system

Abstract

The system according to this embodiment aims to effectively collect and analyze market data and make strategic proposals. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects market data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it cannot be said that market data is effectively collected and analyzed and strategic proposals are sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively collect and analyze market data and make strategic proposals.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects market data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can effectively collect and analyze market data and make strategic proposals. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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, RAM 48, and storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Strategy Agent System according to an embodiment of the present invention is a system that utilizes AI to provide startup managers with market analysis, competitive analysis, and business model considerations, thereby supporting strategic decision-making. For example, the Strategy Agent System uses AI to analyze market data in real time. This allows for rapid understanding of market trends and provides this information to startup managers. For instance, it detects new market trends and changes in consumer preferences in real time and notifies managers. Next, the Strategy Agent System uses AI to analyze the strategies of competitors and make suggestions to startup managers. For example, it detects the launch of new products or changes in marketing strategies by competitors and proposes countermeasures. Furthermore, the Strategy Agent System uses AI to simulate business models and propose the optimal business model. For example, it simulates the profitability and risks of a new business model and presents the manager with the best option. These functions enable startup managers to make strategic decisions quickly without spending a lot of time on market analysis and competitive analysis. Additionally, improved AI analysis accuracy can reduce investment risk and improve profitability. This allows the Strategy Agent System to support the strategic decision-making of startup managers and enable efficient management.

[0029] The strategy agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. The data collection unit collects, for example, publicly available data from the internet. The data collection unit can also obtain data from an internal company database. Furthermore, the data collection unit can collect data in real time. For example, the data collection unit automatically collects data from the internet using web scraping technology. When obtaining data from an internal company database, the data collection unit accesses the database via an API and obtains the necessary data. When collecting data in real time, the data collection unit collects data in real time using technology that processes streaming data. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis and machine learning algorithms, but is not limited to these examples. For example, the analysis unit uses statistical analysis to understand data trends. The analysis unit can also detect data patterns using machine learning algorithms. Furthermore, the analysis unit can perform data clustering. For example, the analysis unit uses regression analysis to predict data trends. When using machine learning algorithms, the analysis department uses the data as training data to build a model. When performing clustering, the analysis department divides the data into groups and analyzes the characteristics of each group. The proposal department makes proposals based on the analysis results obtained by the analysis department. Proposals include, but are not limited to, strategic proposals and improvement suggestions. For example, the proposal department proposes the optimal strategy to startup managers based on the analysis results. The proposal department can also propose areas for improvement to managers based on the analysis results. Furthermore, the proposal department can propose new business models based on the analysis results. For example, the proposal department proposes a new market entry strategy to managers based on the analysis results. When proposing areas for improvement, the proposal department proposes areas for improvement in business processes to managers based on the analysis results. When proposing a new business model, the proposal department proposes a new revenue model to managers based on the analysis results.As a result, the strategy agent system according to this embodiment can efficiently collect, analyze, and propose market data.

[0030] The data collection unit collects market data. This market data includes, but is not limited to, sales data, customer data, and competitor data. The data collection unit collects publicly available data from the internet, for example. Specifically, it uses web scraping technology to automatically collect data from news sites, official company websites, social media, etc. This allows the data collection unit to grasp the latest market trends and competitor movements in real time. The data collection unit can also obtain data from internal company databases. For example, it can access a company's sales database or customer management system and obtain the necessary data via API. This allows the data collection unit to integrate and analyze internal company information with external market data. Furthermore, the data collection unit can collect data in real time. For example, it uses streaming data processing technology to collect social media posts and real-time sales data. This allows the data collection unit to grasp rapidly changing market trends in real time and support rapid decision-making. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis department uses statistical analysis to understand data trends. For example, it performs time-series analysis of sales data to identify seasonal variations and trends. It can also analyze customer data to understand customer purchasing behavior and preferences. Furthermore, the analysis department can detect data patterns using machine learning algorithms. For example, it can segment customers using clustering algorithms and analyze the characteristics of each segment. This enables targeted marketing and personalized promotions. Additionally, the analysis department uses regression analysis to predict data trends. For example, it can predict future sales based on past sales data, which can be used for inventory management and production planning. When using machine learning algorithms, the analysis department uses data as training data to build models. For example, it can build predictive and classification models to make predictions and classifications on new data. This allows the analysis department to quickly and accurately analyze collected data and support data-driven decision-making. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and anomalous data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The Proposal Department makes proposals based on the analysis results obtained by the Analysis Department. These proposals include, but are not limited to, strategic proposals and improvement suggestions. Specifically, the Proposal Department proposes optimal strategies for startup managers based on the analysis results. For example, they propose new market entry strategies and product development strategies to help managers make quick decisions. The Proposal Department can also suggest areas for improvement to managers based on the analysis results. For example, they propose specific improvement measures to streamline business processes and reduce costs. Furthermore, the Proposal Department can propose new business models based on the analysis results. For example, they propose new revenue models such as subscription models and platform business models to support the company's growth. The Proposal Department presents these proposals as concrete action plans, providing them in a format that is easy for managers to implement. For example, they compile the proposals into presentation materials and reports and explain them to managers. The Proposal Department also monitors the implementation status of the proposals and follows up as needed. This allows the Proposal Department to support managers in implementing the proposals and achieving results. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. This allows the proposal department to consistently provide highly accurate proposals based on the latest information and analysis results, supporting the growth and success of companies.

[0033] The Strategy Agent System includes a Competitive Analysis Department that analyzes the strategies of competitors. For example, the Competitive Analysis Department analyzes competitors' pricing strategies. For instance, it collects competitors' product prices and analyzes price fluctuations. The Competitive Analysis Department can also analyze competitors' marketing strategies. For example, it analyzes competitors' advertising campaigns and evaluates their effectiveness. Furthermore, the Competitive Analysis Department can analyze competitors' product lineups. For example, it analyzes the launch status of competitors' new products and evaluates their impact. By analyzing competitors' strategies, it is possible to improve competitiveness.

[0034] The Strategy Agent System includes a simulation unit that simulates business models. For example, the simulation unit can simulate the profitability of a new business model. For instance, it can forecast the revenue of a new business model. The simulation unit can also simulate the risks of a new business model. For example, it can assess the risks of a new business model. Furthermore, the simulation unit can simulate the market adaptability of a new business model. For example, it can evaluate the market adaptability of a new business model. By simulating business models in this way, the system can propose the optimal business model.

[0035] The data collection unit can collect market data in real time. For example, the data collection unit uses streaming data processing technology to collect market data in real time. For instance, the data collection unit collects data in real time and increases the frequency of data updates. Furthermore, the data collection unit can minimize data latency to collect market data in real time. For example, the data collection unit uses high-speed data processing technology to reduce data latency. This allows for real-time market data collection, enabling a grasp of the latest market trends.

[0036] The analysis department can analyze collected market data to detect new market trends and changes in consumer preferences. For example, the analysis department can use statistical analysis to analyze the collected market data. For instance, it can statistically analyze the collected market data to detect new market trends. The analysis department can also use machine learning algorithms to analyze the collected market data. For example, it can use machine learning algorithms to detect changes in consumer preferences. Furthermore, the analysis department can perform data clustering to analyze the collected market data. For example, it can cluster the data and analyze the characteristics of each cluster. This allows for rapid response by detecting market trends and changes in consumer preferences.

[0037] The proposal department can propose optimal strategies to startup managers based on the analysis results. For example, the proposal department can propose new market entry strategies to startup managers based on the analysis results. The proposal department can also propose improvements to business processes to startup managers based on the analysis results. Furthermore, the proposal department can propose new revenue models to startup managers based on the analysis results. In this way, by proposing optimal strategies, the proposal department can support the decision-making of managers.

[0038] The data collection unit can analyze past market data collection history and select the optimal collection method. For example, the data collection unit can discover from past collection history that data collection efficiency is high during specific time periods and concentrate data collection during those times. For example, the data collection unit can analyze past collection history and concentrate data collection during specific time periods. The data collection unit can also confirm that specific data sources are highly reliable based on past collection history and prioritize the use of those data sources. For example, the data collection unit can analyze past collection history and select highly reliable data sources. Furthermore, the data collection unit can avoid data duplication by analyzing past collection history and optimizing the collection frequency. For example, the data collection unit can analyze past collection history and optimize the collection frequency. This allows the optimal collection method to be selected by analyzing past collection history.

[0039] The data collection unit can filter market data by focusing on specific industries or regions. For example, it can prioritize collecting data related to a particular industry and analyze its trends in detail. It can also collect data related to a specific region and understand its market characteristics. Furthermore, the data collection unit can set different filtering criteria for each industry and region to improve the accuracy of the collected data. This allows for detailed market analysis by focusing data collection on specific industries and regions.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting market data. For example, if a user is in a specific region, the data collection unit will prioritize the collection of market data related to that region. For example, the data collection unit will collect data related to a region based on the user's geographical location. The data collection unit can also select highly relevant data sources based on the user's geographical location. For example, the data collection unit will select highly relevant data sources based on the user's geographical location. Furthermore, the data collection unit can update the user's location information in real time to perform optimal data collection. For example, the data collection unit will update the user's location information in real time to perform optimal data collection. This allows for the efficient collection of highly relevant data by considering the user's geographical location.

[0041] The data collection unit can analyze social media trends and collect relevant data when collecting market data. For example, the data collection unit can analyze social media trends in real time and collect relevant market data. Furthermore, the data collection unit can collect consumer preferences and opinions based on social media trends. In addition, the data collection unit can analyze social media trends and detect new market trends. This allows for the efficient collection of relevant market data by analyzing social media trends.

[0042] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis department can perform detailed analysis on important data and simplified analysis on less important data. For instance, the analysis department can differentiate between detailed and simplified analysis based on the importance of the data. The analysis department can also determine the priority of analysis based on the importance of the data. For example, the analysis department can determine the priority of analysis based on the importance of the data. Furthermore, the analysis department can apply multiple analytical methods to important data to provide detailed results. For example, the analysis department can apply multiple analytical methods to important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0043] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a trend analysis algorithm to market data. It can also apply a competitive analysis algorithm to competitor data. Furthermore, it can apply a consumer behavior analysis algorithm to consumer data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible.

[0044] The analysis department can prioritize analysis based on the data collection timing. For example, it can prioritize analyzing the latest data to grasp real-time market trends. The analysis department can also analyze long-term trends based on historical data. For example, it can analyze historical data based on the data collection timing. Furthermore, the analysis department can adjust the level of detail of the analysis according to the data collection timing. For example, it can adjust the level of detail of the analysis according to the data collection timing. This allows for efficient analysis by prioritizing analysis based on the data collection timing.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data to extract important insights. For example, the analysis unit prioritizes analyzing highly relevant data based on its relevance. The analysis unit can also optimize the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform detailed analysis based on highly relevant data. For example, the analysis unit performs detailed analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of the strategies. For example, it can provide detailed proposals for important strategies and simplified proposals for less important ones. The proposal department can also prioritize proposals based on their importance. Furthermore, for important strategies, the proposal department can offer multiple proposals, providing detailed options. This allows for more efficient proposals by adjusting the level of detail based on the importance of the strategies.

[0047] The proposal department can apply different proposal algorithms depending on the strategy category when making a proposal. For example, the proposal department can apply a marketing proposal algorithm to a marketing strategy. For example, the proposal department can apply a product development proposal algorithm to a product development strategy. For example, the proposal department can apply a financial proposal algorithm to a financial strategy. For example, the proposal department can apply a financial proposal algorithm to a financial strategy. By applying different proposal algorithms depending on the strategy category, it becomes possible to make highly accurate proposals.

[0048] The proposal department can prioritize proposals based on the strategy submission deadlines. For example, the proposal department will prioritize proposals for strategies that are urgent. For example, the proposal department will prioritize proposals for urgent strategies based on the strategy submission deadlines. The proposal department can also quickly propose proposals for strategies that are nearing their submission deadlines. For example, the proposal department will quickly propose strategies that are nearing their submission deadlines. Furthermore, the proposal department can adjust the level of detail in proposals according to the submission deadlines. For example, the proposal department will adjust the level of detail in proposals based on the strategy submission deadlines. This allows for more efficient proposals by prioritizing proposals based on the strategy submission deadlines.

[0049] The proposal department can adjust the order of proposals based on the relevance of the strategies. For example, the proposal department can prioritize proposing strategies that are highly relevant. For example, the proposal department can prioritize proposing strategies that are highly relevant based on their relevance. The proposal department can also optimize the order of proposals according to the relevance of the strategies. For example, the proposal department can adjust the order of proposals based on the relevance of the strategies. Furthermore, the proposal department can provide detailed proposals for highly relevant strategies. For example, the proposal department can provide detailed proposals based on the relevance of the strategies. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the strategies.

[0050] The competitive analysis department can improve the accuracy of its analysis by considering the relationships between competitors. For example, the competitive analysis department can conduct competitive analysis by considering the alliances between competitors. For example, the competitive analysis department can conduct competitive analysis based on the alliances between competitors. The competitive analysis department can also conduct competitive analysis based on the market share of competitors. For example, the competitive analysis department can conduct competitive analysis based on the market share of competitors. Furthermore, the competitive analysis department can conduct competitive analysis by considering the product lineup of competitors. For example, the competitive analysis department can conduct competitive analysis based on the product lineup of competitors. By considering the relationships between competitors, a highly accurate competitive analysis becomes possible.

[0051] The competitive analysis department can conduct competitive analysis while considering the attribute information of competitors. For example, the competitive analysis department can conduct competitive analysis while considering the size of competitors' companies. For example, the competitive analysis department can conduct competitive analysis based on the size of competitors' companies. The competitive analysis department can also conduct competitive analysis based on the position of competitors within the industry. For example, the competitive analysis department can conduct competitive analysis based on the position of competitors within the industry. Furthermore, the competitive analysis department can also conduct competitive analysis while considering the financial situation of competitors. For example, the competitive analysis department can conduct competitive analysis based on the financial situation of competitors. By considering the attribute information of competitors, it becomes possible to conduct highly accurate competitive analysis.

[0052] The competitive analysis department can conduct competitive analysis while considering the geographical distribution of competitors. For example, the competitive analysis department can conduct regional competitive analysis based on the geographical distribution of competitors. Furthermore, the competitive analysis department can analyze regional market share while considering the geographical distribution of competitors. For example, the competitive analysis department can analyze regional market share based on the geographical distribution of competitors. In addition, the competitive analysis department can propose regional competitive strategies based on the geographical distribution of competitors. For example, the competitive analysis department can propose regional competitive strategies based on the geographical distribution of competitors. This makes regional competitive analysis possible by considering the geographical distribution of competitors.

[0053] The competitive analysis department can improve the accuracy of its analysis by referring to relevant literature from competitors. For example, the competitive analysis department can conduct competitive analysis by referring to research papers from competitors. For example, the competitive analysis department can conduct competitive analysis based on research papers from competitors. The competitive analysis department can also conduct competitive analysis based on patent information from competitors. For example, the competitive analysis department can conduct competitive analysis based on patent information from competitors. Furthermore, the competitive analysis department can conduct competitive analysis by referring to industry reports from competitors. For example, the competitive analysis department can conduct competitive analysis based on industry reports from competitors. In this way, by referring to relevant literature from competitors, a highly accurate competitive analysis becomes possible.

[0054] The simulation unit can select the optimal simulation method by referring to past simulation data during the simulation. For example, the simulation unit selects the optimal simulation method based on past simulation data. Furthermore, the simulation unit can improve the accuracy of the simulation by referring to past simulation data. In addition, the simulation unit can analyze past simulation data and adjust the level of detail of the simulation. This allows the optimal simulation method to be selected by referring to past simulation data.

[0055] The simulation unit can customize the simulation methods based on the current market conditions during the simulation. For example, the simulation unit can customize the simulation methods based on the current market conditions. The simulation unit can also adjust the level of detail of the simulation, taking into account the current market conditions. Furthermore, the simulation unit can determine the priority of the simulation based on the current market conditions. For example, the simulation unit can determine the priority of the simulation based on the current market conditions. By customizing the simulation methods based on the current market conditions, highly accurate simulations become possible.

[0056] The simulation unit can select the optimal simulation method by considering geographical location information during the simulation. For example, the simulation unit can select the optimal simulation method based on the user's geographical location information. The simulation unit can also adjust the level of detail of the simulation by considering geographical location information. For example, the simulation unit can adjust the level of detail of the simulation by considering geographical location information. Furthermore, the simulation unit can determine the priority of simulations based on geographical location information. For example, the simulation unit can determine the priority of simulations based on geographical location information. As a result, highly accurate simulations can be performed by considering geographical location information.

[0057] The simulation unit can improve the accuracy of the simulation by referring to relevant literature during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to relevant literature. For example, the simulation unit can improve the accuracy of the simulation by referring to relevant literature. The simulation unit can also adjust the level of detail of the simulation based on relevant literature. For example, the simulation unit can adjust the level of detail of the simulation based on relevant literature. Furthermore, the simulation unit can determine the priority of the simulation by referring to relevant literature. For example, the simulation unit can determine the priority of the simulation by referring to relevant literature. As a result, highly accurate simulations can be performed by referring to relevant literature.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can analyze past market data collection history and select the optimal collection method. For example, by analyzing past collection history, it can discover that data collection efficiency is high during specific time periods and concentrate collection during those times. Furthermore, based on past collection history, it can confirm that certain data sources are highly reliable and prioritize their use. Additionally, by analyzing past collection history and optimizing collection frequency, data duplication can be avoided. In short, by analyzing past collection history, the optimal collection method can be selected.

[0060] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on important data and simplified analysis on less important data. It can also prioritize analyses according to the importance of the data. Furthermore, it can apply multiple analytical methods to important data to provide detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0061] The proposal department can adjust the level of detail in proposals based on the importance of the strategies. For example, detailed proposals can be provided for important strategies, while simpler proposals can be provided for less important strategies. Furthermore, the priority of proposals can be determined according to the importance of the strategies. In addition, multiple proposals can be provided for important strategies, offering detailed options. This allows for efficient proposal development by adjusting the level of detail based on the importance of the strategies.

[0062] The competitive analysis department can improve the accuracy of its analysis by considering the relationships between competitors. For example, it can conduct competitive analysis by considering the partnerships between competitors. It can also conduct competitive analysis based on the market share of competitors. Furthermore, it can conduct competitive analysis by considering the product lineups of competitors. In this way, by considering the relationships between competitors, highly accurate competitive analysis becomes possible.

[0063] The simulation unit can select the optimal simulation method by referring to past simulation data. For example, it can select the optimal simulation method based on past simulation data. Furthermore, it can improve the accuracy of simulations by referring to past simulation data. In addition, it can analyze past simulation data and adjust the level of detail of the simulation. This allows for the selection of the optimal simulation method by referring to past simulation data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects market data. This market data includes sales data, customer data, competitor data, etc. The data collection unit can obtain data from publicly available data on the internet or from internal company databases. The data collection unit can also automatically collect data from the internet using web scraping technology, access internal company databases via APIs to obtain necessary data, or collect data in real time using streaming data processing technology. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to understand data trends and machine learning algorithms to detect data patterns. The analysis unit also performs data clustering and predicts data trends using regression analysis. When using machine learning algorithms, the analysis unit uses the data as training data to build a model. When performing clustering, the analysis unit divides the data into groups and analyzes the characteristics of each group. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. These proposals may include strategic proposals, improvement suggestions, and new business model proposals. For example, the proposal department may propose the optimal strategy for startup managers based on the analysis results, suggest areas for improvement to the managers, and propose new market entry strategies, improvements to business processes, and new revenue models.

[0066] (Example of form 2) The Strategy Agent System according to an embodiment of the present invention is a system that utilizes AI to provide startup managers with market analysis, competitive analysis, and business model considerations, thereby supporting strategic decision-making. For example, the Strategy Agent System uses AI to analyze market data in real time. This allows for rapid understanding of market trends and provides this information to startup managers. For instance, it detects new market trends and changes in consumer preferences in real time and notifies managers. Next, the Strategy Agent System uses AI to analyze the strategies of competitors and make suggestions to startup managers. For example, it detects the launch of new products or changes in marketing strategies by competitors and proposes countermeasures. Furthermore, the Strategy Agent System uses AI to simulate business models and propose the optimal business model. For example, it simulates the profitability and risks of a new business model and presents the manager with the best option. These functions enable startup managers to make strategic decisions quickly without spending a lot of time on market analysis and competitive analysis. Additionally, improved AI analysis accuracy can reduce investment risk and improve profitability. This allows the Strategy Agent System to support the strategic decision-making of startup managers and enable efficient management.

[0067] The strategy agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. The data collection unit collects, for example, publicly available data from the internet. The data collection unit can also obtain data from an internal company database. Furthermore, the data collection unit can collect data in real time. For example, the data collection unit automatically collects data from the internet using web scraping technology. When obtaining data from an internal company database, the data collection unit accesses the database via an API and obtains the necessary data. When collecting data in real time, the data collection unit collects data in real time using technology that processes streaming data. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis and machine learning algorithms, but is not limited to these examples. For example, the analysis unit uses statistical analysis to understand data trends. The analysis unit can also detect data patterns using machine learning algorithms. Furthermore, the analysis unit can perform data clustering. For example, the analysis unit uses regression analysis to predict data trends. When using machine learning algorithms, the analysis department uses the data as training data to build a model. When performing clustering, the analysis department divides the data into groups and analyzes the characteristics of each group. The proposal department makes proposals based on the analysis results obtained by the analysis department. Proposals include, but are not limited to, strategic proposals and improvement suggestions. For example, the proposal department proposes the optimal strategy to startup managers based on the analysis results. The proposal department can also propose areas for improvement to managers based on the analysis results. Furthermore, the proposal department can propose new business models based on the analysis results. For example, the proposal department proposes a new market entry strategy to managers based on the analysis results. When proposing areas for improvement, the proposal department proposes areas for improvement in business processes to managers based on the analysis results. When proposing a new business model, the proposal department proposes a new revenue model to managers based on the analysis results.As a result, the strategy agent system according to this embodiment can efficiently collect, analyze, and propose market data.

[0068] The data collection unit collects market data. This market data includes, but is not limited to, sales data, customer data, and competitor data. The data collection unit collects publicly available data from the internet, for example. Specifically, it uses web scraping technology to automatically collect data from news sites, official company websites, social media, etc. This allows the data collection unit to grasp the latest market trends and competitor movements in real time. The data collection unit can also obtain data from internal company databases. For example, it can access a company's sales database or customer management system and obtain the necessary data via API. This allows the data collection unit to integrate and analyze internal company information with external market data. Furthermore, the data collection unit can collect data in real time. For example, it uses streaming data processing technology to collect social media posts and real-time sales data. This allows the data collection unit to grasp rapidly changing market trends in real time and support rapid decision-making. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis department analyzes the data collected by the data collection department. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis department uses statistical analysis to understand data trends. For example, it performs time-series analysis of sales data to identify seasonal variations and trends. It can also analyze customer data to understand customer purchasing behavior and preferences. Furthermore, the analysis department can detect data patterns using machine learning algorithms. For example, it can segment customers using clustering algorithms and analyze the characteristics of each segment. This enables targeted marketing and personalized promotions. Additionally, the analysis department uses regression analysis to predict data trends. For example, it can predict future sales based on past sales data, which can be used for inventory management and production planning. When using machine learning algorithms, the analysis department uses data as training data to build models. For example, it can build predictive and classification models to make predictions and classifications on new data. This allows the analysis department to quickly and accurately analyze collected data and support data-driven decision-making. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and anomalous data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0070] The Proposal Department makes proposals based on the analysis results obtained by the Analysis Department. These proposals include, but are not limited to, strategic proposals and improvement suggestions. Specifically, the Proposal Department proposes optimal strategies for startup managers based on the analysis results. For example, they propose new market entry strategies and product development strategies to help managers make quick decisions. The Proposal Department can also suggest areas for improvement to managers based on the analysis results. For example, they propose specific improvement measures to streamline business processes and reduce costs. Furthermore, the Proposal Department can propose new business models based on the analysis results. For example, they propose new revenue models such as subscription models and platform business models to support the company's growth. The Proposal Department presents these proposals as concrete action plans, providing them in a format that is easy for managers to implement. For example, they compile the proposals into presentation materials and reports and explain them to managers. The Proposal Department also monitors the implementation status of the proposals and follows up as needed. This allows the Proposal Department to support managers in implementing the proposals and achieving results. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. This allows the proposal department to consistently provide highly accurate proposals based on the latest information and analysis results, supporting the growth and success of companies.

[0071] The Strategy Agent System includes a Competitive Analysis Department that analyzes the strategies of competitors. For example, the Competitive Analysis Department analyzes competitors' pricing strategies. For instance, it collects competitors' product prices and analyzes price fluctuations. The Competitive Analysis Department can also analyze competitors' marketing strategies. For example, it analyzes competitors' advertising campaigns and evaluates their effectiveness. Furthermore, the Competitive Analysis Department can analyze competitors' product lineups. For example, it analyzes the launch status of competitors' new products and evaluates their impact. By analyzing competitors' strategies, it is possible to improve competitiveness.

[0072] The Strategy Agent System includes a simulation unit that simulates business models. For example, the simulation unit can simulate the profitability of a new business model. For instance, it can forecast the revenue of a new business model. The simulation unit can also simulate the risks of a new business model. For example, it can assess the risks of a new business model. Furthermore, the simulation unit can simulate the market adaptability of a new business model. For example, it can evaluate the market adaptability of a new business model. By simulating business models in this way, the system can propose the optimal business model.

[0073] The data collection unit can collect market data in real time. For example, the data collection unit uses streaming data processing technology to collect market data in real time. For instance, the data collection unit collects data in real time and increases the frequency of data updates. Furthermore, the data collection unit can minimize data latency to collect market data in real time. For example, the data collection unit uses high-speed data processing technology to reduce data latency. This allows for real-time market data collection, enabling a grasp of the latest market trends.

[0074] The analysis department can analyze collected market data to detect new market trends and changes in consumer preferences. For example, the analysis department can use statistical analysis to analyze the collected market data. For instance, it can statistically analyze the collected market data to detect new market trends. The analysis department can also use machine learning algorithms to analyze the collected market data. For example, it can use machine learning algorithms to detect changes in consumer preferences. Furthermore, the analysis department can perform data clustering to analyze the collected market data. For example, it can cluster the data and analyze the characteristics of each cluster. This allows for rapid response by detecting market trends and changes in consumer preferences.

[0075] The proposal department can propose optimal strategies to startup managers based on the analysis results. For example, the proposal department can propose new market entry strategies to startup managers based on the analysis results. The proposal department can also propose improvements to business processes to startup managers based on the analysis results. Furthermore, the proposal department can propose new revenue models to startup managers based on the analysis results. In this way, by proposing optimal strategies, the proposal department can support the decision-making of managers.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of market data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect only important data. For example, the data collection unit can estimate the user's emotions and adjust the collection frequency if the user is stressed. The data collection unit can also collect detailed data and conduct comprehensive market analysis if the user is relaxed. For example, the data collection unit can estimate the user's emotions and collect detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of important market data in real time. For example, the data collection unit can estimate the user's emotions and collect important data in real time if the user is in a hurry. This allows for efficient data collection by adjusting the timing of market data collection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data collection unit can analyze past market data collection history and select the optimal collection method. For example, the data collection unit can discover from past collection history that data collection efficiency is high during specific time periods and concentrate data collection during those times. For example, the data collection unit can analyze past collection history and concentrate data collection during specific time periods. The data collection unit can also confirm that specific data sources are highly reliable based on past collection history and prioritize the use of those data sources. For example, the data collection unit can analyze past collection history and select highly reliable data sources. Furthermore, the data collection unit can avoid data duplication by analyzing past collection history and optimizing the collection frequency. For example, the data collection unit can analyze past collection history and optimize the collection frequency. This allows the optimal collection method to be selected by analyzing past collection history.

[0078] The data collection unit can filter market data by focusing on specific industries or regions. For example, it can prioritize collecting data related to a particular industry and analyze its trends in detail. It can also collect data related to a specific region and understand its market characteristics. Furthermore, the data collection unit can set different filtering criteria for each industry and region to improve the accuracy of the collected data. This allows for detailed market analysis by focusing data collection on specific industries and regions.

[0079] The data collection unit can estimate the user's emotions and prioritize the market data to collect based on those emotions. For example, if the user is stressed, the unit will prioritize collecting only the most important market data. The unit can also collect detailed market data and conduct comprehensive analysis if the user is relaxed. Furthermore, if the user is in a hurry, the unit can prioritize collecting important market data in real time. This allows for efficient data collection by prioritizing market data according to the user's emotions.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting market data. For example, if a user is in a specific region, the data collection unit will prioritize the collection of market data related to that region. For example, the data collection unit will collect data related to a region based on the user's geographical location. The data collection unit can also select highly relevant data sources based on the user's geographical location. For example, the data collection unit will select highly relevant data sources based on the user's geographical location. Furthermore, the data collection unit can update the user's location information in real time to perform optimal data collection. For example, the data collection unit will update the user's location information in real time to perform optimal data collection. This allows for the efficient collection of highly relevant data by considering the user's geographical location.

[0081] The data collection unit can analyze social media trends and collect relevant data when collecting market data. For example, the data collection unit can analyze social media trends in real time and collect relevant market data. Furthermore, the data collection unit can collect consumer preferences and opinions based on social media trends. In addition, the data collection unit can analyze social media trends and detect new market trends. This allows for the efficient collection of relevant market data by analyzing social media trends.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can estimate the user's emotions and provide simple analysis results if the user is nervous. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results if the user is in a hurry. For example, the analysis unit can estimate the user's emotions and provide concise analysis results if the user is in a hurry. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0083] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis department can perform detailed analysis on important data and simplified analysis on less important data. For instance, the analysis department can differentiate between detailed and simplified analysis based on the importance of the data. The analysis department can also determine the priority of analysis based on the importance of the data. For example, the analysis department can determine the priority of analysis based on the importance of the data. Furthermore, the analysis department can apply multiple analytical methods to important data to provide detailed results. For example, the analysis department can apply multiple analytical methods to important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0084] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a trend analysis algorithm to market data. It can also apply a competitive analysis algorithm to competitor data. Furthermore, it can apply a consumer behavior analysis algorithm to consumer data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. For example, if the user is nervous, the analysis unit can estimate the user's emotions and provide a simple display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the analysis unit estimates the user's emotions and provides a detailed display method if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets to the point. For example, if the analysis unit estimates the user's emotions and provides a display method that gets to the point if the user is in a hurry. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The analysis department can prioritize analysis based on the data collection timing. For example, it can prioritize analyzing the latest data to grasp real-time market trends. The analysis department can also analyze long-term trends based on historical data. For example, it can analyze historical data based on the data collection timing. Furthermore, the analysis department can adjust the level of detail of the analysis according to the data collection timing. For example, it can adjust the level of detail of the analysis according to the data collection timing. This allows for efficient analysis by prioritizing analysis based on the data collection timing.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data to extract important insights. For example, the analysis unit prioritizes analyzing highly relevant data based on its relevance. The analysis unit can also optimize the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform detailed analysis based on highly relevant data. For example, the analysis unit performs detailed analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0088] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide simple and easily understandable suggestions. For example, the suggestion function can estimate the user's emotions and provide simple suggestions if the user is nervous. The suggestion function can also provide detailed suggestions if the user is relaxed. For example, the suggestion function can estimate the user's emotions and provide detailed suggestions if the user is relaxed. Furthermore, if the suggestion function is in a hurry, it can provide concise suggestions. For example, the suggestion function can estimate the user's emotions and provide concise suggestions if the user is in a hurry. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand.

[0089] The proposal department can adjust the level of detail in its proposals based on the importance of the strategies. For example, it can provide detailed proposals for important strategies and simplified proposals for less important ones. The proposal department can also prioritize proposals based on their importance. Furthermore, for important strategies, the proposal department can offer multiple proposals, providing detailed options. This allows for more efficient proposals by adjusting the level of detail based on the importance of the strategies.

[0090] The proposal department can apply different proposal algorithms depending on the strategy category when making a proposal. For example, the proposal department can apply a marketing proposal algorithm to a marketing strategy. For example, the proposal department can apply a product development proposal algorithm to a product development strategy. For example, the proposal department can apply a financial proposal algorithm to a financial strategy. For example, the proposal department can apply a financial proposal algorithm to a financial strategy. By applying different proposal algorithms depending on the strategy category, it becomes possible to make highly accurate proposals.

[0091] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is nervous, the suggestion function can provide a short, to-the-point suggestion. For example, the suggestion function can estimate the user's emotions and provide a short suggestion if they are nervous. The suggestion function can also provide a longer suggestion with more detailed explanations if the user is relaxed. For example, the suggestion function can estimate the user's emotions and provide a longer suggestion if they are relaxed. Furthermore, if the user is in a hurry, the suggestion function can provide a short suggestion that can be quickly understood. For example, the suggestion function can estimate the user's emotions and provide a short suggestion if they are in a hurry. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The proposal department can prioritize proposals based on the strategy submission deadlines. For example, the proposal department will prioritize proposals for strategies that are urgent. For example, the proposal department will prioritize proposals for urgent strategies based on the strategy submission deadlines. The proposal department can also quickly propose proposals for strategies that are nearing their submission deadlines. For example, the proposal department will quickly propose strategies that are nearing their submission deadlines. Furthermore, the proposal department can adjust the level of detail in proposals according to the submission deadlines. For example, the proposal department will adjust the level of detail in proposals based on the strategy submission deadlines. This allows for more efficient proposals by prioritizing proposals based on the strategy submission deadlines.

[0093] The proposal department can adjust the order of proposals based on the relevance of the strategies. For example, the proposal department can prioritize proposing strategies that are highly relevant. For example, the proposal department can prioritize proposing strategies that are highly relevant based on their relevance. The proposal department can also optimize the order of proposals according to the relevance of the strategies. For example, the proposal department can adjust the order of proposals based on the relevance of the strategies. Furthermore, the proposal department can provide detailed proposals for highly relevant strategies. For example, the proposal department can provide detailed proposals based on the relevance of the strategies. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the strategies.

[0094] The competitive analysis unit can estimate the user's emotions and adjust the competitive analysis criteria based on those emotions. For example, if the user is nervous, the competitive analysis unit can provide simple and easy-to-understand competitive analysis results. For example, if the user is nervous, the competitive analysis unit can estimate the user's emotions and provide simple competitive analysis results. The competitive analysis unit can also provide detailed competitive analysis results if the user is relaxed. For example, if the competitive analysis unit estimates the user's emotions and provides detailed competitive analysis results if the user is relaxed. Furthermore, if the competitive analysis unit is in a hurry, it can provide concise competitive analysis results. For example, if the competitive analysis unit estimates the user's emotions and provides concise competitive analysis results if the user is in a hurry. By adjusting the competitive analysis criteria according to the user's emotions, it is possible to provide competitive analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The competitive analysis department can improve the accuracy of its analysis by considering the relationships between competitors. For example, the competitive analysis department can conduct competitive analysis by considering the alliances between competitors. For example, the competitive analysis department can conduct competitive analysis based on the alliances between competitors. The competitive analysis department can also conduct competitive analysis based on the market share of competitors. For example, the competitive analysis department can conduct competitive analysis based on the market share of competitors. Furthermore, the competitive analysis department can conduct competitive analysis by considering the product lineup of competitors. For example, the competitive analysis department can conduct competitive analysis based on the product lineup of competitors. By considering the relationships between competitors, a highly accurate competitive analysis becomes possible.

[0096] The competitive analysis department can conduct competitive analysis while considering the attribute information of competitors. For example, the competitive analysis department can conduct competitive analysis while considering the size of competitors' companies. For example, the competitive analysis department can conduct competitive analysis based on the size of competitors' companies. The competitive analysis department can also conduct competitive analysis based on the position of competitors within the industry. For example, the competitive analysis department can conduct competitive analysis based on the position of competitors within the industry. Furthermore, the competitive analysis department can also conduct competitive analysis while considering the financial situation of competitors. For example, the competitive analysis department can conduct competitive analysis based on the financial situation of competitors. By considering the attribute information of competitors, it becomes possible to conduct highly accurate competitive analysis.

[0097] The competitive analysis unit can estimate the user's emotions and adjust the order in which the competitive analysis results are displayed based on the estimated emotions. For example, if the user is nervous, the competitive analysis unit will prioritize displaying important competitive analysis results. For example, the competitive analysis unit estimates the user's emotions and prioritizes displaying important competitive analysis results when the user is nervous. The competitive analysis unit can also display detailed competitive analysis results when the user is relaxed. For example, the competitive analysis unit estimates the user's emotions and displays detailed competitive analysis results when the user is relaxed. Furthermore, if the user is in a hurry, the competitive analysis unit can display concise competitive analysis results. For example, the competitive analysis unit estimates the user's emotions and displays concise competitive analysis results when the user is in a hurry. By adjusting the order in which the competitive analysis results are displayed according to the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] The competitive analysis department can conduct competitive analysis while considering the geographical distribution of competitors. For example, the competitive analysis department can conduct regional competitive analysis based on the geographical distribution of competitors. Furthermore, the competitive analysis department can analyze regional market share while considering the geographical distribution of competitors. For example, the competitive analysis department can analyze regional market share based on the geographical distribution of competitors. In addition, the competitive analysis department can propose regional competitive strategies based on the geographical distribution of competitors. For example, the competitive analysis department can propose regional competitive strategies based on the geographical distribution of competitors. This makes regional competitive analysis possible by considering the geographical distribution of competitors.

[0099] The competitive analysis department can improve the accuracy of its analysis by referring to relevant literature from competitors. For example, the competitive analysis department can conduct competitive analysis by referring to research papers from competitors. For example, the competitive analysis department can conduct competitive analysis based on research papers from competitors. The competitive analysis department can also conduct competitive analysis based on patent information from competitors. For example, the competitive analysis department can conduct competitive analysis based on patent information from competitors. Furthermore, the competitive analysis department can conduct competitive analysis by referring to industry reports from competitors. For example, the competitive analysis department can conduct competitive analysis based on industry reports from competitors. In this way, by referring to relevant literature from competitors, a highly accurate competitive analysis becomes possible.

[0100] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is nervous, the simulation unit can provide a simple and easy-to-understand simulation result. For example, the simulation unit can estimate the user's emotions and provide a simple simulation result when the user is nervous. The simulation unit can also provide a detailed simulation result when the user is relaxed. For example, the simulation unit can estimate the user's emotions and provide a detailed simulation result when the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can provide a concise simulation result. For example, the simulation unit can estimate the user's emotions and provide a concise simulation result when the user is in a hurry. In this way, by adjusting the simulation method according to the user's emotions, it is possible to provide simulation results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The simulation unit can select the optimal simulation method by referring to past simulation data during the simulation. For example, the simulation unit selects the optimal simulation method based on past simulation data. Furthermore, the simulation unit can improve the accuracy of the simulation by referring to past simulation data. In addition, the simulation unit can analyze past simulation data and adjust the level of detail of the simulation. This allows the optimal simulation method to be selected by referring to past simulation data.

[0102] The simulation unit can customize the simulation methods based on the current market conditions during the simulation. For example, the simulation unit can customize the simulation methods based on the current market conditions. The simulation unit can also adjust the level of detail of the simulation, taking into account the current market conditions. Furthermore, the simulation unit can determine the priority of the simulation based on the current market conditions. For example, the simulation unit can determine the priority of the simulation based on the current market conditions. By customizing the simulation methods based on the current market conditions, highly accurate simulations become possible.

[0103] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is nervous, the simulation unit will prioritize providing important simulation results. For example, the simulation unit will estimate the user's emotions and prioritize providing important simulation results when the user is nervous. The simulation unit can also provide detailed simulation results when the user is relaxed. For example, the simulation unit will estimate the user's emotions and provide detailed simulation results when the user is relaxed. Furthermore, the simulation unit can provide concise simulation results when the user is in a hurry. For example, the simulation unit will estimate the user's emotions and provide concise simulation results when the user is in a hurry. This enables efficient simulation by determining the priority of simulations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The simulation unit can select the optimal simulation method by considering geographical location information during the simulation. For example, the simulation unit can select the optimal simulation method based on the user's geographical location information. The simulation unit can also adjust the level of detail of the simulation by considering geographical location information. For example, the simulation unit can adjust the level of detail of the simulation by considering geographical location information. Furthermore, the simulation unit can determine the priority of simulations based on geographical location information. For example, the simulation unit can determine the priority of simulations based on geographical location information. As a result, highly accurate simulations can be performed by considering geographical location information.

[0105] The simulation unit can improve the accuracy of the simulation by referring to relevant literature during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to relevant literature. For example, the simulation unit can improve the accuracy of the simulation by referring to relevant literature. The simulation unit can also adjust the level of detail of the simulation based on relevant literature. For example, the simulation unit can adjust the level of detail of the simulation based on relevant literature. Furthermore, the simulation unit can determine the priority of the simulation by referring to relevant literature. For example, the simulation unit can determine the priority of the simulation by referring to relevant literature. As a result, highly accurate simulations can be performed by referring to relevant literature.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can estimate the user's emotions and adjust the method of collecting market data based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection and collect only the most important data. If the user is relaxed, the unit can collect detailed data and conduct a comprehensive market analysis. Furthermore, if the user is in a hurry, the unit can prioritize collecting important market data in real time. This allows for efficient data collection by adjusting the method of collecting market data according to the user's emotions.

[0108] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on those emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand results. If the user is relaxed, the analysis unit can provide detailed results. Furthermore, if the user is in a hurry, the analysis unit can provide concise results. By adjusting the level of detail in the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand.

[0109] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion function can provide concise suggestions. By adjusting the content of suggestions according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand.

[0110] The competitive analysis unit can estimate the user's emotions and adjust how the competitive analysis results are displayed based on those emotions. For example, if the user is feeling stressed, the competitive analysis unit can provide simple and easy-to-understand results. If the user is relaxed, the unit can provide detailed results. Furthermore, if the user is in a hurry, the unit can provide concise results. By adjusting how the competitive analysis results are displayed according to the user's emotions, the system can provide competitive analysis results that are easy for the user to understand.

[0111] The simulation unit can estimate the user's emotions and adjust how the simulation results are displayed based on those emotions. For example, if the user is nervous, the simulation unit can provide simple and easy-to-understand simulation results. If the user is relaxed, the simulation unit can provide detailed simulation results. Furthermore, if the user is in a hurry, the simulation unit can provide concise simulation results. By adjusting how the simulation results are displayed according to the user's emotions, the system can provide simulation results that are easy for the user to understand.

[0112] The data collection unit can analyze past market data collection history and select the optimal collection method. For example, by analyzing past collection history, it can discover that data collection efficiency is high during specific time periods and concentrate collection during those times. Furthermore, based on past collection history, it can confirm that certain data sources are highly reliable and prioritize their use. Additionally, by analyzing past collection history and optimizing collection frequency, data duplication can be avoided. In short, by analyzing past collection history, the optimal collection method can be selected.

[0113] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on important data and simplified analysis on less important data. It can also prioritize analyses according to the importance of the data. Furthermore, it can apply multiple analytical methods to important data to provide detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0114] The proposal department can adjust the level of detail in proposals based on the importance of the strategies. For example, detailed proposals can be provided for important strategies, while simpler proposals can be provided for less important strategies. Furthermore, the priority of proposals can be determined according to the importance of the strategies. In addition, multiple proposals can be provided for important strategies, offering detailed options. This allows for efficient proposal development by adjusting the level of detail based on the importance of the strategies.

[0115] The competitive analysis department can improve the accuracy of its analysis by considering the relationships between competitors. For example, it can conduct competitive analysis by considering the partnerships between competitors. It can also conduct competitive analysis based on the market share of competitors. Furthermore, it can conduct competitive analysis by considering the product lineups of competitors. In this way, by considering the relationships between competitors, highly accurate competitive analysis becomes possible.

[0116] The simulation unit can select the optimal simulation method by referring to past simulation data. For example, it can select the optimal simulation method based on past simulation data. Furthermore, it can improve the accuracy of simulations by referring to past simulation data. In addition, it can analyze past simulation data and adjust the level of detail of the simulation. This allows for the selection of the optimal simulation method by referring to past simulation data.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects market data. This market data includes sales data, customer data, competitor data, etc. The data collection unit can obtain data from publicly available data on the internet or from internal company databases. The data collection unit can also automatically collect data from the internet using web scraping technology, access internal company databases via APIs to obtain necessary data, or collect data in real time using streaming data processing technology. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to understand data trends and machine learning algorithms to detect data patterns. The analysis unit also performs data clustering and predicts data trends using regression analysis. When using machine learning algorithms, the analysis unit uses the data as training data to build a model. When performing clustering, the analysis unit divides the data into groups and analyzes the characteristics of each group. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. These proposals may include strategic proposals, improvement suggestions, and new business model proposals. For example, the proposal department may propose the optimal strategy for startup managers based on the analysis results, suggest areas for improvement to the managers, and propose new market entry strategies, improvements to business processes, and new revenue models.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, competitive analysis unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects publicly available data from the internet using the control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit makes proposals based on the analysis results using the specific processing unit 290 of the data processing unit 12. The competitive analysis unit analyzes the strategies of competitors using the specific processing unit 290 of the data processing unit 12. The simulation unit simulates the profitability and risks of a new business model using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, competitive analysis unit, and simulation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects publicly available data from the internet using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit makes proposals based on the analysis results using the specific processing unit 290 of the data processing unit 12. The competitive analysis unit analyzes the strategies of competitors using the specific processing unit 290 of the data processing unit 12. The simulation unit simulates the profitability and risks of a new business model using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, competitive analysis unit, and simulation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects publicly available data from the internet using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit makes proposals based on the analysis results using the specific processing unit 290 of the data processing unit 12. The competitive analysis unit analyzes the strategies of competitors using the specific processing unit 290 of the data processing unit 12. The simulation unit simulates the profitability and risks of a new business model using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, competitive analysis unit, and simulation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects publicly available data from the internet using the control unit 46A of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit makes proposals based on the analysis results using the specific processing unit 290 of the data processing unit 12. The competitive analysis unit analyzes the strategies of competitors using the specific processing unit 290 of the data processing unit 12. The simulation unit simulates the profitability and risks of a new business model using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) The data collection department collects market data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes proposals based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) We have a competitive analysis department that analyzes the strategies of our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a simulation unit for simulating business models. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect market data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We analyze collected market data to detect new market trends and changes in consumer preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the analysis results, we propose the optimal strategy for startup entrepreneurs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of market data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past market data collection history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting market data, filter it to focus on specific industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate user sentiment and prioritize the market data to collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting market data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting market data, analyze social media trends and gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the strategy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the strategy category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the strategy will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the strategy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned competitive analysis unit, We estimate user sentiment and adjust the competitive analysis criteria based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned competitive analysis unit, When conducting competitive analysis, consider the relationships between competitors to improve the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned competitive analysis unit, When conducting competitive analysis, the analysis should take into account the attribute information of competitors. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned competitive analysis unit, It estimates user sentiment and adjusts the order in which competitive analysis results are displayed based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned competitive analysis unit, When conducting competitive analysis, the geographical distribution of competitors should be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned competitive analysis unit, When conducting competitive analysis, referencing relevant literature on competitors can improve the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by referring to past simulation data. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned simulation unit, During the simulation, the simulation method is customized based on the current market conditions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by considering geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned simulation unit, During simulations, we refer to relevant literature to improve the accuracy of the simulations. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects market data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes proposals based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. We have a competitive analysis department that analyzes the strategies of our competitors. The system according to feature 1.

3. It includes a simulation unit for simulating business models. The system according to feature 1.

4. The aforementioned collection unit is Collect market data in real time. The system according to feature 1.

5. The aforementioned analysis unit is We analyze collected market data to detect new market trends and changes in consumer preferences. The system according to feature 1.

6. The aforementioned proposal section is, Based on the analysis results, we propose the optimal strategy for startup entrepreneurs. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of market data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze past market data collection history to select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting market data, filter it to focus on specific industries or regions. The system according to feature 1.

10. The aforementioned collection unit is We estimate user sentiment and prioritize the market data to collect based on that estimated sentiment. The system according to feature 1.

Images