System

Abstract

An object of a system according to an embodiment is to propose a type of industry or a brand suitable for investment based on a future event or a situation in the world.SOLUTION: A system includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects information on future events and situations in the world. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes a type of business and a brand suitable for investment on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not yet fully capable of efficiently suggesting industries and stocks suitable for investment based on future events and global conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to propose industries and stocks suitable for investment based on future events and world conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects information on future events and world situations. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes industries and stocks suitable for investment based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest industries and stocks suitable for investment based on future events and world conditions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The investment proposal system according to an embodiment of the present invention analyzes future events and world situations, and proposes the industries that should be purchased at present, the process that led to the prediction, and multiple related stocks. This allows the investment proposal system to formulate an investment strategy based on future events and situations.

[0029] An investment proposal system according to an embodiment includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects information about future events and world situations. For example, the information collection unit collects information about large-scale sporting events such as the Olympics and the World Cup. The information collection unit can also collect information about international conferences and economic forums. The information collection unit can also collect information about the implementation of new laws and regulations. For example, the information collection unit collects information by scraping online news sites and official announcements. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to analyze information about future events and situations. The analysis unit can also learn data from similar past events to predict the impact of future events. The analysis unit can also use an emotion estimation function to analyze public sentiment toward future events and consider the impact of those sentiments on the market. For example, the analysis unit analyzes posts on social media and blogs to estimate public sentiment. The suggestion unit suggests industries and stocks suitable for investment based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests that the tourism industry and the sporting goods industry will be in the spotlight if the Olympics are held. The suggestion unit may also suggest that renewable energy and environmental technology-related industries will be in the spotlight if new environmental regulations are enacted. The suggestion unit may also suggest specific stocks related to the proposed industries. For example, if the tourism industry is in the spotlight, the suggestion unit may suggest stocks of tourism-related companies. In this way, the investment suggestion system according to the embodiment can suggest industries and stocks suitable for investment based on future events and situations. For example, by purchasing tourism-related stocks before the Olympics, a user can invest in anticipation of increased demand due to the event. Furthermore, by purchasing renewable energy-related stocks before new environmental regulations are enacted, a user can invest in anticipation of market expansion due to the regulations.

[0030] The information collection unit can also collect information from at least one unofficial information source, such as a social networking site or a blog. The information collection unit, for example, builds a system that collects and analyzes information in real time from social networking sites or blogs. For example, it analyzes posts on Twitter or Facebook to understand trends related to future events. The information collection unit can also analyze blog articles to collect information related to future events or situations. For example, it analyzes the content of blog articles and extracts important information. By collecting information from unofficial information sources, a wider variety of data can be used for analysis.

[0031] The analysis unit can learn data from similar past events and predict the impact of future events. For example, the analysis unit can have a generative AI learn data from similar past events and build a system that predicts the impact of future events. For example, it can make predictions based on economic data from past Olympic Games. The analysis unit can also learn data from past economic crises and election results and predict the impact of future events. For example, it can predict the impact of future economic events based on data from past economic crises. In this way, by learning data from similar past events, it can more accurately predict the impact of future events.

[0032] The information collection unit can analyze geographical data and evaluate the impact of events or situations in each region. For example, the information collection unit constructs a system in which a generative AI analyzes geographical data and evaluates the impact of events or situations in each region. For example, it predicts the impact of an event in a specific region on the economy of that region. The information collection unit can also analyze GPS data and evaluate the impact of events in each region. For example, it predicts the impact of an event in a specific region on the tourism industry. The information collection unit can also analyze regional statistical data and evaluate the impact of situations in each region. For example, it analyzes economic indicators in a specific region and evaluates the economic situation in that region. In this way, the impact on each region can be evaluated by analyzing geographical data.

[0033] The analysis unit can incorporate the opinions of experts from different industries and perform analysis from a more multifaceted perspective. For example, the analysis unit builds a system in which the generative AI collects the opinions of experts from different industries and performs analysis based on that data. For example, the analysis unit incorporates the opinions of economists and technical experts. The analysis unit can also collect the opinions of marketing experts and reflect them in the analysis. For example, based on the opinions of marketing experts, it can evaluate the impact of future events on the market. The analysis unit can also collect the opinions of legal experts and reflect them in the analysis. For example, based on the opinions of legal experts, it can evaluate the impact of new regulations on the market. This makes it possible to incorporate the opinions of experts from different industries and perform a more multifaceted analysis.

[0034] The proposal department can perform risk assessment for industries based on past investment performance data. For example, the proposal department constructs a system in which a generation AI analyzes past investment performance data and performs risk assessment for the proposed industry. For example, it calculates a risk score based on past data. The proposal department can also analyze past stock price data and perform risk assessment for industries. For example, it can assess risk based on stock price fluctuations. The proposal department can also analyze past financial reports and perform risk assessment for industries. For example, it can assess risk based on financial indicators. In this way, risk assessment is performed based on past investment performance data, improving the reliability of the proposal.

[0035] The proposal department can evaluate the future growth potential of an industry based on technological innovation or market trends. For example, the proposal department uses generative AI to analyze relevant technological innovations and market trends and build a system to evaluate the future growth potential of the proposed industry. For example, it evaluates the impact of the introduction of new technology on an industry. The proposal department can also analyze patent acquisition and progress in research and development to evaluate the growth potential of an industry. For example, it evaluates the impact of acquiring a new patent on an industry. The proposal department can also analyze changes in consumer behavior and the trends of competitors to evaluate the growth potential of an industry. For example, it can propose industries where consumer demand is increasing. This makes it possible to evaluate future growth potential by taking technological innovation and market trends into account.

[0036] The proposal unit can consider investment strategies for an industry and propose an optimal strategy based on at least one of short-term investment or long-term investment. The proposal unit, for example, builds a system in which the generation AI analyzes different investment strategies and proposes the optimal strategy for the proposed industry. For example, it evaluates the risks and returns of short-term investment and long-term investment. The proposal unit can also evaluate the risks and returns of short-term investment and propose an industry. For example, it evaluates industries based on short-term market fluctuations. The proposal unit can also evaluate the risks and returns of long-term investment and propose an industry. For example, it evaluates industries based on long-term growth potential. This makes it possible to propose an optimal strategy by considering different investment strategies.

[0037] The proposal department can perform a detailed evaluation of stocks based on a company's financial data or performance forecast. For example, the proposal department builds a system in which a generation AI analyzes a company's financial data and performs a detailed evaluation of the proposed stocks. For example, the evaluation is performed based on profitability and debt ratio. The proposal department can also analyze a company's performance forecast and evaluate stocks. For example, the evaluation is performed based on past performance data and economic indicators. The proposal department can also analyze analyst forecasts and evaluate stocks. For example, the evaluation is performed based on analyst reports. In this way, detailed evaluations based on a company's financial data and performance forecasts improve the reliability of proposals.

[0038] The proposal department can evaluate a company's management team or strategy for each stock and take management quality into consideration. For example, the proposal department will build a system in which generative AI analyzes data on a company's management team and evaluates the proposed stocks. For example, the evaluation will be based on the management team's past performance and leadership. The proposal department can also analyze a company's strategy and evaluate stocks. For example, the evaluation will be based on growth strategy and marketing strategy. The proposal department can also analyze a company's technology strategy and evaluate stocks. For example, the impact the introduction of new technology will have on the company. This makes it possible to make proposals that take management quality into consideration by evaluating a company's management team and strategy.

[0039] The proposal department can compare stocks with stocks in different industries and perform a relative evaluation. For example, the proposal department builds a system in which the generation AI analyzes stocks in different industries and performs a relative evaluation of the proposed stocks. For example, the evaluation is performed based on a comparison with companies in the same industry. The proposal department can also analyze stocks in different industries and perform a stock evaluation. For example, it compares stocks in the technology industry and the healthcare industry. The proposal department can also analyze stocks in the financial industry and the consumer goods industry and perform a relative evaluation. For example, it compares the performance of stocks in different industries. This makes it possible to perform a relative evaluation by comparing with stocks in different industries.

[0040] The proposal department can perform simulations of what would happen if a stock were incorporated into an investment portfolio. For example, the proposal department constructs a system in which the generation AI analyzes different investment portfolios and performs simulations of what would happen if the proposed stock were incorporated. For example, it evaluates the balance between risk and return. The proposal department can also analyze stock portfolios and bond portfolios and perform simulations of stocks. For example, it evaluates risk and return based on a combination of stocks and bonds. The proposal department can also analyze mixed portfolios and perform simulations of stocks. For example, it evaluates risk and return based on a combination of different asset classes. This allows simulations of what would happen if the stock were incorporated into different investment portfolios, improving the reliability of the proposals.

[0041] When explaining the reasons, the proposal department can cite past data or examples to provide specific evidence. For example, the proposal department builds a system in which a generative AI analyzes past data and examples to provide specific evidence for the reasons for the proposal. For example, it cites past success stories to explain the basis for the proposal. The proposal department can also analyze past performance data to explain the reasons for the proposal. For example, it can provide the basis for the proposal based on past performance data. The proposal department can also analyze past economic indicators to explain the reasons for the proposal. For example, it can provide the basis for the proposal based on past economic indicators. In this way, by citing past data and examples, the reliability of the proposal is improved.

[0042] When explaining the reasons, the proposal unit can present different scenarios and compare the impact of each scenario. For example, the proposal unit constructs a system in which a generation AI generates different scenarios and compares the impact of each scenario. For example, the proposal unit explains the reasons for the proposal based on multiple scenarios. The proposal unit can also present a baseline scenario or a worst-case scenario and compare the impact. For example, the proposal unit can explain the reasons for the proposal based on the baseline scenario. The proposal unit can also present a best scenario and compare the impact. For example, the proposal unit can explain the reasons for the proposal based on the best scenario. In this way, by presenting different scenarios, the reliability of the proposal is improved.

[0043] The suggestion unit can provide different languages ​​or explanations when explaining the reason, thereby supporting international users. For example, the suggestion unit builds a system in which the generation AI explains the reason for the suggestion in different languages. For example, explanations are provided in multiple languages, such as English, French, and Chinese. The suggestion unit can also support international users by providing explanations in different languages. For example, an explanation is provided in English for an English-speaking user. The suggestion unit can also deepen a user's understanding by providing explanations in different languages. For example, providing an explanation in a user's native language promotes a user's understanding. This allows international users to be supported by providing explanations in different languages.

[0044] The suggestion unit can receive or analyze feedback from users and update the proposal content in real time. For example, the suggestion unit builds a system in which a generation AI analyzes user feedback in real time and updates the proposal content. For example, the proposal content is immediately revised based on user opinions. The suggestion unit can also analyze the content of feedback and improve the proposal content. For example, the proposal content is adjusted based on user comments. The suggestion unit can also analyze survey results and update the proposal content. For example, the proposal content is improved based on survey responses. This makes it possible to analyze user feedback in real time and update the proposal content.

[0045] The proposal unit can learn from the user's investment history and make proposals tailored to the individual investment style. For example, the proposal unit constructs a system in which a generation AI analyzes the user's investment history and makes proposals tailored to the individual investment style. For example, the proposal content is customized based on past investment patterns. The proposal unit can also analyze the user's trading data and make proposals tailored to the investment style. For example, the proposal content is adjusted based on past trading data. The proposal unit can also analyze the user's investment portfolio history and make proposals tailored to the investment style. For example, the proposal content is customized based on the portfolio history. In this way, by learning the user's investment history, it becomes possible to make proposals tailored to the individual investment style.

[0046] The suggestion unit can extract common areas for improvement by integrating feedback from different user groups. For example, the suggestion unit constructs a system in which the generation AI analyzes feedback from different user groups and extracts common areas for improvement. For example, the suggestions are improved by integrating the opinions of multiple users. The suggestion unit can also analyze feedback from different user groups, such as those based on age group, occupation, and investment experience, and extract common areas for improvement. For example, the suggestions are improved based on the opinions of users of different age groups. The suggestion unit can also analyze feedback from users with different occupations and investment experience and extract common areas for improvement. For example, the suggestions are improved based on the opinions of users with different occupations. In this way, common areas for improvement can be extracted by integrating feedback from different user groups.

[0047] The suggestion unit can develop an algorithm for continuously improving the accuracy of the suggestions based on the feedback. For example, the suggestion unit develops an algorithm that analyzes feedback using a generation AI and improves the accuracy of the suggestions based on that data. For example, the suggestion unit weights the feedback and reflects it in the suggestions. The suggestion unit can also use a machine learning algorithm to improve the accuracy of the suggestions based on the feedback. For example, the suggestion unit improves the accuracy of the suggestions by learning from the feedback data. The suggestion unit can also use a statistical model or a heuristic algorithm to improve the accuracy of the suggestions based on the feedback. For example, the suggestion unit analyzes the feedback data and optimizes the suggestions. This develops an algorithm that continuously improves the accuracy of the suggestions based on the feedback, thereby improving the reliability of the suggestions.

[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 analysis unit can analyze investment behavior in different cultural regions and make investment recommendations based on cultural background. For example, since Asian investors tend to be more risk-averse, it can recommend stocks that are expected to produce stable profits. On the other hand, since Western investors tend to be more willing to take risks, it can also recommend stocks that are expected to produce high returns. Furthermore, the analysis unit can take into account the impact of cultural events and holidays on the market and make investment recommendations that are appropriate for specific periods. This makes it possible to make recommendations that take into account investment behavior in different cultural regions.

[0050] The proposal unit can make investment proposals based on the user's health condition and lifestyle. For example, it can propose healthcare-related stocks to a health-conscious user. It can also propose sports goods and outdoor-related stocks to a user who enjoys outdoor activities. It can also propose stocks of eco-friendly companies and companies with sustainable business models based on the user's lifestyle. This makes it possible to propose investments tailored to the user's health condition and lifestyle.

[0051] The information gathering unit can analyze weather data and evaluate the impact of weather on the market. For example, the performance of agricultural stocks can be predicted based on weather data. The performance of energy-related stocks can also be evaluated based on weather data. For example, an increase in energy demand in cold regions can be predicted. The performance of the tourism and leisure industries can also be evaluated based on weather data. For example, an increase in tourism demand can be predicted in regions with continued good weather. In this way, the impact of weather on the market can be evaluated by analyzing weather data.

[0052] The analysis unit can analyze the investment behavior of different age groups and make investment suggestions based on age. For example, it can suggest long-term growth stocks that allow for risk to be taken by young people. It can also suggest stocks that are expected to provide stable returns to middle-aged people. It can also suggest stocks that are expected to provide stable returns with low risk to older people. This makes it possible to make suggestions that take into account the investment behavior of different age groups.

[0053] The information gathering department can collect data on corporate social responsibility (CSR) activities and evaluate the impact of CSR activities on the market. For example, it can evaluate the impact of a company's environmental protection activities on the market. It can also evaluate the impact of a company's social contribution activities on the market. For example, it can evaluate the impact of a company's charitable activities on its brand image. It can also evaluate the impact of a company's activities to improve its working environment on the market. For example, it can evaluate the impact of a company's employee satisfaction on its performance. In this way, by collecting data on a company's CSR activities, it can evaluate the impact on the market.

[0054] The analysis unit can analyze different economic indicators and make investment proposals based on them. For example, it can propose industries where economic growth is expected based on GDP growth rates. It can also evaluate labor market trends based on unemployment rates and make investment proposals. For example, it can propose consumer-related stocks in areas where the unemployment rate is falling. It can also propose industries where price increases are expected based on inflation rates. For example, if the inflation rate is rising, it can propose industries where price increases can be passed on to customers. This makes it possible to make investment proposals based on economic indicators by analyzing different economic indicators.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The information gathering department collects information about upcoming events and current social situations. For example, they gather information about major sporting events like the Olympics and the World Cup, international conferences and economic forums, and the implementation of new laws and regulations. They gather information by scraping online news sites and official announcements. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses generative AI (text generation AI or multimodal generation AI) to analyze information about future events and situations. It can also learn from data on similar past events to predict the impact of future events. It also uses sentiment estimation functions to analyze public sentiment toward future events and consider the impact that sentiment will have on the market. For example, it can analyze social media and blog posts to estimate public sentiment. Step 3: The proposal department suggests industries and stocks suitable for investment based on the results of the analysis by the analysis department. For example, if the Olympics are being held, it may suggest that the tourism and sporting goods industries are attracting attention. If new environmental regulations are coming into effect, it may suggest that renewable energy and environmental technology-related industries are attracting attention. Furthermore, the proposal department may suggest specific stocks related to the proposed industries. For example, if the tourism industry is attracting attention, it may suggest stocks of tourism-related companies.

[0057] (Example 2) The investment proposal system according to an embodiment of the present invention analyzes future events and world situations, and proposes the industries that should be purchased at present, the process that led to the prediction, and multiple related stocks. This allows the investment proposal system to formulate an investment strategy based on future events and situations.

[0058] An investment proposal system according to an embodiment includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects information about future events and world situations. For example, the information collection unit collects information about large-scale sporting events such as the Olympics and the World Cup. The information collection unit can also collect information about international conferences and economic forums. The information collection unit can also collect information about the implementation of new laws and regulations. For example, the information collection unit collects information by scraping online news sites and official announcements. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to analyze information about future events and situations. The analysis unit can also learn data from similar past events to predict the impact of future events. The analysis unit can also use an emotion estimation function to analyze public sentiment toward future events and consider the impact of those sentiments on the market. For example, the analysis unit analyzes posts on social media and blogs to estimate public sentiment. The suggestion unit suggests industries and stocks suitable for investment based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests that the tourism industry and the sporting goods industry will be in the spotlight if the Olympics are held. The suggestion unit may also suggest that renewable energy and environmental technology-related industries will be in the spotlight if new environmental regulations are enacted. The suggestion unit may also suggest specific stocks related to the proposed industries. For example, if the tourism industry is in the spotlight, the suggestion unit may suggest stocks of tourism-related companies. In this way, the investment suggestion system according to the embodiment can suggest industries and stocks suitable for investment based on future events and situations. For example, by purchasing tourism-related stocks before the Olympics, a user can invest in anticipation of increased demand due to the event. Furthermore, by purchasing renewable energy-related stocks before new environmental regulations are enacted, a user can invest in anticipation of market expansion due to the regulations.

[0059] The information collection unit can also collect information from at least one unofficial information source, such as a social networking site or a blog. The information collection unit, for example, builds a system that collects and analyzes information in real time from social networking sites or blogs. For example, it analyzes posts on Twitter or Facebook to understand trends related to future events. The information collection unit can also analyze blog articles to collect information related to future events or situations. For example, it analyzes the content of blog articles and extracts important information. By collecting information from unofficial information sources, a wider variety of data can be used for analysis.

[0060] The analysis unit can learn data from similar past events and predict the impact of future events. For example, the analysis unit can have a generative AI learn data from similar past events and build a system that predicts the impact of future events. For example, it can make predictions based on economic data from past Olympic Games. The analysis unit can also learn data from past economic crises and election results and predict the impact of future events. For example, it can predict the impact of future economic events based on data from past economic crises. In this way, by learning data from similar past events, it can more accurately predict the impact of future events.

[0061] The analysis unit can use the emotion estimation function to analyze the emotions of the general public regarding future events and consider the impact of those emotions on the market. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of the general public from posts on social media or blogs. For example, it predicts that an event with a high level of positive emotions will have a positive impact on the market. The analysis unit can also use the emotion estimation function to analyze the results of a survey and estimate the emotions of the general public. For example, it analyzes the content of the survey responses and calculates an emotion score. The analysis unit can also use the emotion estimation function to analyze the content of news articles and estimate the emotions of the general public. For example, it analyzes the tone of the news article and calculates an emotion score. In this way, by analyzing the emotions of the general public, it becomes possible to make predictions that take into account the impact on the market.

[0062] The information collection unit can analyze geographical data and evaluate the impact of events or situations in each region. For example, the information collection unit constructs a system in which a generative AI analyzes geographical data and evaluates the impact of events or situations in each region. For example, it predicts the impact of an event in a specific region on the economy of that region. The information collection unit can also analyze GPS data and evaluate the impact of events in each region. For example, it predicts the impact of an event in a specific region on the tourism industry. The information collection unit can also analyze regional statistical data and evaluate the impact of situations in each region. For example, it analyzes economic indicators in a specific region and evaluates the economic situation in that region. In this way, the impact on each region can be evaluated by analyzing geographical data.

[0063] The analysis unit can incorporate the opinions of experts from different industries and perform analysis from a more multifaceted perspective. For example, the analysis unit builds a system in which the generative AI collects the opinions of experts from different industries and performs analysis based on that data. For example, the analysis unit incorporates the opinions of economists and technical experts. The analysis unit can also collect the opinions of marketing experts and reflect them in the analysis. For example, based on the opinions of marketing experts, it can evaluate the impact of future events on the market. The analysis unit can also collect the opinions of legal experts and reflect them in the analysis. For example, based on the opinions of legal experts, it can evaluate the impact of new regulations on the market. This makes it possible to incorporate the opinions of experts from different industries and perform a more multifaceted analysis.

[0064] The analysis unit can use the emotion estimation function to analyze a company's emotions toward future events and consider the impact of those emotions on business performance. For example, the analysis unit uses the emotion estimation function to build a system that analyzes emotions from a company's press releases and official announcements. For example, it evaluates the impact of a company's positive emotions on business performance. The analysis unit can also use the emotion estimation function to analyze emotions from company interviews and social media posts. For example, it evaluates emotions toward a company's leadership and strategy. The analysis unit can also use the emotion estimation function to analyze the emotions of a company's employees and evaluate the impact of those emotions on business performance. For example, it evaluates the impact of employee motivation and satisfaction on business performance. In this way, analyzing a company's emotions makes it possible to make predictions that take into account the impact on business performance.

[0065] The proposal department can perform risk assessment for industries based on past investment performance data. For example, the proposal department constructs a system in which a generation AI analyzes past investment performance data and performs risk assessment for the proposed industry. For example, it calculates a risk score based on past data. The proposal department can also analyze past stock price data and perform risk assessment for industries. For example, it can assess risk based on stock price fluctuations. The proposal department can also analyze past financial reports and perform risk assessment for industries. For example, it can assess risk based on financial indicators. In this way, risk assessment is performed based on past investment performance data, improving the reliability of the proposal.

[0066] The proposal department can evaluate the future growth potential of an industry based on technological innovation or market trends. For example, the proposal department uses generative AI to analyze relevant technological innovations and market trends and build a system to evaluate the future growth potential of the proposed industry. For example, it evaluates the impact of the introduction of new technology on an industry. The proposal department can also analyze patent acquisition and progress in research and development to evaluate the growth potential of an industry. For example, it evaluates the impact of acquiring a new patent on an industry. The proposal department can also analyze changes in consumer behavior and the trends of competitors to evaluate the growth potential of an industry. For example, it can propose industries where consumer demand is increasing. This makes it possible to evaluate future growth potential by taking technological innovation and market trends into account.

[0067] The proposal unit can use the emotion estimation function to analyze investor sentiment toward industries and consider the impact of those sentiments on investment decisions. The proposal unit, for example, uses the emotion estimation function to analyze investor sentiment and build a system that evaluates proposed industries based on that data. For example, it prioritizes proposing industries with strong positive sentiment. The proposal unit can also use the emotion estimation function to analyze investor sentiment from comments on investment forums and posts on social media. For example, it evaluates industries based on investor opinions. The proposal unit can also use the emotion estimation function to analyze survey results and evaluate investor sentiment. For example, it can analyze the content of survey responses and calculate an emotion score. By analyzing investor sentiment, it becomes possible to make proposals that take into account the impact on investment decisions.

[0068] The proposal unit can consider investment strategies for an industry and propose an optimal strategy based on at least one of short-term investment or long-term investment. The proposal unit, for example, builds a system in which the generation AI analyzes different investment strategies and proposes the optimal strategy for the proposed industry. For example, it evaluates the risks and returns of short-term investment and long-term investment. The proposal unit can also evaluate the risks and returns of short-term investment and propose an industry. For example, it evaluates industries based on short-term market fluctuations. The proposal unit can also evaluate the risks and returns of long-term investment and propose an industry. For example, it evaluates industries based on long-term growth potential. This makes it possible to propose an optimal strategy by considering different investment strategies.

[0069] The proposal unit can use the emotion estimation function to analyze consumer emotions toward industries and consider the impact of those emotions on the market. The proposal unit, for example, uses the emotion estimation function to analyze consumer emotions and build a system that evaluates proposed industries based on that data. For example, it prioritizes proposing industries with strong positive emotions. The proposal unit can also use the emotion estimation function to analyze consumer emotions from social media posts and consumer reviews. For example, it evaluates industries based on consumer opinions. The proposal unit can also use the emotion estimation function to analyze survey results and evaluate consumer emotions. For example, it analyzes the content of survey responses and calculates an emotion score. This makes it possible to analyze consumer emotions and make proposals that take into account the impact on the market.

[0070] The proposal department can perform a detailed evaluation of stocks based on a company's financial data or performance forecast. For example, the proposal department builds a system in which a generation AI analyzes a company's financial data and performs a detailed evaluation of the proposed stocks. For example, the evaluation is performed based on profitability and debt ratio. The proposal department can also analyze a company's performance forecast and evaluate stocks. For example, the evaluation is performed based on past performance data and economic indicators. The proposal department can also analyze analyst forecasts and evaluate stocks. For example, the evaluation is performed based on analyst reports. In this way, detailed evaluations based on a company's financial data and performance forecasts improve the reliability of proposals.

[0071] The proposal department can evaluate a company's management team or strategy for each stock and take management quality into consideration. For example, the proposal department will build a system in which generative AI analyzes data on a company's management team and evaluates the proposed stocks. For example, the evaluation will be based on the management team's past performance and leadership. The proposal department can also analyze a company's strategy and evaluate stocks. For example, the evaluation will be based on growth strategy and marketing strategy. The proposal department can also analyze a company's technology strategy and evaluate stocks. For example, the impact the introduction of new technology will have on the company. This makes it possible to make proposals that take management quality into consideration by evaluating a company's management team and strategy.

[0072] The proposal unit can use the emotion estimation function to analyze market sentiment toward a stock and consider the impact that sentiment has on the stock price. The proposal unit, for example, uses the emotion estimation function to analyze market sentiment and build a system that evaluates proposed stocks based on that data. For example, it prioritizes the proposal of stocks with strong positive sentiment. The proposal unit can also use the emotion estimation function to analyze market sentiment from social media posts and news articles. For example, it evaluates stocks based on market opinions. The proposal unit can also use the emotion estimation function to analyze comments on investment forums and evaluate market sentiment. For example, it evaluates stocks based on investor opinions. In this way, by analyzing market sentiment, it becomes possible to make proposals that take into account the impact on stock prices.

[0073] The proposal department can compare stocks with stocks in different industries and perform a relative evaluation. For example, the proposal department builds a system in which the generation AI analyzes stocks in different industries and performs a relative evaluation of the proposed stocks. For example, the evaluation is performed based on a comparison with companies in the same industry. The proposal department can also analyze stocks in different industries and perform a stock evaluation. For example, it compares stocks in the technology industry and the healthcare industry. The proposal department can also analyze stocks in the financial industry and the consumer goods industry and perform a relative evaluation. For example, it compares the performance of stocks in different industries. This makes it possible to perform a relative evaluation by comparing with stocks in different industries.

[0074] The proposal department can perform simulations of what would happen if a stock were incorporated into an investment portfolio. For example, the proposal department constructs a system in which the generation AI analyzes different investment portfolios and performs simulations of what would happen if the proposed stock were incorporated. For example, it evaluates the balance between risk and return. The proposal department can also analyze stock portfolios and bond portfolios and perform simulations of stocks. For example, it evaluates risk and return based on a combination of stocks and bonds. The proposal department can also analyze mixed portfolios and perform simulations of stocks. For example, it evaluates risk and return based on a combination of different asset classes. This allows simulations of what would happen if the stock were incorporated into different investment portfolios, improving the reliability of the proposals.

[0075] The proposal unit can use the emotion estimation function to analyze employee emotions toward a stock and consider the impact of those emotions on the company's performance. The proposal unit, for example, uses the emotion estimation function to analyze employee emotions and build a system that evaluates proposed stocks based on that data. For example, the proposal unit can prioritize companies where employees have strong positive emotions. The proposal unit can also use the emotion estimation function to analyze emotions from internal surveys and employee feedback. For example, the proposal unit can evaluate companies based on employee satisfaction and motivation. The proposal unit can also use the emotion estimation function to analyze employee emotions from social media posts and evaluate companies. For example, the proposal unit can evaluate companies based on employee opinions. In this way, analyzing employee emotions makes it possible to make proposals that take into account the impact on company performance.

[0076] When explaining the reasons, the proposal department can cite past data or examples to provide specific evidence. For example, the proposal department builds a system in which a generative AI analyzes past data and examples to provide specific evidence for the reasons for the proposal. For example, it cites past success stories to explain the basis for the proposal. The proposal department can also analyze past performance data to explain the reasons for the proposal. For example, it can provide the basis for the proposal based on past performance data. The proposal department can also analyze past economic indicators to explain the reasons for the proposal. For example, it can provide the basis for the proposal based on past economic indicators. In this way, by citing past data and examples, the reliability of the proposal is improved.

[0077] When explaining the reasons, the proposal unit can present different scenarios and compare the impact of each scenario. For example, the proposal unit constructs a system in which a generation AI generates different scenarios and compares the impact of each scenario. For example, the proposal unit explains the reasons for the proposal based on multiple scenarios. The proposal unit can also present a baseline scenario or a worst-case scenario and compare the impact. For example, the proposal unit can explain the reasons for the proposal based on the baseline scenario. The proposal unit can also present a best scenario and compare the impact. For example, the proposal unit can explain the reasons for the proposal based on the best scenario. In this way, by presenting different scenarios, the reliability of the proposal is improved.

[0078] The suggestion unit can use the emotion estimation function to analyze the user's emotion regarding the reason and adjust the explanation method based on that emotion. For example, the suggestion unit can use the emotion estimation function to analyze the user's emotion and build a system that explains the reason for the suggestion based on that data. For example, the suggestion unit can adjust the tone of the explanation according to the user's emotion. The suggestion unit can also use the emotion estimation function to analyze the user's emotion from feedback or social media posts. For example, the suggestion unit can adjust the explanation method based on the user's opinion. The suggestion unit can also use the emotion estimation function to analyze the results of a questionnaire survey and evaluate the user's emotion. For example, the suggestion unit can analyze the content of the questionnaire responses and calculate an emotion score. In this way, the explanation method can be adjusted by analyzing the user's emotion.

[0079] The suggestion unit can provide different languages ​​or explanations when explaining the reason, thereby supporting international users. For example, the suggestion unit builds a system in which the generation AI explains the reason for the suggestion in different languages. For example, explanations are provided in multiple languages, such as English, French, and Chinese. The suggestion unit can also support international users by providing explanations in different languages. For example, an explanation is provided in English for an English-speaking user. The suggestion unit can also deepen a user's understanding by providing explanations in different languages. For example, providing an explanation in a user's native language promotes a user's understanding. This allows international users to be supported by providing explanations in different languages.

[0080] The proposal unit can use the emotion estimation function to analyze market emotions regarding the reasons and adjust the content of the explanation based on those emotions. For example, the proposal unit can use the emotion estimation function to analyze market emotions and build a system that explains the reasons for the proposal based on that data. For example, the tone of the explanation can be adjusted according to market emotions. The proposal unit can also use the emotion estimation function to analyze market emotions from social media posts and news articles. For example, the content of the explanation can be adjusted based on market opinions. The proposal unit can also use the emotion estimation function to analyze comments on investment forums and evaluate market emotions. For example, the content of the explanation can be adjusted based on investor opinions. In this way, the content of the explanation can be adjusted by analyzing market emotions.

[0081] The suggestion unit can receive or analyze feedback from users and update the proposal content in real time. For example, the suggestion unit builds a system in which a generation AI analyzes user feedback in real time and updates the proposal content. For example, the proposal content is immediately revised based on user opinions. The suggestion unit can also analyze the content of feedback and improve the proposal content. For example, the proposal content is adjusted based on user comments. The suggestion unit can also analyze survey results and update the proposal content. For example, the proposal content is improved based on survey responses. This makes it possible to analyze user feedback in real time and update the proposal content.

[0082] The proposal unit can learn from the user's investment history and make proposals tailored to the individual investment style. For example, the proposal unit constructs a system in which a generation AI analyzes the user's investment history and makes proposals tailored to the individual investment style. For example, the proposal content is customized based on past investment patterns. The proposal unit can also analyze the user's trading data and make proposals tailored to the investment style. For example, the proposal content is adjusted based on past trading data. The proposal unit can also analyze the user's investment portfolio history and make proposals tailored to the investment style. For example, the proposal content is customized based on the portfolio history. In this way, by learning the user's investment history, it becomes possible to make proposals tailored to the individual investment style.

[0083] The suggestion unit can use the emotion estimation function to analyze the user's emotion toward the feedback and adjust the content of the suggestion based on that emotion. For example, the suggestion unit can use the emotion estimation function to analyze the user's emotion toward the feedback and build a system that adjusts the content of the suggestion based on that data. For example, the suggestion unit can prioritize suggestions that have a strong positive emotion. The suggestion unit can also use the emotion estimation function to analyze the user's emotion from feedback or posts on social media. For example, the suggestion unit can adjust the content of the suggestion based on the user's opinion. The suggestion unit can also use the emotion estimation function to analyze the results of a questionnaire survey and evaluate the user's emotion. For example, the suggestion unit can analyze the content of the questionnaire and calculate an emotion score. In this way, the suggestion unit can adjust the content of the suggestion by analyzing the user's emotion.

[0084] The suggestion unit can extract common areas for improvement by integrating feedback from different user groups. For example, the suggestion unit constructs a system in which the generation AI analyzes feedback from different user groups and extracts common areas for improvement. For example, the suggestions are improved by integrating the opinions of multiple users. The suggestion unit can also analyze feedback from different user groups, such as those based on age group, occupation, and investment experience, and extract common areas for improvement. For example, the suggestions are improved based on the opinions of users of different age groups. The suggestion unit can also analyze feedback from users with different occupations and investment experience and extract common areas for improvement. For example, the suggestions are improved based on the opinions of users with different occupations. In this way, common areas for improvement can be extracted by integrating feedback from different user groups.

[0085] The suggestion unit can develop an algorithm for continuously improving the accuracy of the suggestions based on the feedback. For example, the suggestion unit develops an algorithm that analyzes feedback using a generation AI and improves the accuracy of the suggestions based on that data. For example, the suggestion unit weights the feedback and reflects it in the suggestions. The suggestion unit can also use a machine learning algorithm to improve the accuracy of the suggestions based on the feedback. For example, the suggestion unit improves the accuracy of the suggestions by learning from the feedback data. The suggestion unit can also use a statistical model or a heuristic algorithm to improve the accuracy of the suggestions based on the feedback. For example, the suggestion unit analyzes the feedback data and optimizes the suggestions. This develops an algorithm that continuously improves the accuracy of the suggestions based on the feedback, thereby improving the reliability of the suggestions.

[0086] The proposal unit can use the emotion estimation function to analyze the overall market sentiment regarding the feedback and adjust the proposal content based on that sentiment. For example, the proposal unit uses the emotion estimation function to analyze the overall market sentiment and build a system that adjusts the proposal content based on that data. For example, it prioritizes proposals that have strong positive market sentiment. The proposal unit can also use the emotion estimation function to analyze the overall market sentiment from social media posts and news articles. For example, it adjusts the proposal content based on market opinion. The proposal unit can also use the emotion estimation function to analyze comments on investment forums and evaluate the overall market sentiment. For example, it adjusts the proposal content based on investor opinion. In this way, the proposal content can be adjusted by analyzing the overall market sentiment.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The analysis unit can analyze investment behavior in different cultural regions and make investment recommendations based on cultural background. For example, since Asian investors tend to be more risk-averse, it can recommend stocks that are expected to produce stable profits. On the other hand, since Western investors tend to be more willing to take risks, it can also recommend stocks that are expected to produce high returns. Furthermore, the analysis unit can take into account the impact of cultural events and holidays on the market and make investment recommendations that are appropriate for specific periods. This makes it possible to make recommendations that take into account investment behavior in different cultural regions.

[0089] The proposal unit can make investment proposals based on the user's health condition and lifestyle. For example, it can propose healthcare-related stocks to a health-conscious user. It can also propose sports goods and outdoor-related stocks to a user who enjoys outdoor activities. It can also propose stocks of eco-friendly companies and companies with sustainable business models based on the user's lifestyle. This makes it possible to propose investments tailored to the user's health condition and lifestyle.

[0090] The analysis unit can use the emotion estimation function to analyze the user's stress level and adjust investment suggestions based on that stress level. For example, a user with a high stress level can be recommended stable stocks with low risk. Conversely, a user with a low stress level can be recommended stocks with high risk but high returns. Furthermore, the analysis unit can adjust the length of the investment period based on the user's stress level. This makes it possible to make investment suggestions that take the user's stress level into consideration.

[0091] The information gathering unit can analyze weather data and evaluate the impact of weather on the market. For example, the performance of agricultural stocks can be predicted based on weather data. The performance of energy-related stocks can also be evaluated based on weather data. For example, an increase in energy demand in cold regions can be predicted. The performance of the tourism and leisure industries can also be evaluated based on weather data. For example, an increase in tourism demand can be predicted in regions with continued good weather. In this way, the impact of weather on the market can be evaluated by analyzing weather data.

[0092] The suggestion unit can use the emotion estimation function to evaluate the user's investment risk tolerance based on their emotions and make investment suggestions based on that tolerance. For example, low-risk stocks can be suggested to a user who is strongly risk-averse. Also, high-risk, high-return stocks can be suggested to a user who is strongly risk-positive. Furthermore, the length of the investment period can be adjusted based on the user's emotions. This makes it possible to make suggestions based on the user's investment risk tolerance, taking into account their emotions.

[0093] The analysis unit can analyze the investment behavior of different age groups and make investment suggestions based on age. For example, it can suggest long-term growth stocks that allow for risk to be taken by young people. It can also suggest stocks that are expected to provide stable returns to middle-aged people. It can also suggest stocks that are expected to provide stable returns with low risk to older people. This makes it possible to make suggestions that take into account the investment behavior of different age groups.

[0094] The suggestion unit can use the emotion estimation function to suggest the timing of investment based on the user's emotions. For example, it can suggest aggressive investment timing to a user with strong positive emotions. It can also suggest cautious investment timing to a user with strong negative emotions. Furthermore, it can adjust the investment timing based on the user's emotions. This makes it possible to suggest investment timing that takes the user's emotions into consideration.

[0095] The information gathering department can collect data on corporate social responsibility (CSR) activities and evaluate the impact of CSR activities on the market. For example, it can evaluate the impact of a company's environmental protection activities on the market. It can also evaluate the impact of a company's social contribution activities on the market. For example, it can evaluate the impact of a company's charitable activities on its brand image. It can also evaluate the impact of a company's activities to improve its working environment on the market. For example, it can evaluate the impact of a company's employee satisfaction on its performance. In this way, by collecting data on a company's CSR activities, it can evaluate the impact on the market.

[0096] The suggestion unit can use the emotion estimation function to suggest an investment diversification strategy based on the user's emotions. For example, it can suggest diversified investments to a user who is strongly risk-averse. It can also suggest concentrated investments to a user who is strongly risk-positive. Furthermore, it can adjust the investment diversification strategy based on the user's emotions. This makes it possible to suggest an investment diversification strategy that takes the user's emotions into consideration.

[0097] The analysis unit can analyze different economic indicators and make investment proposals based on them. For example, it can propose industries where economic growth is expected based on GDP growth rates. It can also evaluate labor market trends based on unemployment rates and make investment proposals. For example, it can propose consumer-related stocks in areas where the unemployment rate is falling. It can also propose industries where price increases are expected based on inflation rates. For example, if the inflation rate is rising, it can propose industries where price increases can be passed on to customers. This makes it possible to make investment proposals based on economic indicators by analyzing different economic indicators.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The information gathering department collects information about upcoming events and current social situations. For example, they gather information about major sporting events like the Olympics and the World Cup, international conferences and economic forums, and the implementation of new laws and regulations. They gather information by scraping online news sites and official announcements. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses generative AI (text generation AI or multimodal generation AI) to analyze information about future events and situations. It can also learn from data on similar past events to predict the impact of future events. It also uses sentiment estimation functions to analyze public sentiment toward future events and consider the impact that sentiment will have on the market. For example, it can analyze social media and blog posts to estimate public sentiment. Step 3: The proposal department suggests industries and stocks suitable for investment based on the results of the analysis by the analysis department. For example, if the Olympics are being held, it may suggest that the tourism and sporting goods industries are attracting attention. If new environmental regulations are coming into effect, it may suggest that renewable energy and environmental technology-related industries are attracting attention. Furthermore, the proposal department may suggest specific stocks related to the proposed industries. For example, if the tourism industry is attracting attention, it may suggest stocks of tourism-related companies.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. The Information Gathering Department collects information about future events and world affairs, an analysis unit that analyzes the information collected by the information collection unit; a proposal unit that proposes industries and brands suitable for investment based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Learn from data on similar past events and predict the impact of said future events 2. The system of claim 1.

3. The information collecting unit Analyzing geographic data to assess the impact of events or developments by region 2. The system of claim 1.

4. The proposal unit Conduct risk assessment for the above industries based on past investment performance data 2. The system of claim 1.

5. The proposal unit A detailed evaluation of the stock is conducted based on the company's financial data or performance forecast.

2. The system of claim 1.

6. The proposal unit Analyze the user's feelings about the reason and adjust the explanation method based on those feelings 2. The system of claim 1.

7. The proposal unit Analyze user sentiment regarding feedback and adjust suggestions based on that sentiment 2. The system of claim 1.

8. The analysis unit Based on the analysis of public sentiment towards the future event and the impact of that sentiment on the market, 2. The system of claim 1.

Images