System

Abstract

An object of the system according to the embodiment is to enable a company to predict a future business environment and quickly respond to a change in the business environment.SOLUTION: A system according to an embodiment includes an economic data collector, an economic trend analyzer, an industry data collector, an industry growth predictor, a consumer data collector, a consumer behavior analyzer, and a business weather map generator. The economic data collection part collects economic data. The economic trend analysis unit analyzes the data collected by the economic data collection unit. The industrial data collector collects industrial data. The industrial growth forecast unit analyzes the data collected by the industrial data collection unit. The consumer data collection unit collects consumer data. The consumer behavior analyzer analyzes the data collected by the consumer data collector. A business weather map generation part generates a business weather map on the basis of analysis results of the economic trend analysis part, the industrial growth prediction part, and the consumer behavior analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for companies to predict future business environments and respond quickly to those changes.

[0005] The system according to the embodiment aims to enable companies to predict future business environments and quickly respond to those changes. [Means for solving the problem]

[0006] The system according to the embodiment includes an economic data collection unit, an economic trend analysis unit, an industrial data collection unit, an industrial growth forecasting unit, a consumer data collection unit, a consumer behavior analysis unit, and a business weather map generation unit. The economic data collection unit collects economic data. The economic trend analysis unit analyzes the data collected by the economic data collection unit. The industrial data collection unit collects industrial data. The industrial growth forecasting unit analyzes the data collected by the industrial data collection unit. The consumer data collection unit collects consumer data. The consumer behavior analysis unit analyzes the data collected by the consumer data collection unit. The business weather map generation unit generates a business weather map based on the analysis results of the economic trend analysis unit, industrial growth forecasting unit, and consumer behavior analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment enables a company to predict the future business environment and quickly respond to changes therein. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The FutureForecast AI system according to an embodiment of the present invention is a B2B AI analysis system that enables companies to predict future business environments and take proactive measures to address those changes. This system uses AI to analyze global economic trends, industry-specific growth forecasts, and changes in consumer behavior, providing companies with a "business weather map." This allows the FutureForecast AI system to provide strategic navigation that helps companies avoid market storms and steer toward a brighter future.

[0029] The FutureForecast AI system according to the embodiment includes an economic data collection unit, an economic trend analysis unit, an industry data collection unit, an industry growth forecast unit, a consumer data collection unit, a consumer behavior analysis unit, and a business weather map generation unit. The economic data collection unit collects economic data. For example, it collects economic indicators such as GDP growth rates, unemployment rates, and inflation rates for each country. The economic data collection unit can also collect data from international organizations and government agencies. For example, it collects data from the IMF and the World Bank. The economic data collection unit can also update the economic data in real time. For example, it can obtain the latest economic data using an online database. The economic trend analysis unit analyzes the collected economic data. For example, the generation AI analyzes the economic data using a text generation AI (e.g., LLM) and predicts future economic conditions. The generation AI can also analyze the economic data using a multimodal generation AI. The generation AI can also extract and analyze trends in the economic data. For example, the text generation AI has learned a large amount of economic data and has advanced analytical capabilities. The multimodal generative AI can handle multiple modalities, including not only text but also images and audio. The generative AI uses data mining technology to extract important trends from economic data and perform analysis based on them. The industrial data collection unit collects industrial data. For example, it collects market size changes and technological innovation trends in various industries, such as manufacturing, services, and IT. The industrial data collection unit can also collect data from industry associations and companies. For example, it collects industry reports and corporate annual reports. Furthermore, the industrial data collection unit can update industrial data in real time. For example, it can obtain the latest industrial data using online databases. The industrial growth forecasting unit analyzes the collected industrial data. For example, the generative AI can analyze industrial data using text generation AI (e.g., LLM) and predict future industry growth. The generative AI can also analyze industrial data using multimodal generative AI. The generative AI can also extract and analyze trends in industrial data.For example, text generation AI has learned large amounts of industrial data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Generative AI uses data mining technology to extract important trends from industrial data and perform analysis based on them. The consumer data collection unit collects consumer data. For example, it collects consumer purchasing data and behavioral data. It can also collect data from marketing research companies and retailers. For example, it collects purchase history and consumer survey data. It can also update consumer data in real time. For example, it can obtain the latest consumer data using an online database. The consumer behavior analysis unit analyzes the collected consumer data. For example, the generative AI uses text generation AI (e.g., LLM) to analyze consumer data and predict changes in consumer behavior. It can also analyze consumer data using multimodal generation AI. It can also extract and analyze trends in consumer data. For example, text generation AI has learned large amounts of consumer data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses data mining technology to extract important trends from consumer data and conducts analysis based on them. The business weather map generation unit generates a business weather map based on the analysis results of the economic trend analysis unit, industry growth forecast unit, and consumer behavior analysis unit. For example, the generation AI uses text generation AI (e.g., LLM) to generate a business weather map. The generation AI can also generate a business weather map using multimodal generation AI. The generation AI can also extract and generate trends for a business weather map. For example, text generation AI has learned a large amount of analysis results and has advanced generation capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses data mining technology to extract important trends from the analysis results and generates a business weather map based on them.As a result, the FutureForecast AI system according to the embodiment enables companies to predict future business environments and strategically navigate to stay ahead of those changes. For example, the output unit provides business weather maps to companies via web or mobile applications. If companies wish to receive feedback in paper form, the results can be printed using a printer. Sending the results via email provides companies with rapid feedback by sending the results directly to them.

[0030] The economic trend analysis unit can analyze political stability or environmental factors in addition to economic indicators. For example, using generative AI, the economic trend analysis unit analyzes political stability and environmental factors in addition to each country's GDP growth rate, unemployment rate, and inflation rate. For example, economic forecasts are made taking into account political instability factors and the risk of environmental disasters. The economic trend analysis unit also analyzes the stability of government and policy consistency to evaluate political stability. For example, it analyzes the frequency of government changes and the history of policy changes. The economic trend analysis unit also analyzes climate change and the risk of natural disasters to evaluate environmental factors. For example, it analyzes temperature fluctuations and changes in precipitation. This enables more comprehensive economic forecasts.

[0031] The economic trend analysis unit can learn patterns of past economic crises and recessions and predict future risks. For example, the economic trend analysis unit uses generative AI to learn data on past economic crises and recessions and predict future risks. For example, it analyzes data on the Lehman Shock and the COVID-19 shock. The economic trend analysis unit also analyzes past economic data to learn patterns of economic crises. For example, it analyzes sudden declines in economic growth rates and sudden increases in unemployment rates. The economic trend analysis unit also analyzes past recession data to learn patterns of recessions. For example, it analyzes periods of continuous GDP declines and declines in consumption. This makes it possible to predict future economic risks.

[0032] The economic trend analysis unit can subdivide global economic trends by region or city and provide local economic forecasts. The economic trend analysis unit can, for example, use generative AI to subdivide global economic trends by region or city and provide local economic forecasts. For example, it can analyze economic indicators for a specific city. The economic trend analysis unit also collects economic data for each region to make regional economic forecasts. For example, it collects data for each region, such as North America, Europe, and Asia. The economic trend analysis unit also collects economic data for each city to make city-specific economic forecasts. For example, it collects data for each city, such as New York, London, and Tokyo. This makes it possible to provide local economic forecasts for each region or city.

[0033] The economic trend analysis department can directly apply the results of economic trend forecasts to specific business units or projects of a company and make specific strategic proposals. For example, the economic trend analysis department uses generative AI to apply the results of economic trend forecasts to specific business units or projects of a company and make specific strategic proposals. For example, it can propose a strategy for entering a new market. The economic trend analysis department also collects data for each business unit in order to make strategic proposals for each business unit. For example, it collects data on product lines and service departments. The economic trend analysis department also collects data for each project in order to make strategic proposals for each project. For example, it collects data on new product development projects and marketing campaigns. This allows it to make specific strategic proposals for specific business units or projects of a company.

[0034] The industry growth forecasting unit can analyze the speed of technological innovation and the impact of emerging technologies. For example, the industry growth forecasting unit uses generative AI to analyze the speed of technological innovation in industry-specific growth forecasts. For example, it predicts the impact of the introduction of new technologies on the market. The industry growth forecasting unit also collects data on the introduction of new technologies to analyze the impact of emerging technologies. For example, it collects data on new technologies such as AI, blockchain, and IoT. The industry growth forecasting unit also analyzes the speed of technological evolution to evaluate the speed of technological innovation. For example, it analyzes the speed of introduction of new technologies and the speed of technological evolution. This makes it possible to analyze the speed of technological innovation and the impact of emerging technologies.

[0035] The industry growth forecasting unit analyzes supply chain data for each industry and can predict supply-side risks and opportunities. The industry growth forecasting unit, for example, uses generative AI to analyze supply chain data for each industry and predict supply-side risks. For example, it detects supply shortages and logistics delays. The industry growth forecasting unit also collects supplier and logistics data to collect supply chain data. For example, it collects supplier production capacity and logistics delay data. The industry growth forecasting unit also analyzes opportunities to find new suppliers and reduce costs to predict supply-side opportunities. For example, it evaluates opportunities to find new suppliers and reduce costs. This makes it possible to predict supply-side risks and opportunities.

[0036] The industry growth forecasting department can apply industry-specific growth forecasts to a company's specific product lines or services to propose specific market strategies. For example, the generative AI applies industry-specific growth forecasts to a company's specific product lines to propose specific market strategies. For example, it determines the timing of new product launches. The industry growth forecasting department also collects data for each product line to propose market strategies for each product line. For example, it collects data on specific product categories or brands. The industry growth forecasting department also collects data for each service to propose market strategies for each service. For example, it collects data on customer support and maintenance services. This makes it possible to propose specific market strategies for a company's specific product lines or services.

[0037] The industry growth forecasting unit can compare industry growth forecasts with industries in different regions or countries and provide strategies from a global perspective. The industry growth forecasting unit can, for example, use generative AI to compare industry growth forecasts with industries in different regions or countries and provide strategies from a global perspective. For example, growth rates by region can be compared. The industry growth forecasting unit also collects data by region to collect industry data by region. For example, data by region such as North America, Europe, and Asia can be collected. The industry growth forecasting unit also collects data by country to collect industry data by country. For example, data by country such as the United States, Germany, and Japan can be collected. This makes it possible to provide strategies from a global perspective.

[0038] The consumer behavior analysis unit analyzes data from social media or review sites in addition to purchase data, making it possible to predict consumer preferences and trends. For example, using generative AI, the consumer behavior analysis unit analyzes data from social media or review sites in addition to consumer purchase data to predict consumer preferences. For example, it analyzes reviews of popular products. The consumer behavior analysis unit also collects data from Twitter and Facebook to collect social media data. For example, it collects data based on specific hashtags or keywords. The consumer behavior analysis unit also collects data from Amazon reviews and Yelp reviews to collect data from review sites. For example, it collects reviews of specific products or services. This makes it possible to predict consumer preferences and trends.

[0039] The consumer behavior analysis unit analyzes changes in consumer behavior by season and event, and can predict consumption patterns for specific periods. The consumer behavior analysis unit, for example, uses generative AI to analyze changes in consumer behavior by season and predict consumption patterns for specific periods. For example, it analyzes purchasing trends in summer and winter. The consumer behavior analysis unit also collects seasonal data to collect consumption data for each season. For example, it collects data for each season, such as spring, summer, fall, and winter. The consumer behavior analysis unit also collects event data to collect consumption data for each event. For example, it collects data for each event, such as Christmas, Black Friday, and the Olympics. This makes it possible to predict consumption patterns for specific periods.

[0040] The consumer behavior analysis unit can analyze changes in consumer behavior for different demographic groups and strengthen target marketing. The consumer behavior analysis unit can, for example, use generative AI to analyze changes in consumer behavior for different demographic groups and strengthen target marketing. For example, it can analyze purchasing trends by age. The consumer behavior analysis unit also collects data such as age, gender, and region to collect data for each demographic group. For example, it collects data for specific age groups, gender, and region. The consumer behavior analysis unit also proposes marketing strategies for specific demographic groups to strengthen target marketing. For example, it proposes advertising campaigns for specific age groups and promotions by region. This can strengthen target marketing.

[0041] The consumer behavior analysis unit can apply changes in consumer behavior to a company's specific marketing campaigns and promotions and propose effective strategies. For example, the consumer behavior analysis unit uses generative AI to apply changes in consumer behavior to a company's specific marketing campaigns and propose effective strategies. For example, it determines the timing and content of the campaign. The consumer behavior analysis unit also collects data on advertising campaigns and promotional events to collect data for each marketing campaign. For example, it collects data on specific advertising campaigns and promotional events. The consumer behavior analysis unit also collects data on discount campaigns and sales with special offers to collect data for each promotion. For example, it collects data on specific discount campaigns and sales with special offers. This makes it possible to propose effective strategies for a company's specific marketing campaigns and promotions.

[0042] The business weather map generation unit can integrate economic, industrial, and consumer behavior data to provide a composite forecast. The business weather map generation unit, for example, uses generative AI to integrate economic, industrial, and consumer behavior data in a business weather map to provide a composite forecast. For example, economic indicators and consumer behavior data are combined. The business weather map generation unit also collects data from various data sources to collect economic, industrial, and consumer data. For example, it collects data from government agencies, industry associations, and marketing research companies. The business weather map generation unit also analyzes the trends of each data to provide a composite forecast. For example, it analyzes trends in economic data, industrial data, and consumer data. This makes it possible to provide a composite forecast.

[0043] The business weather map generation unit can add risk assessment and opportunity assessment elements to support corporate decision-making. The business weather map generation unit, for example, uses generative AI to add risk assessment elements to the business weather map to support corporate decision-making. For example, it evaluates economic risk and market risk. The business weather map generation unit also collects data from risk assessment data sources to collect data for risk assessment. For example, it collects data on financial risk, operational risk, and strategic risk. The business weather map generation unit also collects data from opportunity assessment data sources to collect data for opportunity assessment. For example, it collects data on the development of new markets and the introduction of new products. This can support corporate decision-making.

[0044] The business weather map generation unit customizes the business weather map for specific departments or projects of a company and can make specific strategic proposals. For example, the business weather map generation unit uses a generation AI to customize the business weather map for specific departments of a company and make specific strategic proposals. For example, it proposes a strategy for the marketing department. The business weather map generation unit also collects data for each department in order to collect data for each department. For example, it collects data for the sales department, marketing department, and research and development department. The business weather map generation unit also collects data for each project in order to collect data for each project. For example, it collects data for new product development projects and marketing campaigns. This makes it possible to make specific strategic proposals for specific departments or projects of a company.

[0045] The business weather map generation unit can compare the business weather map with companies in different industries and regions and use it as a benchmark. The business weather map generation unit, for example, uses generation AI to compare the business weather map with companies in different industries and regions and use it as a benchmark. For example, to evaluate the strategies of competing companies. The business weather map generation unit also collects data by industry to collect data by industry. For example, it collects data from the manufacturing, service, and IT industries. The business weather map generation unit also collects data by region to collect data by region. For example, it collects data from North America, Europe, and Asia. This allows it to be compared with companies in different industries and regions and used as a benchmark.

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

[0047] The FutureForecast AI system can further include an energy consumption data collection unit. The energy consumption data collection unit collects energy consumption data from each country or region and analyzes energy market trends. For example, it collects electricity consumption and renewable energy utilization rates. The energy consumption data collection unit can also collect data on energy suppliers and energy policies. For example, it collects information on the operating status of power plants and government energy policies. Furthermore, the energy consumption data collection unit can update energy data in real time. For example, it can obtain the latest energy data using an online database. This makes it possible to understand energy market trends and support corporate energy strategies.

[0048] The FutureForecast AI system can further include a climate data collection unit. The climate data collection unit collects climate data for each region and analyzes the impact of climate change on the economy and industry. For example, it collects temperature fluctuations and changes in precipitation. The climate data collection unit can also collect climate model data to assess the risks of climate change. For example, it collects data on climate change scenarios and forecast models. Furthermore, the climate data collection unit can update climate data in real time. For example, it can obtain data from weather satellites and weather observation stations. This makes it possible to understand the impact of climate change on the economy and industry and support corporate climate risk management.

[0049] The FutureForecast AI system can also be equipped with a cybersecurity data collection unit. The cybersecurity data collection unit collects cybersecurity data from each company and industry and analyzes cyberrisk trends. For example, it collects the number of cyberattacks and the amount of damage. The cybersecurity data collection unit can also collect security incident data to evaluate cybersecurity threats. For example, it collects data on malware infections and data leaks. Furthermore, the cybersecurity data collection unit can update cybersecurity data in real time. For example, it can obtain data from security vendors and government agencies. This allows for an understanding of cyberrisk trends and supports a company's cybersecurity strategy.

[0050] The FutureForecast AI system can further include a logistics data collection unit. The logistics data collection unit collects logistics data from each company and industry and analyzes logistics efficiency and risks. For example, it collects delivery times and inventory levels. The logistics data collection unit can also collect data on logistics incidents to evaluate logistics risks. For example, it collects data on delivery delays and damage. Furthermore, the logistics data collection unit can update logistics data in real time. For example, it can obtain data from logistics companies and warehouse management systems. This allows for an understanding of logistics efficiency and risks and supports a company's logistics strategy.

[0051] The FutureForecast AI system can further include a health data collection unit. The health data collection unit collects health data from each country and region and analyzes the impact of health conditions on the economy and industry. For example, it collects information on the occurrence of infectious diseases and fluctuations in medical expenses. The health data collection unit can also collect data on health indicators to evaluate health risks. For example, it can collect data on average life expectancy and obesity rates. Furthermore, the health data collection unit can update health data in real time. For example, it can obtain data from health organizations and medical institutions. This makes it possible to understand the impact of health conditions on the economy and industry and support corporate health risk management.

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

[0053] Step 1: The Economic Data Collection Department collects economic data. For example, it collects economic indicators such as GDP growth rates, unemployment rates, and inflation rates for each country. It can also collect data from international organizations and government agencies. For example, it collects data from the IMF and the World Bank. It also uses an online database to update the latest economic data in real time. Step 2: The economic trend analysis unit analyzes the collected economic data. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the economic data and predict future economic conditions. The generation AI uses data mining technology to extract trends in the economic data and conduct analysis based on them. Step 3: The Industrial Data Collection Department collects industrial data. For example, it collects information on changes in market size and technological innovation trends in various industries, such as manufacturing, services, and IT. It can also collect data from industry associations and companies. For example, it collects industry reports and annual company reports. It also uses an online database to update the latest industrial data in real time. Step 4: The industry growth forecasting unit analyzes the collected industry data. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the industry data and predict future industry growth. The generation AI uses data mining technology to extract trends in the industry data and conduct analysis based on them. Step 5: The consumer data collection department collects consumer data. For example, it collects consumer purchasing data and behavioral data. It can also collect data from marketing research companies and retailers. For example, it collects data on purchase history and consumer surveys. It also uses an online database to update the latest consumer data in real time. Step 6: The consumer behavior analysis unit analyzes the collected consumer data. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze consumer data and predict changes in consumer behavior. The generation AI uses data mining technology to extract trends in the consumer data and conduct analysis based on them. Step 7: The business weather map generation unit generates a business weather map based on the analysis results of the economic trend analysis unit, industry growth forecast unit, and consumer behavior analysis unit. For example, the generation AI generates a business weather map using text generation AI (e.g., LLM) or multimodal generation AI. The generation AI uses data mining technology to extract important trends from the analysis results and generates a business weather map based on them.

[0054] (Example 2) The FutureForecast AI system according to an embodiment of the present invention is a B2B AI analysis system that enables companies to predict future business environments and take proactive measures to address those changes. This system uses AI to analyze global economic trends, industry-specific growth forecasts, and changes in consumer behavior, providing companies with a "business weather map." This allows the FutureForecast AI system to provide strategic navigation that helps companies avoid market storms and steer toward a brighter future.

[0055] The FutureForecast AI system according to the embodiment includes an economic data collection unit, an economic trend analysis unit, an industry data collection unit, an industry growth forecast unit, a consumer data collection unit, a consumer behavior analysis unit, and a business weather map generation unit. The economic data collection unit collects economic data. For example, it collects economic indicators such as GDP growth rates, unemployment rates, and inflation rates for each country. The economic data collection unit can also collect data from international organizations and government agencies. For example, it collects data from the IMF and the World Bank. The economic data collection unit can also update the economic data in real time. For example, it can obtain the latest economic data using an online database. The economic trend analysis unit analyzes the collected economic data. For example, the generation AI analyzes the economic data using a text generation AI (e.g., LLM) and predicts future economic conditions. The generation AI can also analyze the economic data using a multimodal generation AI. The generation AI can also extract and analyze trends in the economic data. For example, the text generation AI has learned a large amount of economic data and has advanced analytical capabilities. The multimodal generative AI can handle multiple modalities, including not only text but also images and audio. The generative AI uses data mining technology to extract important trends from economic data and perform analysis based on them. The industrial data collection unit collects industrial data. For example, it collects market size changes and technological innovation trends in various industries, such as manufacturing, services, and IT. The industrial data collection unit can also collect data from industry associations and companies. For example, it collects industry reports and corporate annual reports. Furthermore, the industrial data collection unit can update industrial data in real time. For example, it can obtain the latest industrial data using online databases. The industrial growth forecasting unit analyzes the collected industrial data. For example, the generative AI can analyze industrial data using text generation AI (e.g., LLM) and predict future industry growth. The generative AI can also analyze industrial data using multimodal generative AI. The generative AI can also extract and analyze trends in industrial data.For example, text generation AI has learned large amounts of industrial data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Generative AI uses data mining technology to extract important trends from industrial data and perform analysis based on them. The consumer data collection unit collects consumer data. For example, it collects consumer purchasing data and behavioral data. It can also collect data from marketing research companies and retailers. For example, it collects purchase history and consumer survey data. It can also update consumer data in real time. For example, it can obtain the latest consumer data using an online database. The consumer behavior analysis unit analyzes the collected consumer data. For example, the generative AI uses text generation AI (e.g., LLM) to analyze consumer data and predict changes in consumer behavior. It can also analyze consumer data using multimodal generation AI. It can also extract and analyze trends in consumer data. For example, text generation AI has learned large amounts of consumer data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses data mining technology to extract important trends from consumer data and conducts analysis based on them. The business weather map generation unit generates a business weather map based on the analysis results of the economic trend analysis unit, industry growth forecast unit, and consumer behavior analysis unit. For example, the generation AI uses text generation AI (e.g., LLM) to generate a business weather map. The generation AI can also generate a business weather map using multimodal generation AI. The generation AI can also extract and generate trends for a business weather map. For example, text generation AI has learned a large amount of analysis results and has advanced generation capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses data mining technology to extract important trends from the analysis results and generates a business weather map based on them.As a result, the FutureForecast AI system according to the embodiment enables companies to predict future business environments and strategically navigate to stay ahead of those changes. For example, the output unit provides business weather maps to companies via web or mobile applications. If companies wish to receive feedback in paper form, the results can be printed using a printer. Sending the results via email provides companies with rapid feedback by sending the results directly to them.

[0056] The economic trend analysis unit can analyze political stability or environmental factors in addition to economic indicators. For example, using generative AI, the economic trend analysis unit analyzes political stability and environmental factors in addition to each country's GDP growth rate, unemployment rate, and inflation rate. For example, economic forecasts are made taking into account political instability factors and the risk of environmental disasters. The economic trend analysis unit also analyzes the stability of government and policy consistency to evaluate political stability. For example, it analyzes the frequency of government changes and the history of policy changes. The economic trend analysis unit also analyzes climate change and the risk of natural disasters to evaluate environmental factors. For example, it analyzes temperature fluctuations and changes in precipitation. This enables more comprehensive economic forecasts.

[0057] The economic trend analysis unit can learn patterns of past economic crises and recessions and predict future risks. For example, the economic trend analysis unit uses generative AI to learn data on past economic crises and recessions and predict future risks. For example, it analyzes data on the Lehman Shock and the COVID-19 shock. The economic trend analysis unit also analyzes past economic data to learn patterns of economic crises. For example, it analyzes sudden declines in economic growth rates and sudden increases in unemployment rates. The economic trend analysis unit also analyzes past recession data to learn patterns of recessions. For example, it analyzes periods of continuous GDP declines and declines in consumption. This makes it possible to predict future economic risks.

[0058] The economic trend analysis unit uses the emotion estimation function to analyze market sentiment from economic news or social media posts, and can identify factors that influence economic trends. The economic trend analysis unit, for example, uses the emotion estimation function to analyze market sentiment from economic news or social media posts. For example, if there is a lot of positive news, it predicts an economic upturn. The economic trend analysis unit also uses text analysis technology to analyze economic news. For example, it analyzes text from newspaper articles and online news. The economic trend analysis unit also uses emotion analysis algorithms to analyze social media posts. For example, it analyzes posts on Twitter and Facebook. This makes it possible to analyze market sentiment and identify factors that influence economic trends.

[0059] The economic trend analysis unit can subdivide global economic trends by region or city and provide local economic forecasts. The economic trend analysis unit can, for example, use generative AI to subdivide global economic trends by region or city and provide local economic forecasts. For example, it can analyze economic indicators for a specific city. The economic trend analysis unit also collects economic data for each region to make regional economic forecasts. For example, it collects data for each region, such as North America, Europe, and Asia. The economic trend analysis unit also collects economic data for each city to make city-specific economic forecasts. For example, it collects data for each city, such as New York, London, and Tokyo. This makes it possible to provide local economic forecasts for each region or city.

[0060] The economic trend analysis department can directly apply the results of economic trend forecasts to specific business units or projects of a company and make specific strategic proposals. For example, the economic trend analysis department uses generative AI to apply the results of economic trend forecasts to specific business units or projects of a company and make specific strategic proposals. For example, it can propose a strategy for entering a new market. The economic trend analysis department also collects data for each business unit in order to make strategic proposals for each business unit. For example, it collects data on product lines and service departments. The economic trend analysis department also collects data for each project in order to make strategic proposals for each project. For example, it collects data on new product development projects and marketing campaigns. This allows it to make specific strategic proposals for specific business units or projects of a company.

[0061] The economic trend analysis unit uses the emotion estimation function to analyze the emotions of a company's management and employees, and can predict internal reactions to economic trends. For example, the economic trend analysis unit uses the emotion estimation function to analyze the emotions of a company's management and employees, and predict internal reactions to economic trends. For example, it detects anxiety about an economic downturn. In addition, the economic trend analysis unit collects feedback and survey data from management in order to analyze the emotions of management. For example, it collects opinions of the CEO and CFO. In addition, the economic trend analysis unit collects feedback and survey data from employees in order to analyze the emotions of employees. For example, it collects opinions of full-time employees and part-time employees. This makes it possible to predict internal reactions within the company.

[0062] The industry growth forecasting unit can analyze the speed of technological innovation and the impact of emerging technologies. For example, the industry growth forecasting unit uses generative AI to analyze the speed of technological innovation in industry-specific growth forecasts. For example, it predicts the impact of the introduction of new technologies on the market. The industry growth forecasting unit also collects data on the introduction of new technologies to analyze the impact of emerging technologies. For example, it collects data on new technologies such as AI, blockchain, and IoT. The industry growth forecasting unit also analyzes the speed of technological evolution to evaluate the speed of technological innovation. For example, it analyzes the speed of introduction of new technologies and the speed of technological evolution. This makes it possible to analyze the speed of technological innovation and the impact of emerging technologies.

[0063] The industry growth forecasting unit analyzes supply chain data for each industry and can predict supply-side risks and opportunities. The industry growth forecasting unit, for example, uses generative AI to analyze supply chain data for each industry and predict supply-side risks. For example, it detects supply shortages and logistics delays. The industry growth forecasting unit also collects supplier and logistics data to collect supply chain data. For example, it collects supplier production capacity and logistics delay data. The industry growth forecasting unit also analyzes opportunities to find new suppliers and reduce costs to predict supply-side opportunities. For example, it evaluates opportunities to find new suppliers and reduce costs. This makes it possible to predict supply-side risks and opportunities.

[0064] The industry growth forecasting unit uses the emotion estimation function to analyze the opinions of industry experts and leaders to gain emotional insights into the future of the industry. For example, the industry growth forecasting unit uses the emotion estimation function to analyze the opinions of industry experts and leaders to gain emotional insights into the future of the industry. For example, if there are many positive opinions from experts, the industry growth forecasting unit predicts industry growth. The industry growth forecasting unit also conducts interviews and surveys to collect the opinions of industry experts. For example, it collects the opinions of analysts and consultants. The industry growth forecasting unit also conducts panel discussions and feedback sessions to collect the opinions of leaders. For example, it collects the opinions of company CEOs and leaders of industry associations. This allows for emotional insights into the future of the industry.

[0065] The industry growth forecasting department can apply industry-specific growth forecasts to a company's specific product lines or services to propose specific market strategies. For example, the generative AI applies industry-specific growth forecasts to a company's specific product lines to propose specific market strategies. For example, it determines the timing of new product launches. The industry growth forecasting department also collects data for each product line to propose market strategies for each product line. For example, it collects data on specific product categories or brands. The industry growth forecasting department also collects data for each service to propose market strategies for each service. For example, it collects data on customer support and maintenance services. This makes it possible to propose specific market strategies for a company's specific product lines or services.

[0066] The industry growth forecasting unit can compare industry growth forecasts with industries in different regions or countries and provide strategies from a global perspective. The industry growth forecasting unit can, for example, use generative AI to compare industry growth forecasts with industries in different regions or countries and provide strategies from a global perspective. For example, growth rates by region can be compared. The industry growth forecasting unit also collects data by region to collect industry data by region. For example, data by region such as North America, Europe, and Asia can be collected. The industry growth forecasting unit also collects data by country to collect industry data by country. For example, data by country such as the United States, Germany, and Japan can be collected. This makes it possible to provide strategies from a global perspective.

[0067] The industry growth forecasting unit can use the emotion estimation function to analyze the emotions of consumers and investors and predict market reactions to industry growth. For example, the industry growth forecasting unit uses the emotion estimation function to analyze the emotions of consumers and investors and predict market reactions to industry growth. For example, if there are a lot of positive emotions, the possibility of growth is evaluated. The industry growth forecasting unit also collects consumer review and feedback data to analyze consumer emotions. For example, it collects data from online reviews and surveys. The industry growth forecasting unit also collects investor feedback and market data to analyze investor emotions. For example, it collects stock price fluctuations and investor opinions. This makes it possible to predict market reactions to industry growth.

[0068] The consumer behavior analysis unit analyzes data from social media or review sites in addition to purchase data, making it possible to predict consumer preferences and trends. For example, using generative AI, the consumer behavior analysis unit analyzes data from social media or review sites in addition to consumer purchase data to predict consumer preferences. For example, it analyzes reviews of popular products. The consumer behavior analysis unit also collects data from Twitter and Facebook to collect social media data. For example, it collects data based on specific hashtags or keywords. The consumer behavior analysis unit also collects data from Amazon reviews and Yelp reviews to collect data from review sites. For example, it collects reviews of specific products or services. This makes it possible to predict consumer preferences and trends.

[0069] The consumer behavior analysis unit analyzes changes in consumer behavior by season and event, and can predict consumption patterns for specific periods. The consumer behavior analysis unit, for example, uses generative AI to analyze changes in consumer behavior by season and predict consumption patterns for specific periods. For example, it analyzes purchasing trends in summer and winter. The consumer behavior analysis unit also collects seasonal data to collect consumption data for each season. For example, it collects data for each season, such as spring, summer, fall, and winter. The consumer behavior analysis unit also collects event data to collect consumption data for each event. For example, it collects data for each event, such as Christmas, Black Friday, and the Olympics. This makes it possible to predict consumption patterns for specific periods.

[0070] The consumer behavior analysis unit can use the emotion estimation function to analyze emotions from consumer reviews and feedback and identify areas for improvement in products and services. For example, the consumer behavior analysis unit can use the emotion estimation function to analyze emotions from consumer reviews and feedback and identify areas for improvement in products and services. For example, it can improve areas that have a lot of negative reviews. In addition, the consumer behavior analysis unit collects data from online reviews and surveys to collect review data. For example, it collects data from Amazon reviews and Yelp reviews. In addition, the consumer behavior analysis unit collects customer surveys and support center records to collect feedback data. For example, it collects customer satisfaction surveys and support center feedback. This makes it possible to identify areas for improvement in products and services.

[0071] The consumer behavior analysis unit can analyze changes in consumer behavior for different demographic groups and strengthen target marketing. The consumer behavior analysis unit can, for example, use generative AI to analyze changes in consumer behavior for different demographic groups and strengthen target marketing. For example, it can analyze purchasing trends by age. The consumer behavior analysis unit also collects data such as age, gender, and region to collect data for each demographic group. For example, it collects data for specific age groups, gender, and region. The consumer behavior analysis unit also proposes marketing strategies for specific demographic groups to strengthen target marketing. For example, it proposes advertising campaigns for specific age groups and promotions by region. This can strengthen target marketing.

[0072] The consumer behavior analysis unit can apply changes in consumer behavior to a company's specific marketing campaigns and promotions and propose effective strategies. For example, the consumer behavior analysis unit uses generative AI to apply changes in consumer behavior to a company's specific marketing campaigns and propose effective strategies. For example, it determines the timing and content of the campaign. The consumer behavior analysis unit also collects data on advertising campaigns and promotional events to collect data for each marketing campaign. For example, it collects data on specific advertising campaigns and promotional events. The consumer behavior analysis unit also collects data on discount campaigns and sales with special offers to collect data for each promotion. For example, it collects data on specific discount campaigns and sales with special offers. This makes it possible to propose effective strategies for a company's specific marketing campaigns and promotions.

[0073] The consumer behavior analysis unit uses the emotion estimation function to monitor consumer purchasing willingness and satisfaction in real time, and can make instant marketing adjustments. The consumer behavior analysis unit, for example, uses the emotion estimation function to monitor consumer purchasing willingness and satisfaction in real time, and can make instant marketing adjustments. For example, the effectiveness of a campaign is evaluated in real time. The consumer behavior analysis unit also scores purchasing willingness and conducts questionnaire surveys to monitor purchasing willingness. For example, it scores consumer purchasing willingness and monitors it in real time. The consumer behavior analysis unit also conducts customer satisfaction surveys and NPS (Net Promoter Score) to monitor satisfaction. For example, it monitors customer satisfaction in real time and makes instant marketing adjustments. This makes it possible to monitor consumer purchasing willingness and satisfaction in real time, and make instant marketing adjustments.

[0074] The business weather map generation unit can integrate economic, industrial, and consumer behavior data to provide a composite forecast. The business weather map generation unit, for example, uses generative AI to integrate economic, industrial, and consumer behavior data in a business weather map to provide a composite forecast. For example, economic indicators and consumer behavior data are combined. The business weather map generation unit also collects data from various data sources to collect economic, industrial, and consumer data. For example, it collects data from government agencies, industry associations, and marketing research companies. The business weather map generation unit also analyzes the trends of each data to provide a composite forecast. For example, it analyzes trends in economic data, industrial data, and consumer data. This makes it possible to provide a composite forecast.

[0075] The business weather map generation unit can add risk assessment and opportunity assessment elements to support corporate decision-making. The business weather map generation unit, for example, uses generative AI to add risk assessment elements to the business weather map to support corporate decision-making. For example, it evaluates economic risk and market risk. The business weather map generation unit also collects data from risk assessment data sources to collect data for risk assessment. For example, it collects data on financial risk, operational risk, and strategic risk. The business weather map generation unit also collects data from opportunity assessment data sources to collect data for opportunity assessment. For example, it collects data on the development of new markets and the introduction of new products. This can support corporate decision-making.

[0076] The business weather map generation unit uses the emotion estimation function to analyze the emotions of the company's management and employees regarding the business weather map forecast results, and can predict internal reactions. The business weather map generation unit, for example, uses the emotion estimation function to analyze the emotions of the company's management and employees regarding the business weather map forecast results, and predict internal reactions. For example, it detects the management's sense of anxiety. The business weather map generation unit also collects feedback and survey data from the management to analyze the management's emotions. For example, it collects opinions from the CEO and CFO. The business weather map generation unit also collects employee feedback and survey data to analyze employee emotions. For example, it collects opinions from full-time employees and part-time employees. This makes it possible to predict internal reactions within the company.

[0077] The business weather map generation unit customizes the business weather map for specific departments or projects of a company and can make specific strategic proposals. For example, the business weather map generation unit uses a generation AI to customize the business weather map for specific departments of a company and make specific strategic proposals. For example, it proposes a strategy for the marketing department. The business weather map generation unit also collects data for each department in order to collect data for each department. For example, it collects data for the sales department, marketing department, and research and development department. The business weather map generation unit also collects data for each project in order to collect data for each project. For example, it collects data for new product development projects and marketing campaigns. This makes it possible to make specific strategic proposals for specific departments or projects of a company.

[0078] The business weather map generation unit can compare the business weather map with companies in different industries and regions and use it as a benchmark. The business weather map generation unit, for example, uses generation AI to compare the business weather map with companies in different industries and regions and use it as a benchmark. For example, to evaluate the strategies of competing companies. The business weather map generation unit also collects data by industry to collect data by industry. For example, it collects data from the manufacturing, service, and IT industries. The business weather map generation unit also collects data by region to collect data by region. For example, it collects data from North America, Europe, and Asia. This allows it to be compared with companies in different industries and regions and used as a benchmark.

[0079] The business weather map generation unit uses the emotion estimation function to analyze market reactions to the business weather map forecast results and can support strategic adjustments for companies. The business weather map generation unit, for example, uses the emotion estimation function to analyze market reactions to the business weather map forecast results and can support strategic adjustments for companies. For example, if there are many positive reactions, an aggressive strategy is proposed. The business weather map generation unit also collects market data to analyze market reactions. For example, it collects data on the consumer market, the corporate market, and the international market. The business weather map generation unit also collects data on stock price fluctuations and consumer purchasing behavior to analyze reactions. For example, it collects data on stock price fluctuations and consumer purchasing behavior. This allows for analysis of market reactions and can support strategic adjustments for companies.

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

[0081] The FutureForecast AI system can further include an energy consumption data collection unit. The energy consumption data collection unit collects energy consumption data from each country or region and analyzes energy market trends. For example, it collects electricity consumption and renewable energy utilization rates. The energy consumption data collection unit can also collect data on energy suppliers and energy policies. For example, it collects information on the operating status of power plants and government energy policies. Furthermore, the energy consumption data collection unit can update energy data in real time. For example, it can obtain the latest energy data using an online database. This makes it possible to understand energy market trends and support corporate energy strategies.

[0082] The economic trend analysis unit uses the emotion estimation function to analyze consumer purchasing willingness and investor confidence, and can reflect this in economic forecasts. For example, if consumer purchasing willingness is increasing, an economic upturn is predicted. The economic trend analysis unit also collects consumer review and feedback data to analyze consumer purchasing willingness. For example, it collects data from online reviews and questionnaire surveys. The economic trend analysis unit also collects investor feedback and market data to analyze investor confidence. For example, it collects stock price fluctuations and investor opinions. This allows consumer purchasing willingness and investor confidence to be analyzed and reflected in economic forecasts.

[0083] The FutureForecast AI system can further include a climate data collection unit. The climate data collection unit collects climate data for each region and analyzes the impact of climate change on the economy and industry. For example, it collects temperature fluctuations and changes in precipitation. The climate data collection unit can also collect climate model data to assess the risks of climate change. For example, it collects data on climate change scenarios and forecast models. Furthermore, the climate data collection unit can update climate data in real time. For example, it can obtain data from weather satellites and weather observation stations. This makes it possible to understand the impact of climate change on the economy and industry and support corporate climate risk management.

[0084] The economic trend analysis unit uses the emotion estimation function to analyze the motivation and stress levels of company employees and predict internal reactions to economic trends. For example, if employee motivation is declining, it predicts an economic downturn. The economic trend analysis unit also collects employee feedback and survey data to analyze employee motivation. For example, it collects data from employee satisfaction surveys and stress checks. The economic trend analysis unit also collects employee health data and vacation status to analyze stress levels. For example, it collects health checkup results and vacation history. This makes it possible to analyze employee motivation and stress levels and predict internal reactions to economic trends.

[0085] The FutureForecast AI system can also be equipped with a cybersecurity data collection unit. The cybersecurity data collection unit collects cybersecurity data from each company and industry and analyzes cyberrisk trends. For example, it collects the number of cyberattacks and the amount of damage. The cybersecurity data collection unit can also collect security incident data to evaluate cybersecurity threats. For example, it collects data on malware infections and data leaks. Furthermore, the cybersecurity data collection unit can update cybersecurity data in real time. For example, it can obtain data from security vendors and government agencies. This allows for an understanding of cyberrisk trends and supports a company's cybersecurity strategy.

[0086] The economic trend analysis unit uses the emotion estimation function to analyze consumer brand loyalty and product satisfaction, and can identify factors that influence economic trends. For example, high brand loyalty predicts economic stability. The economic trend analysis unit also collects consumer review and feedback data to analyze brand loyalty. For example, it collects data from online reviews and questionnaire surveys. The economic trend analysis unit also collects consumer feedback and support center data to analyze product satisfaction. For example, it collects customer satisfaction surveys and support center feedback. This makes it possible to analyze consumer brand loyalty and product satisfaction, and identify factors that influence economic trends.

[0087] The FutureForecast AI system can further include a logistics data collection unit. The logistics data collection unit collects logistics data from each company and industry and analyzes logistics efficiency and risks. For example, it collects delivery times and inventory levels. The logistics data collection unit can also collect data on logistics incidents to evaluate logistics risks. For example, it collects data on delivery delays and damage. Furthermore, the logistics data collection unit can update logistics data in real time. For example, it can obtain data from logistics companies and warehouse management systems. This allows for an understanding of logistics efficiency and risks and supports a company's logistics strategy.

[0088] The industry growth forecasting unit can use the emotion estimation function to analyze consumer purchasing willingness and investor confidence, and predict market reaction to industry growth. For example, if consumer purchasing willingness is increasing, industry growth is predicted. The industry growth forecasting unit also collects consumer review and feedback data to analyze consumer purchasing willingness. For example, it collects data from online reviews and surveys. The industry growth forecasting unit also collects investor feedback and market data to analyze investor confidence. For example, it collects stock price fluctuations and investor opinions. This makes it possible to analyze consumer purchasing willingness and investor confidence, and predict market reaction to industry growth.

[0089] The FutureForecast AI system can further include a health data collection unit. The health data collection unit collects health data from each country and region and analyzes the impact of health conditions on the economy and industry. For example, it collects information on the occurrence of infectious diseases and fluctuations in medical expenses. The health data collection unit can also collect data on health indicators to evaluate health risks. For example, it can collect data on average life expectancy and obesity rates. Furthermore, the health data collection unit can update health data in real time. For example, it can obtain data from health organizations and medical institutions. This makes it possible to understand the impact of health conditions on the economy and industry and support corporate health risk management.

[0090] The industry growth forecasting department uses the emotion estimation function to analyze the opinions of industry experts and leaders to gain emotional insights into the future of the industry. For example, if there are many positive opinions from experts, the department predicts industry growth. The industry growth forecasting department also conducts interviews and surveys to collect the opinions of industry experts. For example, it collects the opinions of analysts and consultants. The industry growth forecasting department also conducts panel discussions and feedback sessions to collect the opinions of leaders. For example, it collects the opinions of company CEOs and leaders of industry associations. This allows the department to gain emotional insights into the future of the industry.

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

[0092] Step 1: The Economic Data Collection Department collects economic data. For example, it collects economic indicators such as GDP growth rates, unemployment rates, and inflation rates for each country. It can also collect data from international organizations and government agencies. For example, it collects data from the IMF and the World Bank. It also uses an online database to update the latest economic data in real time. Step 2: The economic trend analysis unit analyzes the collected economic data. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the economic data and predict future economic conditions. The generation AI uses data mining technology to extract trends in the economic data and conduct analysis based on them. Step 3: The Industrial Data Collection Department collects industrial data. For example, it collects information on changes in market size and technological innovation trends in various industries, such as manufacturing, services, and IT. It can also collect data from industry associations and companies. For example, it collects industry reports and annual company reports. It also uses an online database to update the latest industrial data in real time. Step 4: The industry growth forecasting unit analyzes the collected industry data. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the industry data and predict future industry growth. The generation AI uses data mining technology to extract trends in the industry data and conduct analysis based on them. Step 5: The consumer data collection department collects consumer data. For example, it collects consumer purchasing data and behavioral data. It can also collect data from marketing research companies and retailers. For example, it collects data on purchase history and consumer surveys. It also uses an online database to update the latest consumer data in real time. Step 6: The consumer behavior analysis unit analyzes the collected consumer data. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze consumer data and predict changes in consumer behavior. The generation AI uses data mining technology to extract trends in the consumer data and conduct analysis based on them. Step 7: The business weather map generation unit generates a business weather map based on the analysis results of the economic trend analysis unit, industry growth forecast unit, and consumer behavior analysis unit. For example, the generation AI generates a business weather map using text generation AI (e.g., LLM) or multimodal generation AI. The generation AI uses data mining technology to extract important trends from the analysis results and generates a business weather map based on them.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an economic data collection unit that collects economic data; an economic trend analysis unit that analyzes the data collected by the economic data collection unit; an industrial data collection unit that collects industrial data; an industry growth forecasting unit that analyzes the data collected by the industry data collecting unit; a consumer data collection unit that collects consumer data; a consumer behavior analysis unit that analyzes the data collected by the consumer data collection unit; a business weather map generation unit that generates a business weather map based on the analysis results of the economic trend analysis unit, the industry growth forecast unit, and the consumer behavior analysis unit. A system characterized by:

2. The economic trend analysis unit Analyze market sentiment from economic news or social media posts to identify factors influencing economic trends 2. The system of claim 1.

3. The industry growth forecasting unit Analyze the pace of technological change and the impact of emerging technologies 2. The system of claim 1.

4. The consumer behavior analysis unit Analyze purchasing data as well as data from social media or review sites to predict consumer preferences and trends.

2. The system of claim 1.

5. The business weather chart generation unit Analyze the emotions of company management and employees regarding the forecast results of the business weather map and predict internal reactions 2. The system of claim 1.

6. The economic trend analysis unit Breaking down global economic trends by region or city to provide local economic forecasts 2. The system of claim 1.

7. The industry growth forecasting unit Analyze the opinions of industry experts and leaders to gain emotional insight into the future of the industry 2. The system of claim 1.

8. The consumer behavior analysis unit Monitor consumer purchasing intent and satisfaction in real time and make immediate marketing adjustments 2. The system of claim 1.

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