea input unit allows a user to input an outline of an idea. For example, the user may input an idea such as "I want to launch a new online learning platform." The idea input unit also allows a generation AI to search a relevant patent database and compare it with existing patent information to evaluate its novelty. For example, the generation AI may search the patent database, extract similar patents, and evaluate their novelty. The market evaluation unit evaluates market needs based on the idea input by the idea input unit. For example, the generation AI may analyze past market data and trends to determine the current market demand for the idea. The customer identification unit identifies a target customer segment based on the market needs evaluated by the market evaluation unit. For example, the generation AI may analyze demographic data and consumer behavior data to identify which customer segments would be interested in the idea. The competitive analysis unit analyzes the status of competitors based on the target customer segment identified by the customer identification unit. For example, the generation AI may research similar services and products on the market and evaluate their strengths and weaknesses. As a result, the market suitability assessment system according to the embodiment allows users to assess the market suitability of their own side business ideas and receive specific action plans.

[0030] The idea input unit allows the generation AI to search a relevant patent database for the outline of an idea entered by the user and compare it with existing patent information to evaluate its novelty. For example, the idea input unit allows the generation AI to search a patent database based on the outline of an idea entered by the user and extract similar patents. For example, it compares existing patent information with ideas from an online learning platform to evaluate their novelty. The generation AI also searches a patent database and analyzes the technical features of patents similar to the user's idea. For example, it evaluates the technical novelty of the idea based on patent claims and drawings. The generation AI also generates a report evaluating the novelty of the user's idea based on the information extracted from the patent database. For example, it shows the idea's uniqueness and technical advantages compared to existing patents. This allows the novelty of the user's idea to be evaluated.

[0031] When a user inputs an outline of an idea, the idea input unit allows the generation AI to automatically search for related technical literature and research papers to provide technical support. For example, when a user inputs an outline of an idea, the idea input unit allows the generation AI to automatically search for related technical literature and research papers to provide technical support for the idea. For example, it may present papers that explain the technical background of online learning platforms. The generation AI may also analyze technical literature and research papers to extract technical knowledge related to the user's idea. For example, it may evaluate the technical feasibility of the idea based on the latest technological trends and research results. The generation AI may also generate a report that provides technical support for the user's idea based on information from technical literature and research papers. For example, it may show the feasibility and application examples of related technologies. This allows for technical support for the user's idea.

[0032] The market assessment unit can analyze not only past market data but also real-time social media data to make an assessment that reflects the latest market trends. In the market assessment unit, for example, the generation AI analyzes past market data and real-time social media data to make an assessment that reflects the latest market trends. For example, the demand for an online learning platform is analyzed from social media posts. The generation AI also evaluates market needs based on the social media data. For example, the frequency of appearance of specific keywords and hashtags is analyzed to evaluate increases in demand. The generation AI also analyzes real-time social media data to generate an assessment report that reflects the latest market trends. For example, it provides graphs and charts showing trends related to online learning. This makes it possible to make an assessment that reflects the latest market trends.

[0033] The market assessment unit can provide a demand forecast for each region, taking into account the market characteristics of each region. In the market assessment unit, for example, the generation AI takes into account the market characteristics of each region and provides a demand forecast for each region. For example, the demand for the online learning platform is analyzed by region, and regions with high demand are identified. The generation AI also performs a demand forecast based on market data for each region. For example, the penetration rate and growth rate of online learning in a specific region are analyzed to perform a demand forecast. The generation AI also generates a demand forecast report that takes into account the market characteristics of each region. For example, graphs and charts showing the market needs and competitive situation for each region are provided. This makes it possible to provide a demand forecast for each region.

[0034] The customer identification unit can analyze not only demographic data but also consumer lifestyle data to identify more detailed target customer segments. For example, in the customer identification unit, the generation AI analyzes demographic data and consumer lifestyle data to identify detailed target customer segments. For example, the target customer segment for an online learning platform is identified based on data such as age, occupation, and hobbies. The generation AI also creates detailed profiles of the target customer segment based on the lifestyle data. For example, it analyzes the tendency of consumers with specific lifestyles to be interested in online learning. The generation AI also integrates the demographic data and lifestyle data to generate reports that identify the target customer segment in detail. For example, it shows the market needs and behavioral patterns of customers with specific lifestyles. This makes it possible to identify more detailed target customer segments.

[0035] The customer identification unit can analyze consumer purchase history data and identify target customer segments based on purchasing behavior. In the customer identification unit, for example, the generation AI analyzes consumer purchase history data and identifies target customer segments based on purchasing behavior. For example, the target customer segment is identified based on data on consumers who have used online learning services in the past. The generation AI also creates a detailed profile of the target customer segment based on the purchase history data. For example, it analyzes the tendency of consumers with specific purchasing patterns to be interested in online learning. The generation AI also analyzes the purchase history data and generates a report that identifies the target customer segment. For example, it shows the market needs and behavioral patterns of customers with specific purchasing behavior. This makes it possible to identify target customer segments based on purchasing behavior.

[0036] The competitive analysis unit can analyze competitors' financial data and evaluate their economic strengths and weaknesses. In the competitive analysis unit, for example, the generation AI analyzes competitors' financial data and evaluates their economic strengths and weaknesses. For example, it analyzes the revenue and profit margins of competitors in the online learning platform industry. Furthermore, based on the financial data, the generation AI generates a report that evaluates the competitors' economic strengths and weaknesses. For example, it provides graphs and charts showing the competitors' financial health and growth rates. Furthermore, the generation AI analyzes the competitors' financial data and provides indicators for evaluating their economic strengths and weaknesses. For example, it calculates an evaluation score based on the competitors' financial indicators. This makes it possible to evaluate the competitors' economic strengths and weaknesses.

[0037] The competitive analysis unit can analyze customer reviews and ratings of competitors and perform evaluations based on customer satisfaction. In the competitive analysis unit, for example, the generation AI analyzes customer reviews and ratings of competitors and performs evaluations based on customer satisfaction. For example, the generation AI analyzes customer reviews of competitors of an online learning platform and calculates a satisfaction score. Furthermore, based on the customer review and evaluation data, the generation AI generates a report evaluating the customer satisfaction of competitors. For example, it provides graphs and charts showing the customer satisfaction of competitors. Furthermore, the generation AI analyzes customer reviews and ratings of competitors and provides indicators for evaluations based on customer satisfaction. For example, it performs evaluations based on the customer satisfaction scores of competitors. This makes it possible to perform evaluations based on customer satisfaction.

[0038] The competitive analysis unit can classify the results of competitor situation analysis by different market segments and propose competitive strategies for each segment. In the competitive analysis unit, for example, the generation AI classifies the results of competitor situation analysis by different market segments and proposes competitive strategies for each segment. For example, competitors of online learning platforms are classified into the education market, the corporate training market, etc. The generation AI also analyzes the results of competitor situation analysis by market segment and proposes competitive strategies for each segment. For example, it proposes a pricing strategy for the education market and a promotion strategy for the corporate training market. The generation AI also generates a report that classifies the results of competitor situation analysis by different market segments. For example, it provides graphs and charts showing the strengths and weaknesses of competitors in each segment. This makes it possible to propose competitive strategies for different market segments.

[0039] The competitive analysis unit can visualize the results of competitor situation analysis to enable the user to intuitively understand. In the competitive analysis unit, for example, the generation AI visualizes the results of the competitor situation analysis to enable the user to intuitively understand. For example, it provides graphs and charts showing the strengths and weaknesses of competitors of an online learning platform. Furthermore, based on the visualized results of the competitor situation analysis, the generation AI provides the user with an easy-to-understand report. For example, it displays pie charts and trend graphs showing the market share and growth rate of competitors. Furthermore, the generation AI visualizes the results of the competitor situation analysis to provide an interface that the user can intuitively understand. For example, it displays competitor data using an interactive dashboard. This makes it possible to visualize the results of the competitor situation analysis to enable an intuitive understanding.

[0040] The pricing strategy proposal unit uses the generation AI to analyze market data and competitors' pricing, and propose the optimal price range. For example, the generation AI may analyze market data and competitors' pricing, and propose the optimal price range. For example, the unit may propose the monthly fee for an online learning platform by comparing it with other competing services. The generation AI may also propose a pricing strategy based on market data. For example, the unit may set prices taking into account the balance between supply and demand. The generation AI may also analyze competitors' pricing, and generate a report proposing the optimal price range. For example, the generation AI may provide graphs and charts showing competitors' pricing strategies. This makes it possible to propose the optimal price range.

[0041] The pricing strategy proposal unit can propose regional pricing strategies by taking into account the market characteristics of each region. In the pricing strategy proposal unit, for example, the generation AI considers the market characteristics of each region and proposes regional pricing strategies. For example, the price of the online learning platform is set according to the purchasing power and competitive situation of each region. The generation AI also proposes a pricing strategy based on market data for each region. For example, it analyzes the demand and competitive situation in a specific region and sets the optimal price. The generation AI also generates a pricing strategy report that considers the market characteristics of each region. For example, it provides graphs and charts showing the pricing strategy for each region. This makes it possible to propose regional pricing strategies.

[0042] The promotion proposal unit allows the generation AI to analyze past promotion data and marketing trends and propose optimal promotional methods. For example, the generation AI analyzes past promotional data and marketing trends and proposes optimal promotional methods. For example, in promoting an online learning platform, the generation AI proposes social media advertising. The generation AI also proposes promotional methods based on promotional data. For example, it proposes promotion strategies based on past success stories. The generation AI also analyzes marketing trends and generates reports proposing optimal promotional methods. For example, it provides graphs and charts showing the latest marketing trends. This makes it possible to propose optimal promotional methods.

[0043] The promotion proposal unit can propose a promotion strategy for each region, taking into account the market characteristics of each region. In the promotion proposal unit, for example, the generation AI takes into account the market characteristics of each region and proposes a promotion strategy for each region. For example, the promotion of an online learning platform is set according to the purchasing power and competitive situation of each region. The generation AI also proposes a promotion strategy based on market data for each region. For example, it analyzes the demand and competitive situation in a specific region and proposes the optimal promotional method. The generation AI also generates a promotion strategy report that takes into account the market characteristics of each region. For example, it provides graphs and charts showing the promotion strategy for each region. This makes it possible to propose a promotion strategy for each region.

[0044] The promotion proposal unit can classify the results of the proposed promotion methods by different industries and uses and propose more specific promotion strategies. For example, the generation AI in the promotion proposal unit classifies the results of the proposed promotion methods by different industries and uses and proposes specific promotion strategies. For example, the promotion of an online learning platform is classified into the education industry and corporate training uses. The generation AI also analyzes the results of the proposed promotion methods by industry and use and proposes specific promotion strategies. For example, it proposes a promotion strategy for the education industry and a promotion strategy for corporate training uses. The generation AI also generates a report that classifies the results of the proposed promotion methods by different industries and uses. For example, it provides graphs and charts showing promotion strategies for each industry. This makes it possible to propose specific promotion strategies for different industries and uses.

[0045] The promotion proposal unit can visualize the results of the proposed promotion methods so that the user can intuitively understand them. In the promotion proposal unit, for example, the generation AI visualizes the results of the proposed promotion methods so that the user can intuitively understand them. For example, it provides graphs and charts showing the promotion strategy of an online learning platform. Furthermore, based on the visualized results of the proposed promotion methods, the generation AI provides the user with an easy-to-understand report. For example, it displays a graph showing the trend of the promotion strategy or a pie chart showing market share. Furthermore, the generation AI visualizes the results of the proposed promotion methods so that the user can intuitively understand them. For example, it displays data on the promotion strategy using an interactive dashboard. This makes it possible to visualize the results of the proposed promotion methods so that they can be intuitively understood.

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

[0047] The market fit assessment system can further include an experience assessment unit that takes into account the user's past business experience. The experience assessment unit analyzes data on the user's past business successes and failures and assesses the applicability of those successes to the current idea. For example, it analyzes the user's past successful business models and marketing strategies and suggests how they can be applied to the current idea. It also provides advice on how to avoid making the same mistakes based on past failures. This makes it possible to assess market fit by taking advantage of the user's past experience.

[0048] The market conformance assessment system can further include an environmental impact assessment unit. The environmental impact assessment unit assesses the environmental impact of the user's idea and provides advice from a sustainability perspective. For example, it calculates the carbon dioxide emissions associated with realizing the idea and suggests environmentally friendly alternatives. It also suggests environmentally friendly improvements, such as using recyclable materials and improving energy efficiency. This enables environmentally friendly market conformance assessment.

[0049] The market suitability assessment system can further include a cultural suitability assessment unit. The cultural suitability assessment unit evaluates the degree to which a user's idea will be accepted in different cultural spheres. For example, it analyzes the cultural background and consumer preferences of a specific region or country to evaluate the applicability of the idea. It also proposes marketing strategies and promotion methods that take cultural differences into account. This makes it possible to evaluate market suitability in different cultural spheres.

[0050] The market fit assessment system can further include a technical fit assessment unit. The technical fit assessment unit evaluates how well the user's idea fits with current technological trends and technical constraints. For example, it evaluates the technical feasibility of the idea based on the latest technological trends and research results. It also proposes alternative technologies and improvements to overcome technical constraints. This enables market fit assessment from a technical perspective.

[0051] The market suitability assessment system can further include a legal and regulatory suitability assessment unit. The legal and regulatory suitability assessment unit evaluates the degree to which a user's idea complies with relevant laws and regulations. For example, it analyzes laws and regulations and compliance requirements in a specific industry or region to evaluate the applicability of the idea. It also suggests improvements and necessary procedures for complying with laws and regulations. This enables market suitability assessment from the perspective of laws and regulations.

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

[0053] Step 1: In the idea input section, the user inputs an outline of their idea. For example, the user inputs an idea such as "I want to launch a new online learning platform." The idea input section also allows the generation AI to search relevant patent databases and compare the idea with existing patent information to evaluate its novelty. For example, the generation AI searches a patent database, extracts similar patents, and evaluates their novelty. Step 2: The market evaluation unit evaluates market needs based on the idea input by the idea input unit. For example, the generation AI analyzes past market data and trends to determine how much demand there is for the idea in the current market. Step 3: The Customer Identification Department identifies the target customer segment based on the market needs assessed by the Market Assessment Department. For example, the Generative AI analyzes demographic data and consumer behavior data to identify what type of customer segment would be interested in the idea. Step 4: The competitive analysis department analyzes the competitive landscape based on the target customer segments identified by the customer identification department. For example, the generative AI researches similar services and products on the market and evaluates their strengths and weaknesses.

[0054] (Example 2) The market suitability assessment system according to an embodiment of the present invention is a system that evaluates the market needs, target customer segments, and the status of competitors for a side business idea input by a user, and provides a specific action plan. In this way, the market suitability assessment system allows the user to evaluate the market suitability of their own side business idea and receive a specific action plan.

[0055] A market suitability assessment system according to an embodiment includes an idea input unit, a market evaluation unit, a customer identification unit, and a competitive analysis unit. The idea input unit allows a user to input an outline of an idea. For example, the user may input an idea such as "I want to launch a new online learning platform." The idea input unit also allows a generation AI to search a relevant patent database and compare it with existing patent information to evaluate its novelty. For example, the generation AI may search the patent database, extract similar patents, and evaluate their novelty. The market evaluation unit evaluates market needs based on the idea input by the idea input unit. For example, the generation AI may analyze past market data and trends to determine the current market demand for the idea. The customer identification unit identifies a target customer segment based on the market needs evaluated by the market evaluation unit. For example, the generation AI may analyze demographic data and consumer behavior data to identify which customer segments would be interested in the idea. The competitive analysis unit analyzes the status of competitors based on the target customer segment identified by the customer identification unit. For example, the generation AI may research similar services and products on the market and evaluate their strengths and weaknesses. As a result, the market suitability assessment system according to the embodiment allows users to assess the market suitability of their own side business ideas and receive specific action plans.

[0056] The idea input unit allows the generation AI to search a relevant patent database for the outline of an idea entered by the user and compare it with existing patent information to evaluate its novelty. For example, the idea input unit allows the generation AI to search a patent database based on the outline of an idea entered by the user and extract similar patents. For example, it compares existing patent information with ideas from an online learning platform to evaluate their novelty. The generation AI also searches a patent database and analyzes the technical features of patents similar to the user's idea. For example, it evaluates the technical novelty of the idea based on patent claims and drawings. The generation AI also generates a report evaluating the novelty of the user's idea based on the information extracted from the patent database. For example, it shows the idea's uniqueness and technical advantages compared to existing patents. This allows the novelty of the user's idea to be evaluated.

[0057] When a user inputs an outline of an idea, the idea input unit allows the generation AI to automatically search for related technical literature and research papers to provide technical support. For example, when a user inputs an outline of an idea, the idea input unit allows the generation AI to automatically search for related technical literature and research papers to provide technical support for the idea. For example, it may present papers that explain the technical background of online learning platforms. The generation AI may also analyze technical literature and research papers to extract technical knowledge related to the user's idea. For example, it may evaluate the technical feasibility of the idea based on the latest technological trends and research results. The generation AI may also generate a report that provides technical support for the user's idea based on information from technical literature and research papers. For example, it may show the feasibility and application examples of related technologies. This allows for technical support for the user's idea.

[0058] The idea input unit uses the emotion estimation function to analyze the emotions expressed when a user inputs an idea and can provide feedback in real time to elicit positive emotions. In the idea input unit, for example, when a user inputs an idea, the generation AI uses the emotion estimation function to analyze the user's emotions in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function also provides feedback to elicit positive emotions when the user inputs an idea. For example, it presents encouraging messages and success stories. Furthermore, based on the user's emotion data, the generation AI provides feedback in real time to provide advice that will strengthen positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This makes it possible to provide feedback to elicit positive emotions from the user.

[0059] The market assessment unit can analyze not only past market data but also real-time social media data to make an assessment that reflects the latest market trends. In the market assessment unit, for example, the generation AI analyzes past market data and real-time social media data to make an assessment that reflects the latest market trends. For example, the demand for an online learning platform is analyzed from social media posts. The generation AI also evaluates market needs based on the social media data. For example, the frequency of appearance of specific keywords and hashtags is analyzed to evaluate increases in demand. The generation AI also analyzes real-time social media data to generate an assessment report that reflects the latest market trends. For example, it provides graphs and charts showing trends related to online learning. This makes it possible to make an assessment that reflects the latest market trends.

[0060] The market assessment unit can provide a demand forecast for each region, taking into account the market characteristics of each region. In the market assessment unit, for example, the generation AI takes into account the market characteristics of each region and provides a demand forecast for each region. For example, the demand for the online learning platform is analyzed by region, and regions with high demand are identified. The generation AI also performs a demand forecast based on market data for each region. For example, the penetration rate and growth rate of online learning in a specific region are analyzed to perform a demand forecast. The generation AI also generates a demand forecast report that takes into account the market characteristics of each region. For example, graphs and charts showing the market needs and competitive situation for each region are provided. This makes it possible to provide a demand forecast for each region.

[0061] The market assessment unit can use the emotion estimation function to analyze consumer emotion data and perform market assessment that reflects emotional needs. In the market assessment unit, for example, the generation AI uses the emotion estimation function to analyze consumer emotion data and perform market assessment that reflects emotional needs. For example, consumer emotion toward online learning is analyzed and rising demand is evaluated. Furthermore, the generation AI evaluates market needs based on the emotion data. For example, ideas that have strong positive consumer emotion are evaluated preferentially. Furthermore, the generation AI analyzes the emotion data and generates a market assessment report that reflects emotional needs. For example, a graph or chart showing consumer emotion scores is provided. This makes it possible to perform market assessment that reflects emotional needs.

[0062] The customer identification unit can analyze not only demographic data but also consumer lifestyle data to identify more detailed target customer segments. For example, in the customer identification unit, the generation AI analyzes demographic data and consumer lifestyle data to identify detailed target customer segments. For example, the target customer segment for an online learning platform is identified based on data such as age, occupation, and hobbies. The generation AI also creates detailed profiles of the target customer segment based on the lifestyle data. For example, it analyzes the tendency of consumers with specific lifestyles to be interested in online learning. The generation AI also integrates the demographic data and lifestyle data to generate reports that identify the target customer segment in detail. For example, it shows the market needs and behavioral patterns of customers with specific lifestyles. This makes it possible to identify more detailed target customer segments.

[0063] The customer identification unit can analyze consumer purchase history data and identify target customer segments based on purchasing behavior. In the customer identification unit, for example, the generation AI analyzes consumer purchase history data and identifies target customer segments based on purchasing behavior. For example, the target customer segment is identified based on data on consumers who have used online learning services in the past. The generation AI also creates a detailed profile of the target customer segment based on the purchase history data. For example, it analyzes the tendency of consumers with specific purchasing patterns to be interested in online learning. The generation AI also analyzes the purchase history data and generates a report that identifies the target customer segment. For example, it shows the market needs and behavioral patterns of customers with specific purchasing behavior. This makes it possible to identify target customer segments based on purchasing behavior.

[0064] The customer identification unit can use the emotion estimation function to analyze the emotion data of the target customer segment and propose a marketing strategy to elicit emotional empathy. In the customer identification unit, for example, the generation AI uses the emotion estimation function to analyze the emotion data of the target customer segment and propose a marketing strategy to elicit emotional empathy. For example, in the promotion of an online learning platform, the generation AI proposes a marketing strategy to elicit emotional empathy from the target customer segment based on the emotion data. For example, the generation AI analyzes the emotion score and creates advertising copy to elicit empathy. The generation AI also analyzes the emotion data and generates a marketing strategy report to elicit emotional empathy from the target customer segment. For example, the generation AI proposes a marketing strategy based on the emotion score. This makes it possible to propose a marketing strategy to elicit emotional empathy.

[0065] The competitive analysis unit can analyze competitors' financial data and evaluate their economic strengths and weaknesses. In the competitive analysis unit, for example, the generation AI analyzes competitors' financial data and evaluates their economic strengths and weaknesses. For example, it analyzes the revenue and profit margins of competitors in the online learning platform industry. Furthermore, based on the financial data, the generation AI generates a report that evaluates the competitors' economic strengths and weaknesses. For example, it provides graphs and charts showing the competitors' financial health and growth rates. Furthermore, the generation AI analyzes the competitors' financial data and provides indicators for evaluating their economic strengths and weaknesses. For example, it calculates an evaluation score based on the competitors' financial indicators. This makes it possible to evaluate the competitors' economic strengths and weaknesses.

[0066] The competitive analysis unit can analyze customer reviews and ratings of competitors and perform evaluations based on customer satisfaction. In the competitive analysis unit, for example, the generation AI analyzes customer reviews and ratings of competitors and performs evaluations based on customer satisfaction. For example, the generation AI analyzes customer reviews of competitors of an online learning platform and calculates a satisfaction score. Furthermore, based on the customer review and evaluation data, the generation AI generates a report evaluating the customer satisfaction of competitors. For example, it provides graphs and charts showing the customer satisfaction of competitors. Furthermore, the generation AI analyzes customer reviews and ratings of competitors and provides indicators for evaluations based on customer satisfaction. For example, it performs evaluations based on the customer satisfaction scores of competitors. This makes it possible to perform evaluations based on customer satisfaction.

[0067] The competitive analysis unit can use the emotion estimation function to extract emotion data from competitors' customer reviews and evaluate their emotional strengths and weaknesses. In the competitive analysis unit, for example, the generation AI uses the emotion estimation function to extract emotion data from competitors' customer reviews and evaluate their emotional strengths and weaknesses. For example, the generation AI analyzes competitors' customer reviews on an online learning platform and calculates an emotion score. Furthermore, based on the emotion data, the generation AI generates a report evaluating the competitors' emotional strengths and weaknesses. For example, the generation AI provides graphs and charts showing the positive and negative emotions in the competitors' customer reviews. Furthermore, the generation AI extracts emotion data from competitors' customer reviews and provides indicators for evaluating their emotional strengths and weaknesses. For example, the generation AI performs an evaluation based on the competitors' emotion scores. This makes it possible to evaluate their emotional strengths and weaknesses.

[0068] The competitive analysis unit can classify the results of competitor situation analysis by different market segments and propose competitive strategies for each segment. In the competitive analysis unit, for example, the generation AI classifies the results of competitor situation analysis by different market segments and proposes competitive strategies for each segment. For example, competitors of online learning platforms are classified into the education market, the corporate training market, etc. The generation AI also analyzes the results of competitor situation analysis by market segment and proposes competitive strategies for each segment. For example, it proposes a pricing strategy for the education market and a promotion strategy for the corporate training market. The generation AI also generates a report that classifies the results of competitor situation analysis by different market segments. For example, it provides graphs and charts showing the strengths and weaknesses of competitors in each segment. This makes it possible to propose competitive strategies for different market segments.

[0069] The competitive analysis unit can visualize the results of competitor situation analysis to enable the user to intuitively understand. In the competitive analysis unit, for example, the generation AI visualizes the results of the competitor situation analysis to enable the user to intuitively understand. For example, it provides graphs and charts showing the strengths and weaknesses of competitors of an online learning platform. Furthermore, based on the visualized results of the competitor situation analysis, the generation AI provides the user with an easy-to-understand report. For example, it displays pie charts and trend graphs showing the market share and growth rate of competitors. Furthermore, the generation AI visualizes the results of the competitor situation analysis to provide an interface that the user can intuitively understand. For example, it displays competitor data using an interactive dashboard. This makes it possible to visualize the results of the competitor situation analysis to enable an intuitive understanding.

[0070] The competitor analysis unit can use the emotion estimation function to analyze the emotions the user feels about the results of a competitor's situation analysis and make suggestions to elicit positive emotions. In the competitor analysis unit, for example, the generation AI uses the emotion estimation function to analyze the emotions the user feels about the results of a competitor's situation analysis and make suggestions to elicit positive emotions. For example, the generation AI analyzes the user's emotion score about the analysis results. Furthermore, based on the emotion data, the generation AI makes suggestions to elicit positive emotions from the user. For example, it provides positive feedback and encouraging messages about the analysis results. Furthermore, the generation AI analyzes the emotion data and makes suggestions to elicit positive emotions from the user about the results of a competitor's situation analysis. For example, it highlights success stories and positive analysis results. In this way, suggestions can be made to elicit positive emotions from the user about the results of a competitor's situation analysis.

[0071] The pricing strategy proposal unit uses the generation AI to analyze market data and competitors' pricing, and propose the optimal price range. For example, the generation AI may analyze market data and competitors' pricing, and propose the optimal price range. For example, the unit may propose the monthly fee for an online learning platform by comparing it with other competing services. The generation AI may also propose a pricing strategy based on market data. For example, the unit may set prices taking into account the balance between supply and demand. The generation AI may also analyze competitors' pricing, and generate a report proposing the optimal price range. For example, the generation AI may provide graphs and charts showing competitors' pricing strategies. This makes it possible to propose the optimal price range.

[0072] The pricing strategy proposal unit can propose regional pricing strategies by taking into account the market characteristics of each region. In the pricing strategy proposal unit, for example, the generation AI considers the market characteristics of each region and proposes regional pricing strategies. For example, the price of the online learning platform is set according to the purchasing power and competitive situation of each region. The generation AI also proposes a pricing strategy based on market data for each region. For example, it analyzes the demand and competitive situation in a specific region and sets the optimal price. The generation AI also generates a pricing strategy report that considers the market characteristics of each region. For example, it provides graphs and charts showing the pricing strategy for each region. This makes it possible to propose regional pricing strategies.

[0073] The pricing strategy proposal unit can use the emotion estimation function to analyze consumer emotion data and propose a pricing strategy that reflects their emotional needs. In the pricing strategy proposal unit, for example, the generation AI uses the emotion estimation function to analyze consumer emotion data and propose a pricing strategy that reflects their emotional needs. For example, when setting prices for an online learning platform, the generation AI takes into account consumer emotion scores. The generation AI also proposes a pricing strategy based on the emotion data. For example, it prioritizes setting price ranges where consumers have strong positive emotions. The generation AI also analyzes the emotion data and generates a pricing strategy report that reflects their emotional needs. For example, it provides graphs and charts showing consumer emotion scores. This makes it possible to propose a pricing strategy that reflects their emotional needs.

[0074] The promotion proposal unit allows the generation AI to analyze past promotion data and marketing trends and propose optimal promotional methods. For example, the generation AI analyzes past promotional data and marketing trends and proposes optimal promotional methods. For example, in promoting an online learning platform, the generation AI proposes social media advertising. The generation AI also proposes promotional methods based on promotional data. For example, it proposes promotion strategies based on past success stories. The generation AI also analyzes marketing trends and generates reports proposing optimal promotional methods. For example, it provides graphs and charts showing the latest marketing trends. This makes it possible to propose optimal promotional methods.

[0075] The promotion proposal unit can propose a promotion strategy for each region, taking into account the market characteristics of each region. In the promotion proposal unit, for example, the generation AI takes into account the market characteristics of each region and proposes a promotion strategy for each region. For example, the promotion of an online learning platform is set according to the purchasing power and competitive situation of each region. The generation AI also proposes a promotion strategy based on market data for each region. For example, it analyzes the demand and competitive situation in a specific region and proposes the optimal promotional method. The generation AI also generates a promotion strategy report that takes into account the market characteristics of each region. For example, it provides graphs and charts showing the promotion strategy for each region. This makes it possible to propose a promotion strategy for each region.

[0076] The promotion proposal unit can use the emotion estimation function to analyze consumer emotion data and propose a promotion strategy that reflects their emotional needs. For example, the generation AI in the promotion proposal unit uses the emotion estimation function to analyze consumer emotion data and propose a promotion strategy that reflects their emotional needs. For example, in promoting an online learning platform, the consumer's emotion score is taken into consideration. The generation AI also proposes a promotion strategy based on the emotion data. For example, promotional methods that evoke strong positive emotions from consumers are given priority. The generation AI also analyzes the emotion data and generates a promotion strategy report that reflects their emotional needs. For example, a graph or chart showing the consumer's emotion score is provided. This makes it possible to propose a promotion strategy that reflects their emotional needs.

[0077] The promotion proposal unit can classify the results of the proposed promotion methods by different industries and uses and propose more specific promotion strategies. For example, the generation AI in the promotion proposal unit classifies the results of the proposed promotion methods by different industries and uses and proposes specific promotion strategies. For example, the promotion of an online learning platform is classified into the education industry and corporate training uses. The generation AI also analyzes the results of the proposed promotion methods by industry and use and proposes specific promotion strategies. For example, it proposes a promotion strategy for the education industry and a promotion strategy for corporate training uses. The generation AI also generates a report that classifies the results of the proposed promotion methods by different industries and uses. For example, it provides graphs and charts showing promotion strategies for each industry. This makes it possible to propose specific promotion strategies for different industries and uses.

[0078] The promotion proposal unit can visualize the results of the proposed promotion methods so that the user can intuitively understand them. In the promotion proposal unit, for example, the generation AI visualizes the results of the proposed promotion methods so that the user can intuitively understand them. For example, it provides graphs and charts showing the promotion strategy of an online learning platform. Furthermore, based on the visualized results of the proposed promotion methods, the generation AI provides the user with an easy-to-understand report. For example, it displays a graph showing the trend of the promotion strategy or a pie chart showing market share. Furthermore, the generation AI visualizes the results of the proposed promotion methods so that the user can intuitively understand them. For example, it displays data on the promotion strategy using an interactive dashboard. This makes it possible to visualize the results of the proposed promotion methods so that they can be intuitively understood.

[0079] The promotion suggestion unit can use the emotion estimation function to analyze the emotions the user feels about the proposed promotion method results and make suggestions to elicit positive emotions. For example, the generation AI in the promotion suggestion unit uses the emotion estimation function to analyze the emotions the user feels about the proposed promotion method results and make suggestions to elicit positive emotions. For example, the generation AI analyzes the user's emotion score about the proposed promotion method results. Furthermore, based on the emotion data, the generation AI makes suggestions to elicit positive emotions from the user. For example, the generation AI provides positive feedback or encouraging messages about the proposed promotion method results. Furthermore, the generation AI analyzes the emotion data and makes suggestions that will make the user feel positive emotions about the proposed promotion method results. For example, the generation AI emphasizes success stories and positive proposed results. This allows suggestions to be made that will make the user feel positive emotions about the proposed promotion method results.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The market fit assessment system can further include an experience assessment unit that takes into account the user's past business experience. The experience assessment unit analyzes data on the user's past business successes and failures and assesses the applicability of those successes to the current idea. For example, it analyzes the user's past successful business models and marketing strategies and suggests how they can be applied to the current idea. It also provides advice on how to avoid making the same mistakes based on past failures. This makes it possible to assess market fit by taking advantage of the user's past experience.

[0082] The market conformance assessment system can further include an environmental impact assessment unit. The environmental impact assessment unit assesses the environmental impact of the user's idea and provides advice from a sustainability perspective. For example, it calculates the carbon dioxide emissions associated with realizing the idea and suggests environmentally friendly alternatives. It also suggests environmentally friendly improvements, such as using recyclable materials and improving energy efficiency. This enables environmentally friendly market conformance assessment.

[0083] The market suitability assessment system can further include a cultural suitability assessment unit. The cultural suitability assessment unit evaluates the degree to which a user's idea will be accepted in different cultural spheres. For example, it analyzes the cultural background and consumer preferences of a specific region or country to evaluate the applicability of the idea. It also proposes marketing strategies and promotion methods that take cultural differences into account. This makes it possible to evaluate market suitability in different cultural spheres.

[0084] The market fit assessment system can further include a technical fit assessment unit. The technical fit assessment unit evaluates how well the user's idea fits with current technological trends and technical constraints. For example, it evaluates the technical feasibility of the idea based on the latest technological trends and research results. It also proposes alternative technologies and improvements to overcome technical constraints. This enables market fit assessment from a technical perspective.

[0085] The market suitability assessment system can further include a legal and regulatory suitability assessment unit. The legal and regulatory suitability assessment unit evaluates the degree to which a user's idea complies with relevant laws and regulations. For example, it analyzes laws and regulations and compliance requirements in a specific industry or region to evaluate the applicability of the idea. It also suggests improvements and necessary procedures for complying with laws and regulations. This enables market suitability assessment from the perspective of laws and regulations.

[0086] The market suitability assessment system can further use an emotion estimation function to analyze the emotions a user feels when entering an idea and provide real-time feedback to elicit positive emotions. For example, when a user enters an idea, the generation AI uses the emotion estimation function to analyze the user's emotions in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function can also be used to provide feedback to elicit positive emotions when the user enters an idea. For example, it can present encouraging messages or success stories. Furthermore, based on the user's emotion data, the generation AI provides real-time feedback and advice to strengthen positive emotions. For example, it can display appropriate encouragement or praise based on the input content. This makes it possible to provide feedback to elicit positive emotions from the user.

[0087] The market suitability assessment system can further use an emotion estimation function to analyze consumer emotion data and perform market assessment that reflects emotional needs. For example, the generation AI uses the emotion estimation function to analyze consumer emotion data and perform market assessment that reflects emotional needs. For example, consumer emotion toward online learning is analyzed and rising demand is evaluated. The generation AI also evaluates market needs based on the emotion data. For example, ideas with strong positive consumer emotion are prioritized in evaluation. The generation AI also analyzes the emotion data and generates a market assessment report that reflects emotional needs. For example, graphs and charts showing consumer emotion scores are provided. This makes it possible to perform market assessment that reflects emotional needs.

[0088] The market suitability assessment system can further use the emotion estimation function to analyze the emotion data of the target customer segment and propose a marketing strategy to elicit emotional empathy. For example, the generation AI uses the emotion estimation function to analyze the emotion data of the target customer segment and propose a marketing strategy to elicit emotional empathy. For example, in the promotion of an online learning platform, a message that elicits positive emotions is proposed. Furthermore, based on the emotion data, the generation AI proposes a marketing strategy to elicit emotional empathy from the target customer segment. For example, the emotion score is analyzed and advertising copy is created to elicit empathy. Furthermore, the generation AI analyzes the emotion data and generates a marketing strategy report to elicit emotional empathy from the target customer segment. For example, a marketing strategy is proposed based on the emotion score. This makes it possible to propose a marketing strategy to elicit emotional empathy.

[0089] The market suitability assessment system can further use the emotion estimation function to extract emotion data from competitors' customer reviews and evaluate their emotional strengths and weaknesses. For example, the generation AI uses the emotion estimation function to extract emotion data from competitors' customer reviews and evaluate their emotional strengths and weaknesses. For example, it analyzes competitors' customer reviews on an online learning platform and calculates an emotion score. Furthermore, based on the emotion data, the generation AI generates a report evaluating the competitors' emotional strengths and weaknesses. For example, it provides graphs and charts showing the positive and negative emotions in the competitors' customer reviews. Furthermore, the generation AI extracts emotion data from competitors' customer reviews and provides indicators for evaluating their emotional strengths and weaknesses. For example, it performs an evaluation based on the competitors' emotion scores. This makes it possible to evaluate their emotional strengths and weaknesses.

[0090] The market suitability assessment system can further use an emotion estimation function to analyze the emotions a user has about the results of a competitor's situation analysis and make suggestions to elicit positive emotions. For example, the generation AI can use the emotion estimation function to analyze the emotions a user has about the results of a competitor's situation analysis and make suggestions to elicit positive emotions. For example, the generation AI can analyze the user's emotion score about the analysis results. Furthermore, based on the emotion data, the generation AI can make suggestions to elicit positive emotions from the user. For example, the generation AI can provide positive feedback or encouraging messages about the analysis results. Furthermore, the generation AI can analyze the emotion data and make suggestions to elicit positive emotions from the user about the results of a competitor's situation analysis. For example, the generation AI can highlight success stories and positive analysis results. In this way, suggestions can be made to elicit positive emotions from the user about the results of a competitor's situation analysis.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: In the idea input section, the user inputs an outline of their idea. For example, the user inputs an idea such as "I want to launch a new online learning platform." The idea input section also allows the generation AI to search relevant patent databases and compare the idea with existing patent information to evaluate its novelty. For example, the generation AI searches a patent database, extracts similar patents, and evaluates their novelty. Step 2: The market evaluation unit evaluates market needs based on the idea input by the idea input unit. For example, the generation AI analyzes past market data and trends to determine how much demand there is for the idea in the current market. Step 3: The Customer Identification Department identifies the target customer segment based on the market needs assessed by the Market Assessment Department. For example, the Generative AI analyzes demographic data and consumer behavior data to identify what type of customer segment would be interested in the idea. Step 4: The competitive analysis department analyzes the competitive landscape based on the target customer segments identified by the customer identification department. For example, the generative AI researches similar services and products on the market and evaluates their strengths and weaknesses.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. an idea input unit for inputting a user's idea; a market evaluation unit that evaluates market needs based on the ideas input by the idea input unit; a customer identification unit that identifies a target customer segment based on the market needs evaluated by the market evaluation unit; a competitive analysis unit that analyzes the status of competitors based on the target customer segment identified by the customer identification unit. A system characterized by:

2. The idea input unit The generative AI searches relevant patent databases for an idea outline entered by the user, compares it with existing patent information, and evaluates its novelty.

2. The system of claim 1.

3. The idea input unit As you enter an outline of your idea, generative AI automatically searches for relevant technical literature and research papers to provide technical support.

2. The system of claim 1.

4. The idea input unit Analyze the emotions of the user when they input their ideas and provide real-time feedback to elicit positive emotions.

2. The system of claim 1.

5. The market evaluation unit Analyze not only historical market data but also real-time social media data to make assessments that reflect the latest market trends.

2. The system of claim 1.

6. The market evaluation unit Provide regional demand forecasts taking into account the market characteristics of each region 2. The system of claim 1.

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