System

Abstract

An object of the system according to the embodiment is to analyze real-time data, provide highly accurate prediction information, and promote collaboration with an expert.SOLUTION: A system according to an embodiment includes a data collection part, an analysis part, a prediction providing part, a sharing part, and a collaboration part. The data collection unit collects real-time data. The analysis unit analyzes the data collected by the data collection unit. The prediction providing unit provides prediction information based on the data analyzed by the analysis unit. The sharing unit allows the user to share the prediction information provided by the prediction providing unit. The collaboration unit collaborates with experts and industry leaders based on the information shared by the sharing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technologies had the problem of not being able to provide predictive information using real-time data or share accurate information through collaboration with experts.

[0005] The system according to the embodiment aims to analyze real-time data, provide highly accurate prediction information, and promote collaboration with experts. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a forecast providing unit, a sharing unit, and a collaboration unit. The data collection unit collects real-time data. The analysis unit analyzes the data collected by the data collection unit. The forecast providing unit provides forecast information based on the data analyzed by the analysis unit. The sharing unit allows users to share the forecast information provided by the forecast providing unit. The collaboration unit collaborates with experts and industry leaders based on the information shared by the sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze real-time data, provide highly accurate forecast information, and promote collaboration with experts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The prediction system according to the embodiment of the present invention collects real-time data, analyzes it using a generative AI, predicts the future of the industry, and provides the predicted information, which allows companies to make optimal strategic decisions and maximize profits.

[0029] A prediction system according to an embodiment includes a data collection unit, an analysis unit, a prediction providing unit, a sharing unit, and a collaboration unit. The data collection unit collects real-time data. For example, the data collection unit collects sensor data, user behavior data, transaction data, and the like. The data collection unit can also collect economic indicators and consumer behavior data. For example, the data collection unit collects fluctuations in economic indicators in real time and uses the data for analysis. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis or a machine learning algorithm. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit extracts patterns from the collected data using a machine learning algorithm to predict future trends. The prediction providing unit provides prediction information based on the data analyzed by the analysis unit. For example, the prediction providing unit provides sales forecasts and demand forecasts. The prediction providing unit can also provide risk forecasts. For example, the prediction unit predicts future risks based on the analysis results and provides risk management advice to companies. The sharing unit allows users to share the prediction information provided by the prediction providing unit. For example, the prediction information can be shared via cloud services or social media. The sharing unit can also share the prediction information via a dedicated app. For example, a user uses a dedicated app to view forecast information and share the information with other users. The collaboration unit collaborates with experts and industry leaders based on the information shared by the sharing unit. For example, collaboration occurs through online meetings or joint projects. The collaboration unit can also collaborate through feedback sessions. For example, the accuracy of forecast information is improved based on insights provided by experts and industry leaders. This allows the forecasting system according to the embodiment to make optimal strategic decisions and maximize profits. For example, based on the forecast information provided by the forecasting system, companies can optimize the timing of new product development and market launch. Furthermore, by utilizing user feedback, the prediction accuracy of the generation AI can be improved, enabling it to provide more accurate information.Additionally, collaboration with experts and industry leaders will make generative AI predictions more reliable and support enterprise decision-making.

[0030] The data collection unit collects social media trends and word-of-mouth information, and the analysis unit analyzes the trend and word-of-mouth information. The data collection unit, for example, analyzes hashtags and posts on Twitter and Instagram to identify trends. The data collection unit can also analyze posts on online review sites and forums to reflect consumer opinions and ratings in market forecasts. For example, it integrates data from different platforms to perform comprehensive trend analysis. The analysis unit, for example, collects social media trend data and incorporates it into the analysis. For example, it analyzes hashtags and posts on Twitter and Instagram to identify trends. The analysis unit also collects word-of-mouth information, and the generation AI analyzes the data. For example, it analyzes posts on online review sites and forums to reflect consumer opinions and ratings in market forecasts. This makes it possible to predict market trends based on social media trends and word-of-mouth information.

[0031] The analysis unit can detect sudden market fluctuations early using an anomaly detection algorithm. For example, the analysis unit incorporates an anomaly detection algorithm into the generation AI and monitors market data in real time. For example, it detects sudden price fluctuations or changes in trading volume and detects market fluctuations early. The analysis unit also uses an anomaly detection algorithm to enable the generation AI to predict sudden market fluctuations. For example, it can detect abnormal trading patterns or news events and reflect their impact in market forecasts. For example, it can identify abnormal data points and analyze their causes to help with market forecasts. This allows sudden market fluctuations to be detected early.

[0032] The data collection unit collects data from different industries, and the analysis unit can integrate and analyze the data from the different industries. For example, the data collection unit can combine data from the technology industry and the consumer market to make cross-industry predictions. The data collection unit can also analyze data from the healthcare industry and the entertainment industry to predict new market trends. For example, data from the financial industry and the retail industry can be combined to make a comprehensive market prediction. For example, the analysis unit uses the generation AI to collect, integrate, and analyze data from different industries. For example, data from the technology industry and the consumer market can be combined to make a cross-industry prediction. The analysis unit also integrates data from different industries, and the generation AI can make a market prediction from a multi-faceted perspective. For example, data from the healthcare industry and the entertainment industry can be analyzed to predict new market trends. This makes it possible to integrate data from different industries to predict market trends.

[0033] The data collection unit can collect geographical information, and the analysis unit can analyze the geographical information. The data collection unit can, for example, analyze consumer behavior data and economic indicators for each region and predict market trends specific to that region. The data collection unit can also compare data from urban and rural areas and predict market trends for each region. For example, it analyzes weather data and event information for each region and reflects the impact in the market forecast. In the analysis unit, for example, the generation AI collects geographical information and incorporates it into the analysis. For example, it analyzes consumer behavior data and economic indicators for each region and predicts market trends specific to that region. In addition, the analysis unit has the generation AI make market forecasts for each region based on the geographical information. For example, it compares data from urban and rural areas and predicts market trends for each region. This makes it possible to predict market trends for each region based on geographical information.

[0034] The prediction providing unit can improve reliability by displaying past prediction results and their accuracy rates. The prediction providing unit, for example, displays past prediction results and their accuracy rates in the prediction information provided by the generation AI. For example, it compares past predictions with actual results and indicates their accuracy rates. The prediction providing unit also improves the reliability of the prediction information of the generation AI by displaying past prediction results and their accuracy rates. For example, it can also evaluate the accuracy of predictions based on past successes and failures. For example, it indicates the reliability of predictions based on past data. In this way, by displaying past prediction results and their accuracy rates, the reliability of the prediction information can be improved.

[0035] The prediction providing unit can be customized for different industries or applications to provide prediction information that meets specific needs. For example, the prediction providing unit customizes the prediction information provided by the generation AI for different industries or applications. For example, different prediction information is provided for the technology industry and the consumer market. The prediction providing unit also customizes the generation AI for each industry to provide prediction information that meets specific needs. For example, different predictions can be made for the medical industry and the entertainment industry. For example, prediction information that meets the needs of different industries is provided. In this way, by providing prediction information customized for different industries or applications, it is possible to provide information that meets specific needs.

[0036] The prediction providing unit can visualize the prediction information and make it intuitively understandable with graphs and charts. The prediction providing unit, for example, visualizes the prediction information provided by the generation AI and displays it in graphs and charts. For example, by visually showing the prediction data, it can be made intuitively understandable. Furthermore, in order to provide visualized prediction information, the generation AI generates graphs and charts. For example, the prediction results can be visually displayed so that companies can easily understand them. For example, the prediction data can be displayed in graphs and charts so that it can be made intuitively understandable. In this way, by visualizing the prediction information, it can be made intuitively understandable.

[0037] The sharing unit can share predictions and opinions entered by users with other users, thereby improving prediction accuracy on a community basis. The sharing unit, for example, builds a system for sharing predictions and opinions entered by users with other users. For example, predictions and opinions are shared through an online forum or discussion board. The sharing unit also shares users' predictions and opinions in order to improve prediction accuracy on a community basis. For example, users can exchange opinions with each other and use them as learning data for the generative AI. For example, a predictive model can be jointly built and reflected in the learning of the generative AI. In this way, by sharing users' predictions and opinions, prediction accuracy can be improved throughout the entire community.

[0038] The sharing unit can add expert feedback to predictions and opinions entered by users and use them as learning data for the generative AI. The sharing unit, for example, builds a system that adds expert feedback to predictions and opinions entered by users. For example, experts provide comments and advice on the user's predictions. The sharing unit also uses the user's predictions and opinions as learning data for the generative AI based on the expert feedback. For example, it can build a predictive model that reflects expert knowledge. For example, the accuracy of predictions can be improved based on expert evaluations. In this way, adding expert feedback can be used as learning data for the generative AI.

[0039] The sharing unit can automatically translate predictions and opinions entered by users into different languages ​​and obtain feedback from an international perspective. For example, the sharing unit can automatically translate predictions and opinions entered by users into different languages ​​and collect feedback from an international perspective. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The sharing unit can also build a system that posts the translated predictions and opinions on a multilingual platform and obtains feedback from users around the world. For example, based on the predictions and opinions translated into different languages, it can collect advice and improvement suggestions from an international perspective and reflect them in the learning of the generative AI. In this way, feedback from an international perspective can be obtained by automatically translating into different languages.

[0040] The sharing unit can convert predictions and opinions entered by the user into visual notes or mind maps, making them easier to understand visually. For example, the sharing unit can convert predictions and opinions entered by the user into visual notes and display them visually. For example, it can indicate important points with diagrams or icons. The sharing unit can also convert predictions and opinions into mind map format and visually organize related keywords and concepts. For example, it can make it possible to understand the overall picture of predictions and opinions at a glance. For example, a tool can be developed that automatically generates visual notes and mind maps, allowing users to easily visually display predictions and opinions. This makes it easier to understand predictions and opinions by visually displaying them.

[0041] The collaboration department allows the generative AI to analyze knowledge provided by experts and industry leaders to improve the accuracy of the predictive model. For example, the collaboration department allows the generative AI to analyze knowledge provided by experts and industry leaders to improve the accuracy of the predictive model. For example, the predictive model is improved based on the comments and evaluations of experts. The collaboration department also builds a system in which the generative AI analyzes the knowledge of experts and industry leaders to improve the accuracy of the predictive model. For example, it can build a predictive model that reflects expert feedback. For example, it can analyze expert evaluations to improve the accuracy of predictions. In this way, the accuracy of the predictive model can be improved by analyzing the knowledge of experts and industry leaders.

[0042] The collaboration department can collaborate with experts and industry leaders on an online platform and share knowledge in real time. The collaboration department can, for example, collaborate with experts and industry leaders on an online platform and share knowledge in real time. For example, knowledge can be shared through webinars and online discussions. The collaboration department can also utilize an online platform to achieve real-time collaboration with experts and industry leaders. For example, knowledge can be shared through live chat or video conferencing. For example, online forums and discussion boards can be used. In this way, knowledge can be shared in real time by utilizing an online platform.

[0043] The collaboration unit can visualize the knowledge provided by experts and industry leaders and make it easy to understand intuitively with graphs and charts. The collaboration unit, for example, visualizes the knowledge provided by experts and industry leaders and displays it in graphs and charts. For example, by visually presenting knowledge data, it can be made easy to understand intuitively. The collaboration unit also converts the knowledge of experts and industry leaders into graphs and charts to provide visualized knowledge. For example, it can also visually display the knowledge results so that companies can easily understand them. For example, it can display the knowledge data in graphs and charts so that it can be made easy to understand intuitively. In this way, by visualizing the knowledge, it can be made easy to understand intuitively.

[0044] The prediction providing unit can improve reliability by displaying past solutions and their results in the new solution provided by the generation AI. The prediction providing unit, for example, displays past solutions and their results in the new solution provided by the generation AI. For example, it compares the results with past solutions and indicates their reliability. The prediction providing unit also improves the reliability of the solution provided by the generation AI by displaying past solutions and their results. For example, it can evaluate the accuracy of the solution based on past successes and failures. For example, it indicates the reliability of the solution based on past data. In this way, by displaying past solutions and their results, it is possible to improve the reliability of the new solution.

[0045] The forecast providing unit can incorporate scenario analysis into the new solution provided by the generative AI and present multiple solutions. For example, the forecast providing unit incorporates scenario analysis into the new solution provided by the generative AI. For example, it can present solutions under different conditions and propose strategies that companies should take. The forecast providing unit also uses scenario analysis to allow the generative AI to present multiple solutions. For example, it can also provide solutions that respond to changes in economic conditions and market trends. For example, it can propose a company's strategy based on different scenarios. In this way, by presenting multiple solutions, it is possible to consider the strategy that a company should take from multiple angles.

[0046] The prediction providing unit can customize the new solution provided by the generative AI for different industries or applications, thereby providing solutions that meet specific needs. For example, the prediction providing unit customizes the new solution provided by the generative AI for different industries or applications. For example, it can provide different solutions for the technology industry and the consumer market. The prediction providing unit also customizes the generative AI for each industry in order to provide solutions that meet specific needs. For example, it can provide different solutions for the medical industry and the entertainment industry. For example, it provides solutions that meet the needs of different industries. In this way, by providing solutions customized for different industries or applications, it is possible to provide solutions that meet specific needs.

[0047] The prediction providing unit can visualize the new solution provided by the generative AI and make it intuitively understandable with a graph or chart. The prediction providing unit, for example, visualizes the new solution provided by the generative AI and displays it in a graph or chart. For example, by visually showing the solution, it can be intuitively understood. Furthermore, in order to provide the visualized solution, the generative AI generates a graph or chart. For example, it can also visually display the solution so that it can be easily understood by a company. For example, it can display the solution in a graph or chart so that it can be intuitively understood. In this way, by visualizing the new solution, it can be intuitively understood.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The prediction system can also provide personalized forecast information based on user behavior data. For example, it can analyze a user's past purchase history and browsing history to make individual demand forecasts. It can also analyze a user's behavioral patterns to predict fluctuations in demand during specific times of the day or day of the week. Furthermore, it can forecast demand by region based on the user's location information and optimize sales strategies in specific regions. This allows it to provide forecast information customized for each user, making corporate marketing strategies more effective.

[0050] Forecasting systems can also integrate data from different data sources to make more accurate predictions. For example, they can collect weather and traffic data and analyze its impact on consumer behavior. They can also analyze news articles and blog posts to identify social trends. Furthermore, they can combine a company's internal data with external data to make comprehensive market forecasts. This allows them to utilize multiple data sources to provide more accurate forecasts and support corporate decision-making.

[0051] Predictive systems can also use anomaly detection algorithms to detect supply chain risks early. For example, they can monitor supplier production and logistics data in real time to detect abnormal patterns. They can also analyze weather and natural disaster data to predict their impact on the supply chain. They can also analyze trading partners' financial data and credit information to identify high-risk trading partners. This allows for early detection of supply chain risks and strengthens corporate risk management.

[0052] Prediction systems can also combine data from different industries to discover new business opportunities. For example, they can integrate data from the technology and medical industries to propose new healthcare solutions. They can also analyze data from the entertainment and education industries to predict edutainment market trends. They can also combine data from the financial and retail industries to predict consumer purchasing behavior and find cross-selling opportunities. This allows them to leverage data from different industries to discover new business opportunities and support corporate growth.

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

[0054] Step 1: The data collection unit collects real-time data, such as sensor data, user behavior data, transaction data, economic indicators, and consumer behavior data. This allows fluctuations in economic indicators to be collected in real time and used for analysis. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, using statistical analysis, machine learning algorithms, and data mining techniques to extract patterns from the collected data and predict future trends. Step 3: The forecast provider provides forecast information based on the data analyzed by the analyzer. For example, it provides sales forecasts, demand forecasts, and risk forecasts, and gives risk management advice to companies. Step 4: The sharing unit allows users to share the forecast information provided by the forecast providing unit, for example, via cloud services, social media, or a dedicated app. Step 5: The Collaboration Department collaborates with experts and industry leaders based on the information shared by the Sharing Department, for example through online meetings, joint projects, and feedback sessions, to improve the accuracy of the forecast information.

[0055] (Example 2) The prediction system according to the embodiment of the present invention collects real-time data, analyzes it using a generative AI, predicts the future of the industry, and provides the predicted information, which allows companies to make optimal strategic decisions and maximize profits.

[0056] A prediction system according to an embodiment includes a data collection unit, an analysis unit, a prediction providing unit, a sharing unit, and a collaboration unit. The data collection unit collects real-time data. For example, the data collection unit collects sensor data, user behavior data, transaction data, and the like. The data collection unit can also collect economic indicators and consumer behavior data. For example, the data collection unit collects fluctuations in economic indicators in real time and uses the data for analysis. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis or a machine learning algorithm. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit extracts patterns from the collected data using a machine learning algorithm to predict future trends. The prediction providing unit provides prediction information based on the data analyzed by the analysis unit. For example, the prediction providing unit provides sales forecasts and demand forecasts. The prediction providing unit can also provide risk forecasts. For example, the prediction unit predicts future risks based on the analysis results and provides risk management advice to companies. The sharing unit allows users to share the prediction information provided by the prediction providing unit. For example, the prediction information can be shared via cloud services or social media. The sharing unit can also share the prediction information via a dedicated app. For example, a user uses a dedicated app to view forecast information and share the information with other users. The collaboration unit collaborates with experts and industry leaders based on the information shared by the sharing unit. For example, collaboration occurs through online meetings or joint projects. The collaboration unit can also collaborate through feedback sessions. For example, the accuracy of forecast information is improved based on insights provided by experts and industry leaders. This allows the forecasting system according to the embodiment to make optimal strategic decisions and maximize profits. For example, based on the forecast information provided by the forecasting system, companies can optimize the timing of new product development and market launch. Furthermore, by utilizing user feedback, the prediction accuracy of the generation AI can be improved, enabling it to provide more accurate information.Additionally, collaboration with experts and industry leaders will make generative AI predictions more reliable and support enterprise decision-making.

[0057] The data collection unit can collect user emotional data in real time, and the analysis unit can analyze the emotional data. The data collection unit can, for example, extract emotional data from social media posts and reviews, and predict market trends based on emotional fluctuations. The data collection unit can also grasp emotional fluctuations based on users' online behavior and feedback, and predict market trends. For example, it can analyze user comments and ratings, and reflect emotional fluctuations in market forecasts. The analysis unit can, for example, have the generative AI perform emotional analysis in real time to predict market trends based on emotional data. For example, it can analyze user comments and ratings, and reflect emotional fluctuations in market forecasts. This makes it possible to predict market trends based on user emotional data.

[0058] The data collection unit collects social media trends and word-of-mouth information, and the analysis unit analyzes the trend and word-of-mouth information. The data collection unit, for example, analyzes hashtags and posts on Twitter and Instagram to identify trends. The data collection unit can also analyze posts on online review sites and forums to reflect consumer opinions and ratings in market forecasts. For example, it integrates data from different platforms to perform comprehensive trend analysis. The analysis unit, for example, collects social media trend data and incorporates it into the analysis. For example, it analyzes hashtags and posts on Twitter and Instagram to identify trends. The analysis unit also collects word-of-mouth information, and the generation AI analyzes the data. For example, it analyzes posts on online review sites and forums to reflect consumer opinions and ratings in market forecasts. This makes it possible to predict market trends based on social media trends and word-of-mouth information.

[0059] The analysis unit can detect sudden market fluctuations early using an anomaly detection algorithm. For example, the analysis unit incorporates an anomaly detection algorithm into the generation AI and monitors market data in real time. For example, it detects sudden price fluctuations or changes in trading volume and detects market fluctuations early. The analysis unit also uses an anomaly detection algorithm to enable the generation AI to predict sudden market fluctuations. For example, it can detect abnormal trading patterns or news events and reflect their impact in market forecasts. For example, it can identify abnormal data points and analyze their causes to help with market forecasts. This allows sudden market fluctuations to be detected early.

[0060] The data collection unit collects data from different industries, and the analysis unit can integrate and analyze the data from the different industries. For example, the data collection unit can combine data from the technology industry and the consumer market to make cross-industry predictions. The data collection unit can also analyze data from the healthcare industry and the entertainment industry to predict new market trends. For example, data from the financial industry and the retail industry can be combined to make a comprehensive market prediction. For example, the analysis unit uses the generation AI to collect, integrate, and analyze data from different industries. For example, data from the technology industry and the consumer market can be combined to make a cross-industry prediction. The analysis unit also integrates data from different industries, and the generation AI can make a market prediction from a multi-faceted perspective. For example, data from the healthcare industry and the entertainment industry can be analyzed to predict new market trends. This makes it possible to integrate data from different industries to predict market trends.

[0061] The data collection unit can collect geographical information, and the analysis unit can analyze the geographical information. The data collection unit can, for example, analyze consumer behavior data and economic indicators for each region and predict market trends specific to that region. The data collection unit can also compare data from urban and rural areas and predict market trends for each region. For example, it analyzes weather data and event information for each region and reflects the impact in the market forecast. In the analysis unit, for example, the generation AI collects geographical information and incorporates it into the analysis. For example, it analyzes consumer behavior data and economic indicators for each region and predicts market trends specific to that region. In addition, the analysis unit has the generation AI make market forecasts for each region based on the geographical information. For example, it compares data from urban and rural areas and predicts market trends for each region. This makes it possible to predict market trends for each region based on geographical information.

[0062] The analysis unit can use the emotion estimation function to analyze the emotional background of data entered by users. The analysis unit, for example, uses the emotion estimation function to analyze the emotional background of data entered by users. For example, emotional data can be extracted from user comments and reviews to improve the accuracy of predictions. The analysis unit also allows the generation AI to make market predictions based on the user's emotional data. For example, it can prioritize analysis of data with a high proportion of positive emotions to improve the accuracy of predictions. For example, it can analyze user feedback and ratings and reflect the emotional data in market predictions. This makes it possible to improve the accuracy of predictions based on the user's emotional background.

[0063] The prediction providing unit can reflect the user's emotional data and provide predicted information that is likely to resonate emotionally. The prediction providing unit, for example, reflects the user's emotional data in the predicted information provided by the generation AI. For example, it generates predicted information based on data with a lot of positive emotions and provides a prediction that is likely to resonate emotionally. The prediction providing unit also provides predicted information that is likely to resonate emotionally to the generation AI based on the user's emotional data. For example, it can analyze user feedback and evaluations and reflect the emotional data in the prediction. For example, it can extract emotional data from user comments and reviews and reflect it in the prediction. This can improve user trust by providing predicted information that is likely to resonate emotionally.

[0064] The prediction providing unit can improve reliability by displaying past prediction results and their accuracy rates. The prediction providing unit, for example, displays past prediction results and their accuracy rates in the prediction information provided by the generation AI. For example, it compares past predictions with actual results and indicates their accuracy rates. The prediction providing unit also improves the reliability of the prediction information of the generation AI by displaying past prediction results and their accuracy rates. For example, it can also evaluate the accuracy of predictions based on past successes and failures. For example, it indicates the reliability of predictions based on past data. In this way, by displaying past prediction results and their accuracy rates, the reliability of the prediction information can be improved.

[0065] The prediction providing unit can be customized for different industries or applications to provide prediction information that meets specific needs. For example, the prediction providing unit customizes the prediction information provided by the generation AI for different industries or applications. For example, different prediction information is provided for the technology industry and the consumer market. The prediction providing unit also customizes the generation AI for each industry to provide prediction information that meets specific needs. For example, different predictions can be made for the medical industry and the entertainment industry. For example, prediction information that meets the needs of different industries is provided. In this way, by providing prediction information customized for different industries or applications, it is possible to provide information that meets specific needs.

[0066] The prediction providing unit can visualize the prediction information and make it intuitively understandable with graphs and charts. The prediction providing unit, for example, visualizes the prediction information provided by the generation AI and displays it in graphs and charts. For example, by visually showing the prediction data, it can be made intuitively understandable. Furthermore, in order to provide visualized prediction information, the generation AI generates graphs and charts. For example, the prediction results can be visually displayed so that companies can easily understand them. For example, the prediction data can be displayed in graphs and charts so that it can be made intuitively understandable. In this way, by visualizing the prediction information, it can be made intuitively understandable.

[0067] The prediction providing unit can use the emotion estimation function to collect users' emotional reactions to the predicted information and continuously improve the accuracy of the prediction. The prediction providing unit, for example, uses the emotion estimation function to collect users' emotional reactions to the predicted information. For example, it analyzes user feedback and ratings to improve the accuracy of the prediction. The prediction providing unit also has the generation AI continuously improve the accuracy of the prediction based on the users' emotional reaction data. For example, it can prioritize providing predicted information with a high proportion of positive emotions. For example, it can extract emotional data from users' comments and reviews and reflect it in the prediction. In this way, by collecting users' emotional reactions, the accuracy of the prediction can be continuously improved.

[0068] The sharing unit can use the emotion estimation function to add emotional data to predictions and opinions entered by users and utilize it for the training of the generative AI. The sharing unit, for example, uses the emotion estimation function to add emotional data to predictions and opinions entered by users. For example, emotional data can be extracted from user comments and reviews and utilized for the training of the generative AI. The sharing unit also uses the emotion estimation function to build a system that adds emotional data to user predictions and opinions. For example, it can analyze user feedback and evaluations and reflect the emotional data in the training of the generative AI. For example, data with a high percentage of positive emotions can be prioritized for analysis to improve the accuracy of predictions. In this way, adding emotional data to user predictions and opinions can be utilized for the training of the generative AI.

[0069] The sharing unit can share predictions and opinions entered by users with other users, thereby improving prediction accuracy on a community basis. The sharing unit, for example, builds a system for sharing predictions and opinions entered by users with other users. For example, predictions and opinions are shared through an online forum or discussion board. The sharing unit also shares users' predictions and opinions in order to improve prediction accuracy on a community basis. For example, users can exchange opinions with each other and use them as learning data for the generative AI. For example, a predictive model can be jointly built and reflected in the learning of the generative AI. In this way, by sharing users' predictions and opinions, prediction accuracy can be improved throughout the entire community.

[0070] The sharing unit can add expert feedback to predictions and opinions entered by users and use them as learning data for the generative AI. The sharing unit, for example, builds a system that adds expert feedback to predictions and opinions entered by users. For example, experts provide comments and advice on the user's predictions. The sharing unit also uses the user's predictions and opinions as learning data for the generative AI based on the expert feedback. For example, it can build a predictive model that reflects expert knowledge. For example, the accuracy of predictions can be improved based on expert evaluations. In this way, adding expert feedback can be used as learning data for the generative AI.

[0071] The sharing unit can automatically translate predictions and opinions entered by users into different languages ​​and obtain feedback from an international perspective. For example, the sharing unit can automatically translate predictions and opinions entered by users into different languages ​​and collect feedback from an international perspective. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The sharing unit can also build a system that posts the translated predictions and opinions on a multilingual platform and obtains feedback from users around the world. For example, based on the predictions and opinions translated into different languages, it can collect advice and improvement suggestions from an international perspective and reflect them in the learning of the generative AI. In this way, feedback from an international perspective can be obtained by automatically translating into different languages.

[0072] The sharing unit can convert predictions and opinions entered by the user into visual notes or mind maps, making them easier to understand visually. For example, the sharing unit can convert predictions and opinions entered by the user into visual notes and display them visually. For example, it can indicate important points with diagrams or icons. The sharing unit can also convert predictions and opinions into mind map format and visually organize related keywords and concepts. For example, it can make it possible to understand the overall picture of predictions and opinions at a glance. For example, a tool can be developed that automatically generates visual notes and mind maps, allowing users to easily visually display predictions and opinions. This makes it easier to understand predictions and opinions by visually displaying them.

[0073] The sharing unit can use the emotion estimation function to analyze the emotional background of predictions and opinions entered by users and reflect this in the learning of the generative AI. The sharing unit, for example, uses the emotion estimation function to analyze the emotional background of predictions and opinions entered by users. For example, it extracts emotional data from user comments and reviews and reflects this in the learning of the generative AI. The sharing unit also builds a system in which the generative AI analyzes the emotional background of predictions and opinions based on user emotional data. For example, it can prioritize analysis of data with a high proportion of positive emotions to improve prediction accuracy. For example, it analyzes user feedback and evaluations and reflects the emotional data in the learning of the generative AI. In this way, the emotional background of users' predictions and opinions can be analyzed and reflected in the learning of the generative AI.

[0074] The collaboration unit can use the emotion estimation function to add emotional data to knowledge provided by experts and industry leaders, and use it in the training of the generative AI. For example, the collaboration unit uses the emotion estimation function to add emotional data to knowledge provided by experts and industry leaders. For example, emotional data can be extracted from experts' comments and evaluations and used in the training of the generative AI. The collaboration unit also uses the emotion estimation function to build a system that adds emotional data to the knowledge of experts and industry leaders. For example, it can analyze expert feedback and evaluations and reflect the emotional data in the training of the generative AI. For example, data with a high percentage of positive emotions can be analyzed preferentially to improve prediction accuracy. In this way, adding emotional data to the knowledge of experts and industry leaders can be used in the training of the generative AI.

[0075] The collaboration department allows the generative AI to analyze knowledge provided by experts and industry leaders to improve the accuracy of the predictive model. For example, the collaboration department allows the generative AI to analyze knowledge provided by experts and industry leaders to improve the accuracy of the predictive model. For example, the predictive model is improved based on the comments and evaluations of experts. The collaboration department also builds a system in which the generative AI analyzes the knowledge of experts and industry leaders to improve the accuracy of the predictive model. For example, it can build a predictive model that reflects expert feedback. For example, it can analyze expert evaluations to improve the accuracy of predictions. In this way, the accuracy of the predictive model can be improved by analyzing the knowledge of experts and industry leaders.

[0076] The collaboration department can collaborate with experts and industry leaders on an online platform and share knowledge in real time. The collaboration department can, for example, collaborate with experts and industry leaders on an online platform and share knowledge in real time. For example, knowledge can be shared through webinars and online discussions. The collaboration department can also utilize an online platform to achieve real-time collaboration with experts and industry leaders. For example, knowledge can be shared through live chat or video conferencing. For example, online forums and discussion boards can be used. In this way, knowledge can be shared in real time by utilizing an online platform.

[0077] The collaboration unit can visualize the knowledge provided by experts and industry leaders and make it easy to understand intuitively with graphs and charts. The collaboration unit, for example, visualizes the knowledge provided by experts and industry leaders and displays it in graphs and charts. For example, by visually presenting knowledge data, it can be made easy to understand intuitively. The collaboration unit also converts the knowledge of experts and industry leaders into graphs and charts to provide visualized knowledge. For example, it can also visually display the knowledge results so that companies can easily understand them. For example, it can display the knowledge data in graphs and charts so that it can be made easy to understand intuitively. In this way, by visualizing the knowledge, it can be made easy to understand intuitively.

[0078] The collaboration unit can use the emotion estimation function to analyze the emotional background of knowledge provided by experts and industry leaders and reflect this in the learning of the generative AI. For example, the collaboration unit uses the emotion estimation function to analyze the emotional background of knowledge provided by experts and industry leaders. For example, emotional data can be extracted from experts' comments and evaluations and reflected in the learning of the generative AI. The collaboration unit also builds a system in which the generative AI analyzes the emotional background of knowledge based on the emotional data of experts and industry leaders. For example, it can prioritize the analysis of data with a high proportion of positive emotions to improve prediction accuracy. For example, it can analyze expert feedback and evaluations and reflect the emotional data in the learning of the generative AI. In this way, the emotional background of knowledge from experts and industry leaders can be analyzed and reflected in the learning of the generative AI.

[0079] The prediction providing unit uses an emotion estimation function to reflect the user's emotional data in the new solution provided by the generation AI, thereby providing a solution that is easy to empathize with emotionally. The prediction providing unit, for example, uses the emotion estimation function to reflect the user's emotional data in the new solution provided by the generation AI. For example, it generates a solution based on data with a lot of positive emotions and provides a solution that is easy to empathize with emotionally. The prediction providing unit also provides a solution that is easy to empathize with emotionally based on the user's emotional data. For example, it can analyze user feedback and evaluations and reflect the emotional data in the solution. For example, it can extract emotional data from user comments and reviews and reflect it in the solution. This makes it possible to provide solutions that are easy to empathize with emotionally, thereby improving user trust.

[0080] The prediction providing unit can improve reliability by displaying past solutions and their results in the new solution provided by the generation AI. The prediction providing unit, for example, displays past solutions and their results in the new solution provided by the generation AI. For example, it compares the results with past solutions and indicates their reliability. The prediction providing unit also improves the reliability of the solution provided by the generation AI by displaying past solutions and their results. For example, it can evaluate the accuracy of the solution based on past successes and failures. For example, it indicates the reliability of the solution based on past data. In this way, by displaying past solutions and their results, it is possible to improve the reliability of the new solution.

[0081] The forecast providing unit can incorporate scenario analysis into the new solution provided by the generative AI and present multiple solutions. For example, the forecast providing unit incorporates scenario analysis into the new solution provided by the generative AI. For example, it can present solutions under different conditions and propose strategies that companies should take. The forecast providing unit also uses scenario analysis to allow the generative AI to present multiple solutions. For example, it can also provide solutions that respond to changes in economic conditions and market trends. For example, it can propose a company's strategy based on different scenarios. In this way, by presenting multiple solutions, it is possible to consider the strategy that a company should take from multiple angles.

[0082] The prediction providing unit can customize the new solution provided by the generative AI for different industries or applications, thereby providing solutions that meet specific needs. For example, the prediction providing unit customizes the new solution provided by the generative AI for different industries or applications. For example, it can provide different solutions for the technology industry and the consumer market. The prediction providing unit also customizes the generative AI for each industry in order to provide solutions that meet specific needs. For example, it can provide different solutions for the medical industry and the entertainment industry. For example, it provides solutions that meet the needs of different industries. In this way, by providing solutions customized for different industries or applications, it is possible to provide solutions that meet specific needs.

[0083] The prediction providing unit can visualize the new solution provided by the generative AI and make it intuitively understandable with a graph or chart. The prediction providing unit, for example, visualizes the new solution provided by the generative AI and displays it in a graph or chart. For example, by visually showing the solution, it can be intuitively understood. Furthermore, in order to provide the visualized solution, the generative AI generates a graph or chart. For example, it can also visually display the solution so that it can be easily understood by a company. For example, it can display the solution in a graph or chart so that it can be intuitively understood. In this way, by visualizing the new solution, it can be intuitively understood.

[0084] The prediction providing unit can use the emotion estimation function to collect users' emotional reactions to new solutions and continuously improve the accuracy of the solutions. The prediction providing unit, for example, uses the emotion estimation function to collect users' emotional reactions to new solutions. For example, it analyzes user feedback and ratings to improve the accuracy of the solutions. The prediction providing unit also has the generation AI continuously improve the accuracy of the solutions based on the users' emotional reaction data. For example, it can prioritize providing solutions with a high proportion of positive emotions. For example, it can extract emotional data from users' comments and reviews and reflect it in the solutions. In this way, by collecting users' emotional reactions, the accuracy of the solutions can be continuously improved.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The prediction system can also provide personalized forecast information based on user behavior data. For example, it can analyze a user's past purchase history and browsing history to make individual demand forecasts. It can also analyze a user's behavioral patterns to predict fluctuations in demand during specific times of the day or day of the week. Furthermore, it can forecast demand by region based on the user's location information and optimize sales strategies in specific regions. This allows it to provide forecast information customized for each user, making corporate marketing strategies more effective.

[0087] The prediction system can also predict the effectiveness of advertising based on user emotional data. For example, it can extract emotional data from users' social media posts and reviews to predict the effectiveness of advertising campaigns. It can also improve the accuracy of advertising targeting based on users' online behavior and feedback. Furthermore, it can optimize the creative elements of advertising based on user emotional data to provide ads that are more likely to resonate emotionally. This can maximize the effectiveness of advertising campaigns and support companies' marketing strategies.

[0088] Forecasting systems can also integrate data from different data sources to make more accurate predictions. For example, they can collect weather and traffic data and analyze its impact on consumer behavior. They can also analyze news articles and blog posts to identify social trends. Furthermore, they can combine a company's internal data with external data to make comprehensive market forecasts. This allows them to utilize multiple data sources to provide more accurate forecasts and support corporate decision-making.

[0089] Predictive systems can also use anomaly detection algorithms to detect supply chain risks early. For example, they can monitor supplier production and logistics data in real time to detect abnormal patterns. They can also analyze weather and natural disaster data to predict their impact on the supply chain. They can also analyze trading partners' financial data and credit information to identify high-risk trading partners. This allows for early detection of supply chain risks and strengthens corporate risk management.

[0090] Prediction systems can also combine data from different industries to discover new business opportunities. For example, they can integrate data from the technology and medical industries to propose new healthcare solutions. They can also analyze data from the entertainment and education industries to predict edutainment market trends. They can also combine data from the financial and retail industries to predict consumer purchasing behavior and find cross-selling opportunities. This allows them to leverage data from different industries to discover new business opportunities and support corporate growth.

[0091] The prediction system can also suggest product improvements based on user emotional data. For example, it can extract emotional data from user reviews and feedback to identify product weaknesses and areas for improvement. It can also suggest new features and designs based on user emotional data. Furthermore, it can optimize product marketing strategies and provide messages that resonate emotionally. This makes it possible to utilize user emotional data to suggest product improvements and support companies' product development.

[0092] The prediction system can further improve the quality of customer support based on user emotion data. For example, it can extract emotion data from user inquiries and complaints and suggest appropriate responses to support staff. It can also optimize training programs for support staff based on user emotion data. Furthermore, it can evaluate customer support performance and identify areas for improvement based on user emotion data. This makes it possible to utilize user emotion data to improve the quality of customer support and increase customer satisfaction.

[0093] The prediction system can also provide personalized content based on the user's emotional data. For example, it can extract emotional data from the user's browsing history and feedback and recommend individual content. It can also provide content tailored to specific times of day or situations based on the user's emotional data. Furthermore, it can optimize the creative elements of content based on the user's emotional data and provide content that is likely to resonate emotionally. This makes it possible to utilize the user's emotional data to provide personalized content and improve user engagement.

[0094] The prediction system can further optimize new product launch strategies based on user sentiment data. For example, sentiment data can be extracted from user reviews and feedback to identify the timing and target market for a new product launch. It can also optimize marketing messages for new products based on user sentiment data. Furthermore, it can formulate promotional strategies for new products and implement campaigns that resonate with users emotionally. This allows companies to optimize new product launch strategies and support their success by leveraging user sentiment data.

[0095] The predictive system can further optimize customer loyalty programs based on user emotional data. For example, emotional data can be extracted from users' purchase history and feedback to optimize loyalty program benefits and rewards. The predictive system can also optimize loyalty program communication strategies based on user emotional data. Furthermore, the predictive system can evaluate loyalty program performance and identify areas for improvement based on user emotional data. This allows the system to utilize user emotional data to optimize customer loyalty programs and increase customer loyalty.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The data collection unit collects real-time data, such as sensor data, user behavior data, transaction data, economic indicators, and consumer behavior data. This allows fluctuations in economic indicators to be collected in real time and used for analysis. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, using statistical analysis, machine learning algorithms, and data mining techniques to extract patterns from the collected data and predict future trends. Step 3: The forecast provider provides forecast information based on the data analyzed by the analyzer. For example, it provides sales forecasts, demand forecasts, and risk forecasts, and gives risk management advice to companies. Step 4: The sharing unit allows users to share the forecast information provided by the forecast providing unit, for example, via cloud services, social media, or a dedicated app. Step 5: The Collaboration Department collaborates with experts and industry leaders based on the information shared by the Sharing Department, for example through online meetings, joint projects, and feedback sessions, to improve the accuracy of the forecast information.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 AI 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.

[0146] 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.

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

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

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

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

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is 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.

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

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

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

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

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects real-time data; an analysis unit that analyzes the data collected by the data collection unit; a prediction providing unit that provides prediction information based on the data analyzed by the analysis unit; a sharing unit that allows users to share the prediction information provided by the prediction providing unit; a collaboration unit that collaborates with experts and industry leaders based on the information shared by the sharing unit. A system characterized by:

2. The data collection unit Collecting emotion data of the user in real time; The analysis unit Analyzing the emotion data 2. The system of claim 1.

3. The data collection unit Collect social media trends and word-of-mouth information, The analysis unit Analyzing the trends and the word-of-mouth information 2. The system of claim 1.

4. The analysis unit Anomaly detection algorithms are used to detect sudden market fluctuations early.

2. The system of claim 1.

5. The data collection unit Collect data from the different industries; The analysis unit Integrate and analyze data from the above different industries 2. The system of claim 1.

6. The data collection unit Collect geographical information, The analysis unit Analyzing the geographical information 2. The system of claim 1.

7. The analysis unit Analyzing the emotional context of the data entered by the user 2. The system of claim 1.

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