System

Abstract

An object of a system according to an embodiment is to analyze settlement information of a listed company, select a company expected to grow, and provide beneficial information to an investor.SOLUTION: A system includes a settlement information acquisition part, a company selection part, a profit rate comparison part, and an information provision part. The settlement information acquisition part acquires settlement information of the listed company. The company selection part analyzes the settlement information acquired by the settlement information acquisition part and selects a company expected to grow. The profit rate comparison unit compares the average profit rate of the business category of the company selected by the company selection unit with the current stock price. An information providing part provides the information obtained by the profit rate comparing part to the stock purchaser.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] With conventional technology, the process of predicting corporate growth based on financial information from listed companies and finding undervalued stocks was complicated, posing a significant burden for investors.

[0005] The system according to the embodiment aims to analyze financial information of listed companies, select companies that are expected to grow, and provide useful information to investors. [Means for solving the problem]

[0006] The system according to the embodiment includes a financial information acquisition unit, a company selection unit, a profit margin comparison unit, and an information provision unit. The financial information acquisition unit acquires financial information of listed companies. The company selection unit analyzes the financial information acquired by the financial information acquisition unit and selects companies that are expected to grow. The profit margin comparison unit compares the average profit margin of the industry of the companies selected by the company selection unit with the current stock price. The information provision unit provides the information obtained by the profit margin comparison unit to stock purchasers. [Effects of the Invention]

[0007] The system according to the embodiment can analyze financial information of listed companies, select companies that are expected to grow, and provide useful information to investors. [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 company analysis system according to the embodiment of the present invention is a system that analyzes the financial statements of listed companies, selects companies that are expected to grow, and provides this information to stock buyers. As a result, the company analysis system can provide investors with information on company growth prospects and undervalued stocks.

[0029] The company analysis system according to the embodiment includes a financial information acquisition unit, a company selection unit, a profit margin comparison unit, and an information provision unit. The financial information acquisition unit acquires financial information of listed companies. For example, the financial information acquisition unit acquires financial statements and cash flow statements of companies. The financial information acquisition unit can also collect publicly disclosed information about companies. For example, the financial information acquisition unit acquires financial information from the companies' official websites. The company selection unit analyzes the financial information acquired by the financial information acquisition unit and selects companies expected to grow. For example, the company selection unit evaluates past performance trends and future growth strategies. The company selection unit can also analyze industry trends and identify companies expected to grow. For example, the company selection unit evaluates the growth potential of companies based on market forecast data for the industry. The profit margin comparison unit compares the average profit margin of the industry of the companies selected by the company selection unit with the current stock price. For example, the profit margin comparison unit acquires the average profit margin for each industry from a database and compares it with the profit margin of the selected company. The profit margin comparison unit can also acquire the current stock price and evaluate the balance with the profit margin. For example, the profit margin comparison unit obtains the current stock price from a stock price database and evaluates the balance with the profit margin. The information provision unit provides the information obtained by the profit margin comparison unit to stock purchasers. For example, the information provision unit creates a report including indicators of a company's growth prospects and undervalued stocks. The information provision unit can also link with a securities company's system to provide information to stock purchasers. For example, the information provision unit provides the report through the securities company's website. This allows the company analysis system according to the embodiment to provide investors with information on a company's growth prospects and undervalued stocks. For example, investors can maximize their returns by investing in companies with growth prospects. Furthermore, they can make investment decisions at the appropriate time based on the information on undervalued stocks.

[0030] The financial information acquisition unit can simultaneously analyze a company's past press releases and news articles, taking into account the company's external environment and market reactions. For example, when the generation AI analyzes a company's financial information, the financial information acquisition unit simultaneously reads past press releases and news articles and analyzes the company's external environment and market reactions. For example, it can analyze a company's new product announcements and market reactions and reflect them in the financial information. This allows for more accurate corporate growth predictions by taking into account a company's external environment and market reactions.

[0031] The financial information acquisition unit can also incorporate information on a company's supply chain and business partner information, enabling more comprehensive analysis. For example, when the generation AI analyzes a company's financial information, the financial information acquisition unit incorporates the company's supply chain information to evaluate the health and risk of the supply chain. For example, it can analyze the financial status and supply risks of major business partners and reflect this in the financial information. In this way, by incorporating supply chain information and business partner information, a company's risk profile can be more accurately evaluated.

[0032] The company selection department can analyze a company's R&D investment and new product development status to evaluate its potential for technological innovation. For example, the company selection department uses a generative AI to analyze a company's R&D investment and evaluate its potential for technological innovation. For example, it analyzes trends in R&D expenses and the development status of new products to evaluate a company's growth potential. In this way, by analyzing R&D investment and the development status of new products, it is possible to evaluate a company's potential for technological innovation.

[0033] The company selection department can analyze a company's international expansion status and performance in overseas markets to evaluate its global growth potential. For example, the company selection department uses a generation AI to analyze a company's international expansion status and evaluate its global growth potential. For example, it analyzes sales and market share in overseas markets to evaluate a company's growth potential. This makes it possible to evaluate a company's global growth potential by analyzing its international expansion status and performance in overseas markets.

[0034] The profit margin comparison unit can analyze a company's past stock price trends and market volatility to perform risk assessment. For example, the generation AI analyzes a company's past stock price trends and performs risk assessment. For example, it analyzes past stock price fluctuations and market volatility to evaluate a company's risk profile. This makes it possible to evaluate a company's risk profile by analyzing past stock price trends and market volatility.

[0035] The profit margin comparison unit can analyze a company's financial soundness and debt ratio to evaluate financial risk. For example, the generation AI analyzes a company's financial soundness and evaluates financial risk. For example, it analyzes a company's debt ratio and current ratio to evaluate financial risk. This makes it possible to evaluate a company's financial risk by analyzing financial soundness and debt ratio.

[0036] The information provision unit can generate detailed reports including future forecasts and scenario analyses for the company. For example, the information provision unit generates detailed reports based on future forecasts for the company using the generation AI. For example, it creates reports including growth forecasts and risk scenarios for the company. In this way, by generating detailed reports including future forecasts and scenario analyses, more specific information can be provided to investors.

[0037] The information provision unit can generate a comparative report that includes a company's competitive analysis and market position. For example, the information provision unit generates a comparative report based on a company's competitive analysis using a generation AI. For example, it creates a report that includes a performance comparison with competitors and market position. In this way, by generating a comparative report that includes a competitive analysis and market position, it is possible to provide investors with information that helps them understand the competitive environment.

[0038] The information provider can provide information including a company's ESG score and sustainability report. For example, the information provider uses the generation AI to provide analytical information based on a company's ESG score. For example, the information provider evaluates a company's environmental measures and social contribution activities and creates a report including an ESG score. This allows the provision of ESG scores and sustainability reports to provide investors with information about a company's sustainability.

[0039] The information provision department can provide technical reports that include information on a company's technological innovation and patents. For example, the information provision department uses a generation AI to provide technical reports based on the company's technological innovation. For example, the information provision department evaluates the state of a company's technological innovation and patent application information and creates a technical report. By providing technical reports that include information on technological innovation and patents, it is possible to provide investors with information on a company's technological growth potential.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The Company Selection Department can analyze corporate social responsibility (CSR) activities and environmental protection activities and take into account a company's social reputation. For example, the Company Selection Department can collect data on a company's CSR reports and environmental protection activities and analyze the company's social reputation. The Company Selection Department can also suggest socially responsible investment destinations to investors based on the company's social reputation. In this way, by taking into account a company's social reputation, more comprehensive investment information can be provided to investors.

[0042] The financial information acquisition unit can analyze data related to a company's employee satisfaction and working environment to evaluate the company's internal environment. For example, the financial information acquisition unit collects reports on employee satisfaction surveys and working environments to analyze the company's internal environment. The financial information acquisition unit can also evaluate the impact of employee satisfaction and working environments on the company's growth. This allows for more accurate corporate growth predictions by taking the company's internal environment into consideration.

[0043] The Profit Margin Comparison Department can analyze a company's shareholder structure and the trends of major shareholders to evaluate the influence of shareholders. For example, the Profit Margin Comparison Department collects data on a company's shareholder structure and the trading trends of major shareholders to analyze the influence of shareholders. The Profit Margin Comparison Department can also evaluate the impact that the trends of major shareholders have on a company's stock price. This allows for a more accurate evaluation of a company's risk profile by taking into account the influence of shareholders.

[0044] The Company Selection Department can analyze a company's brand value and market reputation to evaluate its brand power. For example, the Company Selection Department can collect a company's brand value evaluation report and market reputation data to analyze its brand power. The Company Selection Department can also evaluate the impact that brand power has on the company's growth. This allows for a more accurate evaluation of a company's growth potential by taking brand power into consideration.

[0045] The profitability comparison unit can analyze a company's capital structure and financing strategy to evaluate the soundness of its financial strategy. For example, the profitability comparison unit collects information on a company's capital structure data and financing strategy to analyze the soundness of its financial strategy. The profitability comparison unit can also evaluate the impact of financing strategies on the company's growth. This allows for a more accurate evaluation of a company's risk profile by taking into account the soundness of its financial strategy.

[0046] The information department can analyze a company's performance and market share by region and evaluate the growth potential of each region. For example, the information department can collect sales data and market share by region of a company and analyze the growth potential of each region. The information department can also evaluate market trends and the competitive environment of each region. This allows the department to provide investors with more specific investment information by taking into account the growth potential of each region.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The financial information acquisition unit acquires financial information of listed companies. For example, the financial information acquisition unit acquires financial statements and cash flow statements of companies and collects financial information from the companies' official websites. Step 2: The Company Selection Department analyzes the financial information acquired by the Financial Information Acquisition Department and selects companies that are expected to grow. For example, the Company Selection Department evaluates past performance trends and future growth strategies, and analyzes industry trends to identify companies that are expected to grow. Step 3: The profit margin comparison unit compares the average profit margin for the industry of the companies selected by the company selection unit with the current stock price. For example, the profit margin comparison unit retrieves the average profit margin for each industry from the database, compares it with the profit margin of the selected company, retrieves the current stock price, and evaluates the balance with the profit margin. Step 4: The information providing section provides the information obtained by the profitability comparison section to the stock purchaser. For example, the information providing section creates a report including indicators of the company's growth prospects and undervalued stocks, and provides the information to the stock purchaser in cooperation with the securities company's system.

[0049] (Example 2) The company analysis system according to the embodiment of the present invention is a system that analyzes the financial statements of listed companies, selects companies that are expected to grow, and provides this information to stock buyers. As a result, the company analysis system can provide investors with information on company growth prospects and undervalued stocks.

[0050] The company analysis system according to the embodiment includes a financial information acquisition unit, a company selection unit, a profit margin comparison unit, and an information provision unit. The financial information acquisition unit acquires financial information of listed companies. For example, the financial information acquisition unit acquires financial statements and cash flow statements of companies. The financial information acquisition unit can also collect publicly disclosed information about companies. For example, the financial information acquisition unit acquires financial information from the companies' official websites. The company selection unit analyzes the financial information acquired by the financial information acquisition unit and selects companies expected to grow. For example, the company selection unit evaluates past performance trends and future growth strategies. The company selection unit can also analyze industry trends and identify companies expected to grow. For example, the company selection unit evaluates the growth potential of companies based on market forecast data for the industry. The profit margin comparison unit compares the average profit margin of the industry of the companies selected by the company selection unit with the current stock price. For example, the profit margin comparison unit acquires the average profit margin for each industry from a database and compares it with the profit margin of the selected company. The profit margin comparison unit can also acquire the current stock price and evaluate the balance with the profit margin. For example, the profit margin comparison unit obtains the current stock price from a stock price database and evaluates the balance with the profit margin. The information provision unit provides the information obtained by the profit margin comparison unit to stock purchasers. For example, the information provision unit creates a report including indicators of a company's growth prospects and undervalued stocks. The information provision unit can also link with a securities company's system to provide information to stock purchasers. For example, the information provision unit provides the report through the securities company's website. This allows the company analysis system according to the embodiment to provide investors with information on a company's growth prospects and undervalued stocks. For example, investors can maximize their returns by investing in companies with growth prospects. Furthermore, they can make investment decisions at the appropriate time based on the information on undervalued stocks.

[0051] The financial information acquisition unit can simultaneously analyze a company's past press releases and news articles, taking into account the company's external environment and market reactions. For example, when the generation AI analyzes a company's financial information, the financial information acquisition unit simultaneously reads past press releases and news articles and analyzes the company's external environment and market reactions. For example, it can analyze a company's new product announcements and market reactions and reflect them in the financial information. This allows for more accurate corporate growth predictions by taking into account a company's external environment and market reactions.

[0052] The financial information acquisition unit can also incorporate information on a company's supply chain and business partner information, enabling more comprehensive analysis. For example, when the generation AI analyzes a company's financial information, the financial information acquisition unit incorporates the company's supply chain information to evaluate the health and risk of the supply chain. For example, it can analyze the financial status and supply risks of major business partners and reflect this in the financial information. In this way, by incorporating supply chain information and business partner information, a company's risk profile can be more accurately evaluated.

[0053] The financial results information acquisition unit can use the emotion estimation function to analyze the market's emotional reaction at the time of financial results announcement and evaluate the company's future prospects based on the emotional data. For example, the financial results information acquisition unit can use the emotion estimation function to analyze the market's emotional reaction at the time of financial results announcement in real time and evaluate the company's future prospects based on the data. For example, a company with a large number of positive emotional reactions can be given a high rating. This allows for a more accurate evaluation of the company's future prospects by taking the market's emotional reaction into consideration.

[0054] The company selection department can analyze a company's R&D investment and new product development status to evaluate its potential for technological innovation. For example, the company selection department uses a generative AI to analyze a company's R&D investment and evaluate its potential for technological innovation. For example, it analyzes trends in R&D expenses and the development status of new products to evaluate a company's growth potential. In this way, by analyzing R&D investment and the development status of new products, it is possible to evaluate a company's potential for technological innovation.

[0055] The company selection department can analyze a company's international expansion status and performance in overseas markets to evaluate its global growth potential. For example, the company selection department uses a generation AI to analyze a company's international expansion status and evaluate its global growth potential. For example, it analyzes sales and market share in overseas markets to evaluate a company's growth potential. This makes it possible to evaluate a company's global growth potential by analyzing its international expansion status and performance in overseas markets.

[0056] The company selection unit can use the emotion estimation function to analyze the market's emotional reaction to a company's growth strategy and evaluate its growth potential based on the emotional data. For example, the company selection unit can use the emotion estimation function to analyze the market's emotional reaction to a company's growth strategy in real time and evaluate its growth potential based on the data. For example, companies with a high number of positive emotional reactions are given a high rating. This allows for a more accurate evaluation of a company's growth potential by taking the market's emotional reaction into consideration.

[0057] The profit margin comparison unit can analyze a company's past stock price trends and market volatility to perform risk assessment. For example, the generation AI analyzes a company's past stock price trends and performs risk assessment. For example, it analyzes past stock price fluctuations and market volatility to evaluate a company's risk profile. This makes it possible to evaluate a company's risk profile by analyzing past stock price trends and market volatility.

[0058] The profit margin comparison unit can analyze a company's financial soundness and debt ratio to evaluate financial risk. For example, the generation AI analyzes a company's financial soundness and evaluates financial risk. For example, it analyzes a company's debt ratio and current ratio to evaluate financial risk. This makes it possible to evaluate a company's financial risk by analyzing financial soundness and debt ratio.

[0059] The profit margin comparison unit can use the emotion estimation function to analyze the market's emotional reaction to a company's stock price and adjust the undervalued stock index based on the emotional data. For example, the profit margin comparison unit can use the emotion estimation function to analyze the market's emotional reaction to a company's stock price in real time and adjust the undervalued stock index based on the data. For example, companies with a large number of positive emotional reactions are given a high rating. This allows the undervalued stock index to be adjusted more accurately by taking the market's emotional reaction into consideration.

[0060] The information provision unit can generate detailed reports including future forecasts and scenario analyses for the company. For example, the information provision unit generates detailed reports based on future forecasts for the company using the generation AI. For example, it creates reports including growth forecasts and risk scenarios for the company. In this way, by generating detailed reports including future forecasts and scenario analyses, more specific information can be provided to investors.

[0061] The information provision unit can generate a comparative report that includes a company's competitive analysis and market position. For example, the information provision unit generates a comparative report based on a company's competitive analysis using a generation AI. For example, it creates a report that includes a performance comparison with competitors and market position. In this way, by generating a comparative report that includes a competitive analysis and market position, it is possible to provide investors with information that helps them understand the competitive environment.

[0062] The information providing unit can use the emotion estimation function to analyze the emotional reactions of stock purchasers to company analysis information and adjust the content of the report based on the emotional data. For example, the information providing unit can use the emotion estimation function to analyze the emotional reactions of stock purchasers to company analysis information in real time and adjust the content of the report based on the data. For example, it can emphasize information that has a high number of positive emotional reactions. This allows the content of the report to be more appropriately adjusted by taking into account the emotional reactions of stock purchasers.

[0063] The information provider can provide information including a company's ESG score and sustainability report. For example, the information provider uses the generation AI to provide analytical information based on a company's ESG score. For example, the information provider evaluates a company's environmental measures and social contribution activities and creates a report including an ESG score. This allows the provision of ESG scores and sustainability reports to provide investors with information about a company's sustainability.

[0064] The information provision department can provide technical reports that include information on a company's technological innovation and patents. For example, the information provision department uses a generation AI to provide technical reports based on the company's technological innovation. For example, the information provision department evaluates the state of a company's technological innovation and patent application information and creates a technical report. By providing technical reports that include information on technological innovation and patents, it is possible to provide investors with information on a company's technological growth potential.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The Company Selection Department can analyze corporate social responsibility (CSR) activities and environmental protection activities and take into account a company's social reputation. For example, the Company Selection Department can collect data on a company's CSR reports and environmental protection activities and analyze the company's social reputation. The Company Selection Department can also suggest socially responsible investment destinations to investors based on the company's social reputation. In this way, by taking into account a company's social reputation, more comprehensive investment information can be provided to investors.

[0067] The financial information acquisition unit can analyze data related to a company's employee satisfaction and working environment to evaluate the company's internal environment. For example, the financial information acquisition unit collects reports on employee satisfaction surveys and working environments to analyze the company's internal environment. The financial information acquisition unit can also evaluate the impact of employee satisfaction and working environments on the company's growth. This allows for more accurate corporate growth predictions by taking the company's internal environment into consideration.

[0068] The company selection department can use the emotion estimation function to analyze the market's emotional reaction to a company's leadership and management team, and evaluate the company's business stability based on that emotional data. For example, the company selection department can use the emotion estimation function to analyze the market's emotional reaction to a company's CEO and management team in real time, and evaluate the company's business stability based on that data. This allows for a more accurate evaluation of a company's business stability by taking into account the market's emotional reaction to the management team.

[0069] The Profit Margin Comparison Department can analyze a company's shareholder structure and the trends of major shareholders to evaluate the influence of shareholders. For example, the Profit Margin Comparison Department collects data on a company's shareholder structure and the trading trends of major shareholders to analyze the influence of shareholders. The Profit Margin Comparison Department can also evaluate the impact that the trends of major shareholders have on a company's stock price. This allows for a more accurate evaluation of a company's risk profile by taking into account the influence of shareholders.

[0070] The information providing unit can use the emotion estimation function to analyze consumers' emotional reactions to the company's products and services and adjust the content of the report based on the emotional data. For example, the information providing unit can use the emotion estimation function to analyze consumers' emotional reactions to the company's products and services in real time and adjust the content of the report based on the data. This allows the content of the report to be more appropriately adjusted by taking consumers' emotional reactions into consideration.

[0071] The Company Selection Department can analyze a company's brand value and market reputation to evaluate its brand power. For example, the Company Selection Department can collect a company's brand value evaluation report and market reputation data to analyze its brand power. The Company Selection Department can also evaluate the impact that brand power has on the company's growth. This allows for a more accurate evaluation of a company's growth potential by taking brand power into consideration.

[0072] The information provision department can use the emotion estimation function to analyze the market's emotional reaction to a company's general shareholders' meeting or other important events, and adjust the content of the report based on the emotion data. For example, the information provision department can use the emotion estimation function to analyze the market's emotional reaction to an important event such as a company's general shareholders' meeting or a new product launch in real time, and adjust the content of the report based on the data. This allows the content of the report to be more appropriately adjusted by taking into account the market's emotional reaction to the important event.

[0073] The profitability comparison unit can analyze a company's capital structure and financing strategy to evaluate the soundness of its financial strategy. For example, the profitability comparison unit collects information on a company's capital structure data and financing strategy to analyze the soundness of its financial strategy. The profitability comparison unit can also evaluate the impact of financing strategies on the company's growth. This allows for a more accurate evaluation of a company's risk profile by taking into account the soundness of its financial strategy.

[0074] The company selection unit can use the emotion estimation function to analyze the market's emotional response to a company's advertising campaign or marketing strategy, and evaluate the growth potential based on the emotional data. For example, the company selection unit can use the emotion estimation function to analyze the market's emotional response to a company's advertising campaign or marketing strategy in real time, and evaluate the growth potential based on the data. This allows for a more accurate evaluation of the company's growth potential by taking into account the market's emotional response to the marketing strategy.

[0075] The information department can analyze a company's performance and market share by region and evaluate the growth potential of each region. For example, the information department can collect sales data and market share by region of a company and analyze the growth potential of each region. The information department can also evaluate market trends and the competitive environment of each region. This allows the department to provide investors with more specific investment information by taking into account the growth potential of each region.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The financial information acquisition unit acquires financial information of listed companies. For example, the financial information acquisition unit acquires financial statements and cash flow statements of companies and collects financial information from the companies' official websites. Step 2: The Company Selection Department analyzes the financial information acquired by the Financial Information Acquisition Department and selects companies that are expected to grow. For example, the Company Selection Department evaluates past performance trends and future growth strategies, and analyzes industry trends to identify companies that are expected to grow. Step 3: The profit margin comparison unit compares the average profit margin for the industry of the companies selected by the company selection unit with the current stock price. For example, the profit margin comparison unit retrieves the average profit margin for each industry from the database, compares it with the profit margin of the selected company, retrieves the current stock price, and evaluates the balance with the profit margin. Step 4: The information providing section provides the information obtained by the profitability comparison section to the stock purchaser. For example, the information providing section creates a report including indicators of the company's growth prospects and undervalued stocks, and provides the information to the stock purchaser in cooperation with the securities company's system.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

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

[0099] The 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.

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

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

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

[0103] Fig. 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.

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

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

[0106] In the 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.

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

[0108] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0110] The data processing system 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

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

[0114] The 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.

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

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a financial information acquisition unit that acquires financial information of listed companies; a company selection unit that analyzes the financial information acquired by the financial information acquisition unit and selects companies that are expected to grow; a profit margin comparison unit that compares the average profit margin of the industry of the companies selected by the company selection unit with the current stock price; an information providing unit that provides the information obtained by the profit rate comparison unit to stock purchasers; A system characterized by:

2. The settlement information acquisition unit The company's past press releases and news articles are also analyzed at the same time, taking into account the company's external environment and market reaction.

2. The system of claim 1.

3. The company selection department Analyze the R&D investment and new product development status of the company and evaluate its potential for technological innovation 2. The system of claim 1.

4. The profit rate comparison unit Analyze the company's past stock price trends and market volatility to assess risk.

2. The system of claim 1.

5. The information providing unit Generate detailed reports including future forecasts and scenario analysis for the company 2. The system of claim 1.

6. The settlement information acquisition unit Analyze the market's emotional response at the time of financial results announcement and evaluate the future prospects of the company based on that emotional data.

2. The system of claim 1.

7. The company selection department Analyze the market's emotional response to the company's growth strategy and evaluate its growth potential based on that emotional data.

2. The system of claim 1.

8. The profit rate comparison unit Analyze the market's emotional response to the company's stock price and adjust the undervalued stock index based on that emotional data.

2. The system of claim 1.

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